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    REVIEWISSN 1967-502X


    Mondher Bellalah and Jean-Luc Prigent

    Aims and Scope

    The Euro-Mediterranean Economics and Finance Review is a peer-reviewed research journal ofthe Mediterranean Association of Finance Insurance and Management (AMFAM). It is intended

    to develop research in economics, finance and management with aspecial emphasis on the main

    issues and problems regarding the Euro-Mediterranean zone. The journal is committed to

    excellence by publishing high quality research papers in economics and finance with

    theoretical and empirical contents as well as invited viewpoints (2000 to 4000 words) written

    by well-known experts.

    The journal's editorial policy is to publish original articles that obey the accepted standards

    and to improve communications between academies practitioners and policymakers at both

    national and international levels. While recognizing the Euro-Mediterranean origins of the

    research papers, the journal is also open to research that shows diversity in theoretical andmethodological underpinning.

    Editorial Office

    Regis Dumoulin (Managing Editor, ISC Paris)

    David Heller (Co-managing editor, ISC Paris)

    Sandrine Clais (Editorial Assistant, ISC Paris)

    22, Boulevard du Fort de Vaux

    75017 Paris


    Email: [email protected]

    Phone: +33 1 40 53 99 99 | Fax: +33 1 40 53 98 98

    For any information, please contact Sandrine Clais at [email protected]

    Access this journal electronicallyThe current and past issues of the journal can be found
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    Mondher Bellalah, University of Cergy-Pontoise, France

    Jean-Luc Prigent, University of Cergy-Pontoise, France


    Harry Markowitz, Nobel Prize Laureate, University of California, San Diego, USA

    Edward Prescott, Nobel Prize Laureate, Arizona State University, USA


    Michael Adler

    Columbia University, USARudy Aernoudt

    Brussels Business School, Belgium

    Aman Agarwal

    Indian Institute of Finance, India

    Gordon Alexander


    Mohamed Arouri

    University of Auvergne, France

    Mohamed Ayadi

    HEC Montreal, Canada

    Giovanni Barone-Adesi

    University of Lugano, Switzerland

    Hatem Ben Ameur

    HEC Montreal, Canada

    Jean-Franois Boulier

    CA Asset Management, France

    Michael Brennan


    Eric Briys

    Cyberlibris, Belgium

    Harvey R. CampbellDuke University, USA

    K.C. Chen

    California State University, USA

    Ephraim Clark

    Middlessex University, UK

    Georges Constantinides

    University of Chicago, USA

    Manuel Jos Da Rocha Armada

    University of Minho, Portugal

    Gabriel Desgranges

    University of Cergy-Pontoise, France

    Joao Duque, ISEG Portugal

    Alain Finet

    ULB, BelgiumPhilippe Foulquier

    EDHEC Business School, France

    Bertrand Jacquillat

    IEP, France

    Frank Janseen

    Catholic University of Louvain, Belgium

    Cuong Le Van

    PSE & University of Paris 1, France

    Michel Levasseur

    University of Lille 2, France

    Patrick Navatte

    University of Rennes 1, France

    Andr de Palma

    University of Cergy-Pontoise, France

    Bernard Paranque

    Euromed Management, France

    Kuntara Pukthuanthong

    San Diego University

    Franois Quittard-Pinon

    University of Lyon 1, France

    Richard RollUCLA, USA

    Olivier Scaillet

    HEC, Genve, Switzerland

    Stefan Straetmans

    Maastricht University, Netherlands

    Hracles Vladimirou

    University of Cyprus, Cyprus

    Jose Scheinkman

    Princeton University, USA

    Paul Willmott

    Editor Derivatives, UK

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    Special Issue: Financial crisis in conventional and Islamic Banking

    Guest Editor: Prof. Dr. Omar Masood

    The current global financial crisis has affected both the conventional and the Islamic financial

    system. The lessons learnt from the crisis need to be addressed for the betterment and stability of

    both systems. This special issue will focus on three key areas. First, it will briefly set out the

    various lessons learnt from the crisis. Secondly, it will expound on solutions derived from the

    lessons learnt. Finally, it will explore the means to reshape the behavioural patterns and

    responsibilities of economic agents in the financial system.

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    Number 5


    EDITORIAL.................................................................................................................................................. 3

    1 Role of Accountants and Fair Value accounting leading towards the Global Financial Crisis . 5

    2 An Empirical Analysis of Credit Risk Management in Islamic Banks of Pakistan ................... 21

    3 How do the historical perspective and systemic effects of house price movements help to

    explain the pattern of consumption in the U.K? .................................................................................... 31

    4 Significant role of derivatives in islamic capital market ............................................................... 45

    5 The Stability Estimation and Growth Analysis of Islamic Banks: The case of OIC countries . 62

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    1 Role of Accountants and Fair Value accounting leading

    towards the Global Financial Crisis

    Omar Masood *, Royal Docks Business School, University of East London, London, UnitedKingdom

    Mondher Bellalah, University of Cergy and ISC Paris Business School, Paris, France


    Since the 2007 market turmoil surrounding complex structured credit products, fair

    value accounting and its application through the business cycle has/have been a topic of

    considerable debate. As the illiquidity of certain products became more severe, financial

    institutions turned increasingly to model-based valuations that, despite increased

    disclosure requirements, were nevertheless accompanied by growing opacity in the

    classification of products across the fair value spectrum. In this study, we make an

    attempt to review an analysis regarding implications of the subprime crisis for

    accounting. These implications depend on the interplay among attributes of subprime

    mortgages and other positions, the evolution of market prices and illiquidity during the

    crisis, and the requirements of the applicable accounting standards, while credit losses

    on subprime positions are recorded under various standards. We focus on losses

    recorded based on the fair value measurement guidance provided in FAS 157, Fair

    Value Measurements. First, we overviewed the institutional and market aspects of

    subprime mortgages and other positions, focusing on those with the greatest relevance

    for accounting. Second, we discussed the critical aspects of FAS 157s definition of fair

    value and guidance for fair value measurements. We focused on practical difficulties

    that have arisen in applying that definition and guidance to subprime positions in the

    current illiquid markets. We also raise potential Criticisms of Fair Value Accounting

    during the Credit Crunch.

    KEYWORDS: Subprime crisis; credit crunch; fair value accounting; securitization.

    JEL Classification: M 00, M40, M41, M42


    Fair value accounting is a financial reporting approach in which companies are required or

    permitted to measure and report on an ongoing basis certain assets and liabilities (generally

    financial instruments) at estimates of the prices they would receive if they were to sell the assets

    or would pay if they were to be relieved of the liabilities. Under fair value accounting, companies

    report losses when the fair values of their assets decrease or liabilities increase. Those losses

    reduce companies reported equity and may also reduce companies reported net income. Some

    parties have strong opinion that fair value accounting has a major contribution in strengthen

    credit crises, specially pointing to the obvious difficulties of measuring the fair values of

    subprime positions in the current illiquid markets and the feedback effects noted above. This is

    untenable. The subprime crisis was caused by firms and households making bad operating,

    investing, and financing decisions, managing risks poorly, and in some instances committing

    fraud. The best way to stem the credit crunch and damage caused by these actions is to speed the

    * Omar Masood is at the Royal Docks Business School, University of East London, London, United

    KingdomMondher Bellalah is at the University of Cergy and ISC Paris Business School, Paris, France

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    price adjustment process by providing market participants with the most accurate and complete

    information about subprime positions. While imperfect, fair value accounting provides better

    information about these positions and is a better platform for mandatory and voluntary

    disclosure than alternative measurement attributes, including any form of cost-based accounting.

    This is not to say that guidance for the measurement of fair values in illiquid markets cannot be

    improved. While FAS 157 provides a clearer definition of fair value and considerably expanded

    guidance specifying how fair value should be measured than prior GAAP, the current market

    illiquidity has raised significant challenges for the interpretability of this definition and guidance.

    FAS 157s definition of fair value reflects the idea that there can be orderly transactions based

    on the conditions that exist at the measurement date. During the subprime crisis, this idea has

    become increasingly difficult to sustain even in thought experiments and, more importantly,

    practically useless as a guide to preparers estimation of fair values. FAS 157s fair value

    measurement guidance includes a hierarchy of inputs that favours observable market inputs over

    unobservable firm-supplied inputs, but that ultimately requires preparers to employ the

    assumptions that market participants would use in pricing the asset or liabili ty. This hierarchy

    provides little help to preparers who have to decide whether to base their fair valuations on thepoor quality signals currently being generated by markets versus highly judgmental firm-

    supplied inputs such as forecasts of house price depreciation. For the duration of the crisis,

    preparers will need to exercise considerably more than the usual professional judgment to apply

    FAS 157s language to their specific circumstances.

    As the successive waves of the subprime crisis have hit, firms have repeatedly and sharply

    revised upward their estimates of credit losses. These revisions are inevitable consequences of

    how the subprime crisis evolved, and they do not imply there have been any problems either

    with accounting standards or how preparers have applied them. However, these revisions and

    the high potential for further upward revisions have contributed to the aforementioned feedback

    effects between reported losses and market illiquidity. Needless to say, this market illiquidity isdamaging our real estate and credit markets and overall economy, and it needs to be cured

    through means that do not simply push the problem into the future. As always, essential

    components of such a cure are for firms to provide relevant, reliable, and understandable

    financial report information and for users to conduct careful and dispassionate analysis of that


    The remainder of the essay is structured as follows. In Section II, we overview the short synopsis

    of credit crises. In Section III, we describe the critical aspects of FAS 157s definition of fair value

    and guidance for fair value measurements. We describe the practical difficulties that have arisen

    in applying that definition and guidance to subprime positions in the current illiquid markets.

    We also discuss a potential issue regarding the application of FAS 159, The Fair Value Option forFinancial Assets and Financial Liabilities, during credit crunch. Section IV reveals our findings

    regarding potential Criticisms of Fair Value Accounting during the Credit Crunch Section V

    contain our concluding remarks.


    The International Monetary Fund (2008) estimates that the credit crisis will cost about $945 billion

    dollars, the latest in a long list of estimates presented in Figure 1 below. No one knows the

    ultimate cost of the crisis, but it certainly will exceed the costs of the last major financial crisis

    presented by the collapse of the savings and loan industry. This problem began in the subprime

    mortgage market and then quickly spilled over into other areas of the mortgage industry and the

    capital markets, culminating in a liquidity and credit crisis that is still unfolding. Unsurprisingly,litigation has been on the rise.

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    Figure 1. Estimates of Losses Due to the Subprime and Credit Crises

    3. CRISES

    Just as in the credit crisis, the lawsuits initially started in the mortgage industry. For the most

    part, these were suits against mortgage lenders. The subjects of litigation then moved on to be the

    issuers and underwriters of securities whose cash flows are backed by the principal and interest

    payments of mortgages. Now, the litigation has also engulfed investors who either purchasedthese securities or packaged them into other securities. As the liquidity crisis intensifies, areas

    that are not directly related to the subprime mortgage sector are starting to suffer losses,

    including the commercial paper market, the leveraged buyout industry, and auction-rate

    securities, to name a few examples. As the write-downs continue to accumulate, additional types

    of lawsuits are expected to emerge.

    The value of asset-backed securities (ABS) backed by subprime products has fallen as the

    performance of the subprime loans has continued to worsen. Figure 2 illustrates the value of two

    indices tracking the BBB rated and BBB- rated tranches of home equity deals based on loans from

    the last six months of 2006. An initial investment of $100 (on 19 January 2007) in the BBB index

    would have been worth only $5.46 by 8 May 2008; both indices showed a decline of almost 95%as of 8 May 2008.

    Figure 2. Index Values of Subprime Home Equity ABS Deals from the Second Half of 2006, 19

    January 2007 to 8 May 2008

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    Subprime Mortgage-Related Securities Lawsuits

    Almost every market participant in the securitization processwhich transforms illiquid assets

    such as mortgages, auto loans, and student loans into tradable securitieshas been named as a

    defendant. The list of defendants includes lenders, issuers, underwriters, rating agencies,

    accounting firms, bond insurers, hedge funds, CDOs, and many more.As of 21 April 2008, there

    had been 132 securities lawsuits related to subprime and credit issues, of which 56 were filed

    since January 2008. New York has the most filings, with 48%, while California follows with 14%

    and Florida wraps up the top three with 7%. Filings in other states range between 1% and 5%

    (lawsuits by state are shown in Figure 3 below). This is consistent with recent trends in

    shareholder class actions, where the US circuit courts encompassing New York (Second Circuit),

    California (Ninth Circuit), and Florida (Eleventh Circuit) have seen the most activity in recent


    Figure 3. Partial Count of Subprime-Related Lawsuits by State (through 21 April 2008)

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    The majority of the early lawsuits have been against mortgage lenders. As various other market

    participants reveal the extent of their losses and exposure, they too are being dragged into

    litigation. The plaintiffs include shareholders, investors, issuers and underwriters of securities,

    plan participants, and others. Figure 4 gives a breakdown of securities defendants and plaintiffs.

    Figure 4. The Players: Plaintiffs and Defendants (through 21 April 2008)

    Scope of Fair Value Accounting

    As depicted in Figure 6, the valuation attributes required by the accounting standards governing

    the accounting for subprime positions can be subdivided into the following broad categories.

    Some of these standards require or allow subprime positions to be fair valued on the balance

    sheet (e.g., FAS 115 for trading and AFS securities, FAS 133 for derivatives, FIN 45 for guarantees

    at inception, and FAS 159 for positions for which the fair value option is chosen). When fair value

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    is the valuation attribute, unrealized gains on the positions may be recorded either on the income

    statement (e.g., FAS 115 for trading securities, FAS 133 for non hedge and fair value hedge

    derivatives, and FAS 159 for financial instruments for which the fair value option is elected) or in

    other comprehensive income (FAS 115 for AFS securities and FAS 133 for cash flow hedge


    Other of these standards requires subprime positions to be recorded at amortized cost (possibly

    zero) on the balance sheet. Assets accounted for at amortized cost generally are subject to

    impairment write-downs if criteria specified in the standards are met. Assets deemed impaired

    based on the relevant criteria are required to be written down to fair value under some standards

    (e.g., FAS 115 for HTM securities and SOP 01-6 for held-for-sale loans) and to other valuation

    attributes that generally are higher than fair value under other standards (e.g., FAS 5 and FAS 114

    for held-for-investment loans). Similarly, under FAS 115 unrealized gains and losses on AFS

    securities that previously were recorded in other comprehensive income are recorded in income

    when the AFS are deemed impaired.

    Critical Aspects of the Definition of Fair Value

    FAS 157 defines fair value as the price that would be received to sell an asset or paid to transfer

    a liability in an orderly transaction between market participants at the measurement date. In this

    section, we unpack and discuss the constituent elements of this definition, indicating the practical

    difficulties involved in applying each element and the slippage among the elements given the

    current market illiquidity for subprime positions. The definition reflects an optimal exit value

    notion of fair value, that is, the highest values of assets and the lowest values of liabilities

    currently held by the firm. This notion corresponds to firms solvency more than do the possible

    alternative fair value notions of entry value (the price that would be paid to buy an asset or

    received from issuing a liability) or value in use (the entity-specific value to the current holder

    of an item). In particular, if all assets and liabilities on a firms balance sheet were perfectly

    measured at exit value, then owners equity would equal the cash expected to remain if the firm

    liquidated all of those items in orderly transactions between market participants at the

    measurement date, that is, not in fire sales. Given the paramount importance of maintaining

    solvency during the subprime crisis, this element of the definition of fair value is well suited to

    users of financial reports current informational needs.

    At the measurement date means that fair value should reflect the conditions that exist at the

    balance sheet date. If markets are illiquid and credit spreads are at historically high levels, as is

    now the case, then the fair values should reflect those conditions. In particular, firms should not

    incorporate their expectations of market liquidity and credit spreads returning to normal over

    some horizon, regardless of what historical experience, statistical models, or expert opinion

    indicates. While one can question this element of the fair value definition, it has considerable

    precedent in the accounting literaturenotably FAS 107, Disclosures about Fair Value of

    Financial Instruments, and SEC enforcement actions20 and it is hard to imagine the FASB

    proposing a definition of fair value without it.

    An orderly transaction is one that is unforced and unhurried. The firm is expected to conduct

    usual and customary marketing activities to identify potential purchasers of assets and assumers

    of liabilities, and these parties are expected to conduct usual and customary due diligence. Each

    of these activities could take months in the current environment, because of the few and noisy

    signals about the values of subprime positions currently being generated by market transactions

    and because of parties natural skepticism regarding those values. Hence, the earliest such an

    orderly transaction might occur could easily be a quarter or more after the balance sheet date. At

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    that time, market conditions almost certainly will differ from those that exist at the balance sheet

    date, for better or, as been the case lately, worse.

    The Fair Value Hierarchy

    FAS 157 creates a hierarchy of inputs into fair value measurements, from most to least reliable.

    Level 1 input is unadjusted quoted market prices in active markets for identical items. Whilesome accounting academics, bank regulators, and others worry that market values might be

    incorrect or their use in accounting might have undesirable incentive or feedback effects, in our

    opinion pure mark-to-market measurements using such maximally reliable inputs are the rough

    equivalent of accounting nirvana. Even in times of normal market liquidity, this nirvana does not

    exist for most subprime positions, however, and so we can safely ignore such philosophical

    disputes in this essay. Level 2 inputs are other directly or indirectly observable market data.

    There are two broad subclasses of these inputs. The first and generally preferable subclass is

    quoted market prices in active markets for similar items or in inactive markets for identical items.

    These inputs yield adjusted mark-to-market measurements that are less than ideal but usually

    still pretty good, depending on the nature and magnitude of the required adjustments. The

    second subclass is other observable inputs such as yield curves, exchange rates, empirical

    correlations, et cetera. These inputs yield mark-to-model measurements that are disciplined by

    market information but that can only be as good as the models employed. In our view, this

    second subclass usually has less in common with the first subclass than with better quality level 3

    measurements described below.

    In times of normal market liquidity, many subprime positions would be fair valued using level 2

    measurements. For example, while most subprime MBS trade over-the-counter and rarely, in

    normal markets dealers generally do their best to provide bid and ask prices for these securities.

    There are also price and yield indices for portfolios of subprime positions available from Market

    and other sources. The price transparency offered by these sources has substantially evaporated

    during the subprime crisis, however. Dealers are reluctant to provide bid and ask quotes for

    subprime positions, and when they do the bid-ask spread is very wide. Very few truly orderly

    transactions are occurring, and those that do occur typically are privately negotiated principal-to-

    principal transactions for which the terms and positions involved are largely opaque to market

    participants. Market has announced that there will be no indices for the first half of 2008 vintage,

    due to an insufficient number of securitizations.

    Level 3 inputs are unobservable, firm-supplied estimates. While these inputs should reflect the

    assumptions that market participants would use, they yield mark-to-model valuations that are

    largely undisciplined by market information. Due to the declining price transparency described

    above, many subprime positions that previously were fair valued using level 2 inputs must now

    be fair valued using level 3 inputs. While many firms have been criticized in the popular press for

    this migration of fair value measurements down the hierarchy, this migration is an inevitable

    result of the deterioration of price transparency in the subprime crisis.

    Level 3 inputs usually are based on historical data in some fashion. Historical data is only useful

    for fair valuation purposes to the extent that the future is expected to be similar, or at least

    capable of being related, to the past. For subprime positions, a critical level 3 input is house price

    depreciation. Most of the historical data to date (and a fortiori up to earlier points in the subprime

    crisis) reflect a period in which house price appreciation was robust and so defaults were few,

    uncorrelated, and yielded small percentage losses given default. Hence, this historical data is of

    little use for the purposes of determining this input and thus the fair values of subprime

    positions. Instead, firms must forecast future house price depreciation, as well as other primitive

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    variables such as future interest rates and the time when subprime mortgagors will be able to

    refinance again. These variables are critical determinants of the future number and correlation of

    defaults and the percentage magnitude of losses given default.


    Subprime positions are subject to the disclosure requirements of the governing accounting

    standards (e.g., FAS 115 for securities) that we do not mention here.22 Instead, we discuss threeoverarching disclosure requirements of particular relevance to subprime positions during the

    subprime crisis.

    First, FAS 157 requires disclosures of fair value measurements by level of the hierarchy. The

    required disclosures are considerably more detailed for level 3 fair value measurements than for

    level 1 or 2 measurements. In particular, for level 3 measurements firms most provide

    quantitative reconciliations of beginning and end-of period fair values, distinguishing total

    (realized and unrealized) gains and losses from net purchases, sales, issuances, settlements, and

    transfers. The line-item location of gains and losses on the income statement must be indicated.

    Qualitative descriptions of measurement inputs and valuation techniques must be provided.

    These disclosure requirements make the effects of level 3 measurements on the financialstatements considerably more transparent than they would have been under prior GAAP, and

    users of financial reports are fortunate to have them available during the subprime crisis.

    Second, SOP 94-6, Disclosure of Certain Significant Risks and Uncertainties, requires disclosures

    regarding an uncertain estimate such as a fair value when it is reasonably possible the estimate

    will change in the near term (one year or less) and the effect of the change would be material to

    the financial statements. The disclosure should indicate the nature of the uncertainty. Disclosures

    of the factors that cause the estimate to be sensitive to change are encouraged but not required.

    Neither FAS 157 nor SOP 94-6 requires quantitative disclosures of the forecasted values of the

    primitive variables that underlie level 3 fair valuations or of the sensitivities of the fair valuations

    to movements in those primitive variables. In the absence of such quantitative disclosures, duringthe subprime crisis I have found level 3 fair values to be very difficult to interpret for a given firm

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    and to compare across firms. To enhance the interpretability of level 3 fair values, I/we suggest

    the FASB consider requiring disclosures of firms forecasts of primitive variables when those

    forecasts have material effects on their level 3 fair valuations.

    Third, SAS 1 requires disclosures of type 2 subsequent events, i.e., events that occur between the

    balance sheet date and the financial report filing date, if these events render the financial

    statements misleading as of the filing date. Very significant type 2 subsequent events occurred for

    many firms holding large subprime positions in the third and fourth quarters of 2007.

    Specifically, the third and fourth waves of the subprime crisis described above hit after the end of

    the third and fourth fiscal quarters of many firms, respectively, but before the filing dates for

    those quarters. Citigroups previously discussed third quarter 2007 subsequent events disclosure

    is a good example.

    Fair Value Option

    FAS 159 allows firms to elect to fair value individual financial instruments upon the adoption of

    the standard or at the inception of the instruments. One type of exercise of the fair value option

    with particular salience in the subprime crisis is the decision by many securities firms to fairvalue the liabilities of their consolidated securitization entities. Securities firms have made this

    choice primarily because they are required by industry or other GAAP to record the entities

    assets at fair value, and so electing the fair value option for the entities liabilities yields

    symmetric accounting. In general, such symmetry is a desirable thing, as offsetting gains and

    losses on these economically matched positions are recorded in the same period.

    A concern, however, is that these firms may have the incentive to provide moral recourse to the

    securitization entities. When this is the case, the firms may bear the losses on the entities assets

    without benefiting from offsetting gains on the entities liabilities. At a minimum, the fair values

    of the entities liabilities would have to be adjusted for any expected provision of moral recourse,

    a problematic valuation exercise given the non contractual nature of moral recourse.Potential Criticisms of Fair Value Accounting

    During the Credit Crunch

    Unrealized Gains and Losses Reverse

    There are two distinct reasons why unrealized gains and losses may reverse with greater than

    50% probability. First, the market prices of positions may be bubble prices that deviate from

    fundamental values. Second, these market prices may not correspond to the future cash flows

    most likely to be received or paid because the distribution of future cash flows is skewed. For

    example, the distribution of future cash flows on an asset may include some very low probability

    but very high loss severity future outcomes that reduce the fair value of the asset.

    Bubble Prices

    The financial economics literature now contains considerable theory and empirical evidence that

    markets sometimes exhibit bubble prices that either are inflated by market optimism and

    excess liquidity or are depressed by market pessimism and illiquidity compared to fundamental

    values. Bubble prices can result from rational short horizon decisions by investors in dynamically

    efficient markets, not just from investor irrationality or market imperfections. Whether bubble

    prices have existed for specific types of positions during the credit crunch is debatable, but it

    certainly is possible.

    In FAS 157s hierarchy of fair value measurement inputs, market prices for the same or similarpositions are the preferred type of input. If the market prices of positions currently are depressed

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    below their fundamental values as a result of the credit crunch, then firms unrealized losses on

    positions would be expected to reverse in part or whole in future periods. Concerned with this

    possibility, some parties have argued that it would be preferable to allow or even require firms to

    report amortized costs or level 3 mark-to model fair values for positions rather than level 2

    adjusted mark-to-market fair values that yield larger unrealized losses. If level 1 inputs are

    available, then with a few narrow exceptions FAS 157 requires firms to measure fair values atthese active market prices for identical positions without any adjustments for bubble pricing.

    However, if only level 2 inputs are available and firms can demonstrate that these inputs reflect

    forced sales, then FAS 157 (implicitly) allows firms to make the argument that level 3 mark-to-

    models based fair values are more faithful to FAS 157s fair value definition.

    If we agree with the FASBs decision in FAS 157 that the possible existence of bubble price s in

    liquid markets should not affect the measurement of fair value. It is very difficult to know when

    bubble prices exist and, if so, when the bubbles will burst. Different firms would undoubtedly

    have very different views about these matters, and they likely would act in inconsistent and

    perhaps discretionary fashions. To be useful, accounting standards must impose a reasonably

    high degree of consistency in application. It should also be noted that amortized costs reflect anybubble prices that existed when positions were incepted. In this regard, the amortized costs of

    subprime-mortgage related positions incepted during the euphoria preceding the subprime crisis

    are far more likely to reflect bubble prices than are the current fair values of those positions.

    Future Cash Flows

    Fair values should reflect the expected future cash flows based on current information as well as

    current risk-adjusted discount rates for positions. When a position is more likely to experience

    very unfavourable future cash flows than very favourable future cash flows, or vice-versa

    statistically speaking, when it exhibits a skewed distribution of future cash flowsthen the

    expected future cash flows differ from the most likely future cash flows. This implies that over

    time the fair value of the position will be revised in the direction of the most likely future cash

    flows with greater than 50% probability, possibly considerably greater. While some parties

    appear to equate this phenomenon with expected reversals of unrealized gains and losses such as

    result from bubble prices, it is not the same thing. When distributions of future cash flows are

    skewed, fair values will tend to be revised by relatively small amounts when they are revised in

    the direction of the most likely future cash flows but by relatively large amounts when they are

    revised in the opposite direction. Taking into account the sizes and probabilities of the possible

    future cash flows, the unexpected change in fair value will be zero on average.

    Financial instruments that are options or that contain embedded options exhibit skewed

    distributions of future cash flows. Many financial instruments have embedded options, and in

    many cases the credit crunch has accentuated the importance of these embedded options. Super

    senior CDOs, which have experienced large unrealized losses during the credit crunch, are a

    good example. At inception, super senior CDOs are structured to be near credit riskless

    instruments that return their par value with accrued interest in almost all circumstances. Super

    senior CDOs essentially are riskless debt instruments with embedded written put options on

    some underlying set of assets. Super senior CDOs return their par value with accrued interest as

    long as the underlying assets perform above some relatively low threshold (reflecting the riskless

    debt instruments),but they pay increasingly less than this amount the more the underlying assets

    perform below that threshold (reflecting the embedded written put options). As a result of the

    embedded written put options, the fair values of super senior CDOs typically are slightly less

    than the values implied by the most likely cash flows. During the credit crunch, the underlyingassets (often subprime mortgage-backed securities) performed very poorly, increasing the

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    importance of the embedded put option and decreasing the fair value of super senior CDOs

    further below the value implied by the most likely outcome, which for some super seniors may

    still be to return the par value with accrued interest. To illustrate this subtle statistical point,

    assume that the cash flows for a super senior CDO are driven by home price depreciation, and

    that the distribution of percentage losses is modestly skewed with relatively small probability of

    large losses, as indicated in the following table.

    Estimated loss on

    Home price depreciation Probability occurs (Value of) super senior CDO

    as a percentage of par value

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    Together, the orderly transaction and at the measurement date elements of FAS 157s fair

    value definition reflect the semantics behind the fair in fair value. Fair values are not

    necessarily the currently realizable values of positions; they are hypothetical values that reflect

    fair transaction prices even if current conditions do not support such transactions. When markets

    are severely illiquid, as they have been during the credit crunch, this notion yields significant

    practical difficulties for preparers of firms financial statements. Preparers must imaginehypothetical orderly exit transactions even though actual orderly transactions might not occur

    until quite distant future dates. Preparers will often want to solicit actual market participants for

    bids to help determine the fair values of positions, but they cannot do so when the time required

    exceeds that between the balance sheet and financial report filing dates. Moreover, any bids that

    market participants might provide would reflect market conditions at the expected transaction

    date, not the balance sheet date.

    When level 2 inputs are driven by forced sales in illiquid markets, FAS 157 (implicitly) allows

    firms to use level 3 model-based fair values. For firms to be able to do this, however, their

    auditors and the SEC generally require them to provide convincing evidence that market prices

    or other market information are driven by forced sales in illiquid markets. It may be difficult forfirms to do this, and if they cannot firms can expect to be required to use level 2 fair values that

    likely will yield larger unrealized losses. In our view, the FASB can and should provide

    additional guidance to help firms, their auditors, and the SEC individually understand and

    collectively agree what constitutes convincing evidence that level 2 inputs are driven by forced

    sales in illiquid markets. The FASB could do this by developing indicators of market illiquidity,

    including sufficiently large bid-ask spreads or sufficiently low trading volumes or depths.

    These variables could be measured either in absolute terms or relative to normal levels for the

    markets involved. When firms are able to show that such indicators are present, the FASB should

    explicitly allow firms to report level 3 model-based fair values rather than level 2 valuations as

    long as they can support their level 3 model-based fair values as appropriate in theory and withadequate statistical evidence. Requiring firms to compile indicators of market illiquidity and to

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    provide support for level 3 mark-to-model valuations provides important discipline on the

    accounting process and cannot be avoided. Relatedly, we also believes that the FASB should

    require firms to disclose their significant level 3 inputs and the sensitivities of the fair values to

    these inputs for all of their material level 3 model-based fair values. If such disclosures were

    required, then level 3 model-based fair values likely would be informationally richer than poor

    quality level 2 fair values.


    By recognizing unrealized gains and losses, fair value accounting moves the recognition of

    income and loss forward in time compared to amortized cost accounting. In addition, as

    discussed in Section IV.A.1 unrealized gains and losses may be overstated and thus subsequently

    reverse if bubble prices exist. If firms make economically suboptimal decisions or investors

    overreact because of reported unrealized gains and losses, then fair value accounting may yield

    adverse feedback effects that would not occur if amortized cost accounting were used instead.

    For example, some parties have argued that financial institutions write-downs of subprime and

    other assets have caused further reductions of the market values of those assets and possibly

    even systemic risk.

    These parties argue that financial institutions reporting unrealized losses has caused them to sell

    the affected assets to raise capital, to remove the taint from their balance sheets, or to comply

    with internal or regulatory investment policies. These parties also argue that financial

    institutions issuance of equity securities to raise capital have crowded out direct investment in

    the affected assets. It is possible that fair value accounting-related feedback effects have

    contributed slightly to market illiquidity, although he is unaware of any convincing empirical

    evidence that this has been the case. However, it is absolutely clear that the subprime crisis that

    gave rise to the credit crunch was primarily caused by firms, investors, and households making

    bad operating, investing, and financing decisions, managing risks poorly, and in some instances

    committing fraud, not by accounting. The severity and persistence of market illiquidity during

    the credit crunch and any observed adverse feedback effects are much more plausibly explained

    by financial institutions considerable risk overhang10 of subprime and other positions and their

    need to raise economic capital, as well as by the continuing high uncertainty and information

    asymmetry regarding those positions. Financial institutions actually selling affected assets and

    issuing capital almost certainly has mitigated the overall severity of the credit crunch by allowing

    these institutions to continue to make loans. Because of its timeliness and informational richness,

    fair value accounting and associated mandatory and voluntary disclosures should reduce

    uncertainty and information asymmetry faster over time than amortized cost accounting would,

    thereby mitigating the duration of the credit crunch.

    Moreover, even amortized cost accounting is subject to impairment write-downs of assets under

    various accounting standards and accrual of loss contingencies under FAS 5. Hence, any

    accounting-related feedback effects likely would have been similar in the absence of FAS 157 and

    other fair value accounting standards.


    Financial history contains many examples of the cycle characteristic of the subprime market

    discovery of profitability, expansion of credit activity, weakening of credit standards as

    competitive pressures to maintain volumes increase, followed by subsequent collapse. The

    subprime cycle is unique mainly in the lack of clarity regarding the distribution of mortgage

    default risks, especially in the failure to recognize that even the mortgage trusts might sufferenough write offs that their own securities could be wholly or partially defaulted. The principal

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    lesson from each of these cycles is that risk control needs to be tougher during the upswing of the

    cycle, just when everyone believes it to be unnecessary. If the industry cannot control risks on its

    own regardless of how confusing the allocation of the risks might be then regulators must

    ensure they do so. Sadly, in the many cycles where the foregoing effects have been observed,

    regulatory corrective action is almost always too little and too late to offset some painful losses.

    Like all of the severe crises that have periodicallybe/been(?) set our remarkably flexible economy,

    the subprime crisis is not and could not be the fault of any one set of parties. The entire economic

    system failed to appreciate the risks of the rapid growth in risk-layered subprime mortgages, the

    inevitable end of house price appreciation, and unprecedented global market liquidity. These

    factors combined to enable all-too-human undisciplined behaviours in lenders, borrowers, and

    investors, all of whom were unquestioningly optimistic for as long as the sun shined upon home

    equity. Economic policy, bank regulation, corporate governance, financial reporting, common

    sense, fear of debt and bankruptcy, and all of our other protective mechanisms were insufficient

    to curb these behaviours.

    This passage also captures how divorced the process was from the economic and statistical

    concepts, such as fair value, that underlie accounting.

    Accounting, fair value or otherwise, will never eliminate such behaviours. It can only play two

    roles. It can provide periodic financial reports that inform relatively rational and knowledgeable

    market participants on an ongoing basis, thereby mitigating the adverse effects of these

    behaviours. It can provide a common information set upon which market participants can

    recalibrate their valuations and risk assessments when the economic cycle turns. In our view, fair

    value accounting plays an essential part in both of these roles, but especially in allowing such

    recalibrations to occur as quickly and efficiently as possible, as it is now doing in the subprime

    crisis. By comparison, any form of historical cost accounting would drag out these recalibrations

    over considerably longer period, likely worsening the ultimate economic cost of the crisis.

    This is not to say that fair value accounting and other aspects of GAAP have worked perfectly

    during the subprime crisis. The crisis has made clear that financial statement preparers need

    additional guidance regarding how to calculate fair values in illiquid markets. Users of financial

    reports need better disclosures about the critical estimates underlying level 3 fair values and how

    sensitive fair values are to those estimates. Accounting standard setters need to consider what

    guidance and disclosures to require. Preparers need to provide these disclosures in an

    informative fashion, and users must analyze them carefully and dispassionately. Accounting

    researchers and teachers can contribute to all of these processes. Indeed, for all of us who care

    about accounting and its role in our economy, there is much work to be done.


    American Institute of Certified Public Accountants. 1994. SOP 94-6, Disclosure of Certain

    Significant Risks and Uncertainties, New York, NY.

    Bloomfield, R., M. Nelson, and S. Smith. 2006. Feedback Loops, Fair Value Accounting, and

    Correlated Investments. Review of Accounting Studies 11(2/3): 377-416,

    Stephen R. Stubben, 2008, Fair Value Accounting for Liabilities and Own Credit Risk, The

    Accounting Review, Vol.83, No.3.

    Basel Committee on Banking Supervision, 2006, Results of the Fifth Quantitative Impact Study

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    2006, Sound Credit Risk Assessment and Valuation for Loans (Basel: Bank for

    International Settlements).

    Borio, Claudio, and Kostas Tsatsaronis, 2005, Accounting, Prudential Regulations and

    Financial Stability: Elements of a Synthesis, BIS Working Papers No. 180 (Basel: Bank

    for International Settlements).

    Bruche, Max and Carlos Gonzlez-Aguado, 2008, Recovery Rates, Default Probabilities, and the

    Credit Cycle, CEMFI Working Paper, (Madrid:Centro de Estudios Monetarios Financieros).

    Calza, Alessandro, Tommaso Monacelli and Livio Stracca, 2006, Mortgage Markets,

    Anderson, R. C., Mansi, S. A., & Reeb, D. M. (2004). Board characteristics, accounting report

    Integrity and the cost of debt. Journal of Accounting & Economics, 37, 315342

    Bantel, K. A., & Jackson., S. E. (1989). Top management and innovations in banking: Does theComposition of the top team make a difference? Strategic Management Journal, 10, 107124

    Bank of England. 2008. Financial Stability Report. Issue No. 23. April.

    Barlevy, G. 2007. Economic Theory and Asset Bubbles. Economic Perspectives, Third Quarter, 44-


    Bies, S. 2008. Fair Value Accounting. Speech to the International Association of Credit

    Portfolio Managers General Meeting, New York, New York, November 18.

    CFA Institute. 2005. A Comprehensive Business Reporting Model: Financial Reporting

    for Investors. Center for Financial Market Integrity.

    Financial Accounting Standards Board (FASB). 1975. Accounting for Contingencies.

    Statement of Financial Accounting Standards No. 5. Norwalk, CT: FASB.

    1982. Accounting for Certain Mortgage Banking Activities. Statement of

    Financial Accounting Standards No. 65. Norwalk, CT: FASB.

    1991. Disclosures about Fair Value of Financial Instruments. Statement of Financial Accounting

    Standards No. 107. Norwalk, CT: FASB.

    1993. Accounting for Certain Investments in Debt and Equity Securities.

    Statement of Financial Accounting Standards No. 115. Norwalk, CT: FASB.1998. Accounting for

    Derivative Instruments and Hedging Activities. Statement of

    Financial Accounting Standards No. 133. Norwalk, CT: FASB.

    2000. Accounting for Transfers and Servicing of Financial Assets and

    Extinguishments of Liabilities. Statement of Financial Accounting Standards No. 140.

    Norwalk, CT: FASB.

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    International Monetary Fund. April 2008. Containing Systemic Risks and Restoring Financial


    Johnson, S. 2008a. The fair-value blame game. March 19.

    Understanding Accounting related Allegation, By Dr. Faten Sabry, Anmol Sinha, and Sungi Lee,

    July 3, 2008

    Accounting in and for the Subprime Crisis, by Stephen G. Ryan, March 2008, Stern School of

    Business, New York University

    Fair value Accounting, understanding the issue, raised by credit crunch, by Stephen G. Ryan, July


    The Subprime Crisis -- Cause, Effect and Consequences, by R. Christopher

    Value Relevance of FAS 157 Fair Value Hierarchy Information and the Impact of

    Corporate Governance Mechanisms by: Chang Joon Song, Wayne Thomas, Han Yi: June 2004

    Fair Value Accounting and Gains from Asset Securitizations: A Convenient Earnings

    Management Tool with Compensation Side-Benefits: By Patricia M,Dechow,Linda

    Pennington-Cross, A. Subprime and Prime Mortgages: Loss Distributions, working paper 03-1,

    Office of Federal Housing Enterprise Oversight, Washington D.C.

    The Presidents Working Group on Financial Markets. 2008. Policy Statement on Financial

    Market Developments,

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    2 An Empirical Analysis of Credit Risk Management in

    Islamic Banks of Pakistan

    Asma Abdul Rehman*, Cardiff Metropolitan University


    The purpose of this study is to investigate the banks factors which have significantly

    influence the credit risk of Islamic banks operating in Pakistan. Secondary data is

    obtained from annual reports of the Islamic banks from 2007 to 2011. Data is analyzed

    by using descriptive statistics, correlation matrix and multiple regression analysis.

    Findings reveal that total debt equity ratio and capital adequacy ratio have positive and

    significant relationship with credit risk whereas asset utilization has a negative and

    significant relationship with credit risk. Considering the importance of credit Risk

    management in Islamic banks, this research has determined the credit risk management

    in Pakistani Islamic banks. This study would be helpful as a base study for future

    conceptual model.

    Keywords: Credit risk, Non-performing loan, capital adequacy, debt to equity ratio,

    asset utilization ratio, Islamic banks, Pakistan

    G21 : Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages


    Islamic banking industry has been significantly growing over the last three decades and now has

    total assets of around $1.2 trillion with annual growth rate over 20 percent.

    Credit risk is considered the major risk in banking industry. Credit risk management is an

    integral part of banks loan process. In developing countries, the development process depends a

    lot on financial intermediaries. Empirical researches have shown that a good financial sector

    contributes to the development of economy. Financial institutions should have a credit risk

    management system in place to identify measure, monitor and control credit risk which in turn

    prevents distress or collapse of the financial institutions. The concepts of a sound risk

    management system in financial institutions and regulations provide a mechanism to strengthen

    and improve the supervision and risk management system. A successful system for risk

    management needs a positive risk culture.

    The financial crisis of 2007 have provided a golden opportunity to Islamic banks for the

    expansion in the other parts of the world because Islamic banks are considered as much safer as

    they do not include risky products offerings (Lahem 2009, Cihak and Hesse 2008). Islamic banks

    have managed to survive during financial crisis due to uniqueness of Islamic banking products

    (Zeitun, 2012).

    Today, there is unstable circumstances in Pakistan that has put banks both Islamic banks and

    conventional banks to face numerous barrier to grow. Islamic banks are new to the industry that

    is the reason they are more obvious to the unstable conditions. There is not a specific study that

    has been conduct specifically on credit Risk management in Islamic banks of Pakistan. So, this

    study will add value to literature and will be useful for Islamic Banks, practitioner as well as for

    academic point of view.

    *Asma Abdul Rehman is at Cardiff Metropolitan University, Email: [email protected]

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    The aim of this paper is to investigate empirically the internal factors that have an impact on the

    capital risk ratio in Islamic banks operating in Pakistan.

    The overall objectives of the current study are as follows:

    1. To investigate the impact of total debt equity ratio on credit risk ration of Islamic banks

    operating in Pakistan.

    2. To examine the effect of NPL ratio on credit risk ratio of Islamic banks of Pakistan.

    3. To determine the effect of capital adequacy ratio on credit risk of Islamic banks.

    4. To investigate the effect of banks size on credit risk of Islamic banks.

    5. To examine the impact of asset utilization ratio on credit risk of Islamic banks operating

    in Pakistan.

    The remaining paper has been organized as follows: Section 2 discusses the Islamic banking

    system in Pakistan briefly, section 3 through light on previous studies related to Islamic bankingand credit Risk management, section 4 explains the Methodology in detail, Section 5 is related to

    the Data Analysis and finally section 6 presents the conclusion of the study.


    Islamic banking is growing significantly in Pakistan as it constitute of over 10% of banking

    system in Pakistan with 903 billion rupees of assets and with 1115 branches of Islamic banks

    operating all over Pakistan (SBP, 2013). Islamic banking is having profits of 4.3 billion rupees as

    the end of June 2013. Islamic bankings share of assets in banking industry is reached to 9%. It is

    estimated that with this growing trend Islamic banking industry will reach double of its market

    share by 2020. There are 19 Islamic banks working in Pakistan out of these banks five banks are

    full-fledge Islamic banks in Pakistan such as Meezan bank, Bank Islami, Dubai Islamic bank, Burj

    bank and Al-Baraka bank and remaining are Islamic windows of conventional banks working in


    Following table shows the statistics of Islamic banking industry of Pakistan:

    Table 1: Islamic banking industry

    Industry progress (billion RS.) Share in industry

    June 2012 March 2013 June 2013 June


    March 2013 June


    Total Assets 711 847 903 8.2% 8.7% 9.0%

    Deposits 603 704 771 8.9% 9.7% 9.9%

    Net Financing &


    543 666 700 7.9% 8.9% 8.8%

    NPL 18.3 19.5 19.4 - - -

    Branches 886 1100 1115 - - -

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    Credit risk is the most prominent risk in banking industry. According to Drzik et al. (1998), credit

    risk is comprised of 60% of total risks in commercial banks. Credit risk refers to defaulting of

    counterparty on debt payment or meeting contractual obligation. Fraser et al. (2001) pointed out

    that credit risk is considered the major reason of banks failure in recent years, and it is most

    evident risk that is faced by banks.

    Khan and Ahmad (2001) states that the nature of the risk goes to change due to change in the

    composition of its assets & liabilities as well as profit and loss sharing ratio. Their analysis

    highlights that credit risk depends upon profit and loss sharing model of financing. The most

    important risk that seriously affects the banks viability is credit risk. In order to maintain

    sustainable growth of Islamic banking is to identify the key factors which influence Islamic

    banking credit risk. They investigate that according to bankers point of view there is lack of

    understanding the risk which is involved in the Islamic banks.

    Brewer (1994) has studied the impact of loan activities on bank risk. He have used ratio of loan to

    asset for banks risks because loans are illiquid and considered as higher default risk than anyother banking asset. Findings of the study reveal that there is a positive relationship between loan

    to asset ratio and banks risk measure. Whereas Altunbas (2005) is of the view point that credit

    risk management strategies suggest that there is negative relationship between loan to asset ratio

    and bank risk.

    Bashir (1999) examines the effects of scale (total assets) on the performance of Islamic banks. And

    there findings revealed that there is negative and statistically significant relationship between

    size of banks and the risk index indicates that large size is economically efficient.

    Hayati et al. (2002) conduct a study on factors influencing credit risk in Islamic banking. This

    study emphasis on that, operating side by side with conventional banks, Islamic banks are

    equally vulnerable to risks. The future of Islamic financial institutions will depend to a largeextent on how well they manage risks. This ability could be enhanced if the factors affecting these

    risks are systematically identified.

    Ahmad and Arif (2007) investigate the key determinants of credit risk of banks. The study

    compares the emerging economys credit risk with developed economies. Eight key factors are

    taken as potential risk determinants in the two test models to find out which are the factors that

    have major contribution in the credit risk. The study finds that regulatory capital, loan loss

    provision, loan to deposit ratio are significantly related to the credit risk. In other words, these

    are the key determinants of credit risk. Whereas, leverage is not a significant determinant of

    credit risk. The study concludes that emerging economys banks are facing more credit risk as

    compared to developed economys banks.

    Martin and Hesse (2008) analyze whether small Islamic banks tend to be more financially strong

    as compared to large Islamic banks, which may affect credit risk management issues of large

    Islamic banks. The study uses z-score as a dependent variable to measure the banks risk

    individually. The study conducts using data of 20 banks. The study finds that small Islamic banks

    are stronger as compared to small commercial banks. Stability of small Islamic banks is high as

    compared to large Islamic banks. Islamic banks are more stable when operating on small scale

    and facing low risk than large Islamic banks.

    Farida Najuna (2011) examines the relationship between bank specific variables and credit risk

    and analyzes the financing structure. The study conducted using the data of Malaysian Islamic

    banks. Five bank specific variables are used including financing expansion, financing quality,

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    capital buffer, bank size and capital ratio. Three dummy variables which are regressed against

    credit risk. The findings show that four bank specific variables: capital ratio, capital buffer,

    financing expansion and financing quality have significant relationship with credit risk.

    Financing structure also has a significant influence on the level of credit risk.

    Ahmed et al. (2011) conduct a comprehensive study to determine the firms level factors which

    have significantly influence the risk management practices of Islamic banks in Pakistan. For this

    purpose, the current study selects credit, operational and liquidity risks as dependent variables

    while size, leverage, NPLs ratio, capital adequacy and asset management are used as explanatory

    variable for the period of four years from 2006 to 2009. The results indicate that size of Islamic

    banks have a positive and statistically significant relationship with financial risks (credit and

    liquidity risk), whereas its relation with operational risk is found to be negative and insignificant.

    The asset management establishes a positive and significant relationship with liquidity and

    operational risk.

    Nawaz et al (2012) have conducted research on credit risk and performance of Nigerian banks by

    using secondary data from 2004 to 2008. Results showed that no-performing loan has a negative

    relationship with return on assets.

    Ogboi and Unuafe (2013) have studied impact of credit risk management of performance of

    Nigerian commercial banks. They have used panel data for the years 2004 to 2009. They have

    used return on asset as a proxy for credit risk in their study. Findings illustrate that NPL has a

    negative relationship with credit risk whereas Capital adequacy ratio has a positive and

    significant relationship with credit risk.

    Masoud et al (2013) have studied risk management in Iranian banks. The purpose of their study

    was to investigate relationship between banking ratio with credit, liquidity and operational risks.

    They have used banks secondary data for the year2006 to 2011. Their findings reveal that capital

    adequacy have a negative relationship with credit risk whereas debt to equity ratio has a positiverelationship with credit risk.


    To comply with the objective of this research, the paper is primarily based on quantitative

    research, which constructed an econometric model. This research paper attempts to investigate

    the effect of specific factors on credit risk in Islamic banks. This study has used secondary data

    that were taken from annual reports of Islamic banks operating in Pakistan. There are 5 full-

    fledge Islamic banks operating in Pakistan such as: Meezan bank, Bank Islami Pakistan ltd., Burj

    Bank, Dubai Islamic bank, Al-Baraka bank. This study has used data from year 2007 to year 2011.

    E-views 5.0 are used for data analysis. Data is analyzed by applying descriptive and inferential

    statistics. The descriptive statistics apply to find the mean and standard deviation of the


    Pearson correlation is calculated to indicate the relationship between independent variables and

    to examine if there is any problem of autocorrelation between independent variables. And finally

    regression analysis is applied to derive the significance and relationship between dependent and

    independent variables.

    4.1. Research Models

    Following table shows the dependent and independent variables, their abbreviations and their

    proxies used in this study.

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    Table 2: Variables and Proxies

    Variables Abbreviations Proxies

    Credit Risk

    CR Ratio of Total Debt to Total


    Predictor variables

    Total Debt equity ratio DER Total Debt/ Total equity

    NPLs RatioNPL Non-Performing Loans/Total


    Capital Adequacy RatioCAR Tier 1 Capital + Tier 2 Capital /

    Risk Weighted Assets

    Bank's Size BS Logarithm of Total Assets

    Asset Utilization RatioAUR

    Operating Income/Total Assets

    Following Regression Equation is to be estimated in this research study:

    CRit = 0 + 1 (DERit) + 2 (NPLit) + 3 (CARit) + 4 (BSit) + 5 (AURit) + it (1)

    4.2. Variables and hypothesis development:

    1. Credit Risk

    Credit risk refers to delayed, deferred and default in principal amount or interest amount by

    counterparties which it is obligated to do. In this study the ratio of total debt to total assets is

    used as a proxy to depict credit risk. Because the higher the ratio the greater risk will be

    associated with banks operation. This ratio is an indicator of financial leverage of banks.

    2. Bank's Size

    Bank size is the major issue in calculating the risk about the banks. In this study, Log of total

    assets is used as a proxy for estimating banks size.H1: There exists a relationship between banks size (BS) and credit risk of Islamic banks.

    3. NPLs Ratio

    Non-performing loans are credits which are perceived as possible losses of funds due to loan

    default by banks. In this study, NPL ration is calculated through non-performing loans /total

    loans. It is expected that NPL has negative relationship with credit risk ratio (Ahmad et al., 2011;

    Ogboi and Unuafe, 2013)

    H2: There exists a relationship between non-performing loans (NPL) and credit risk of Islamic


    4. Capital Adequacy ratio

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    CAR refers to a percentage of a bank's risk weighted credit exposures. The formula to calculate

    CAR is as follows:

    Tier 1 Capital + Tier 2 Capital / Risk Weighted Assets

    This ratio is used to protect depositors and promote the stability and efficiency of financial

    systems around the world. It is expected that CAR has a positive relationship with Credit riskratio (OGBOI and UNUAFE, 2013)

    H3: There exists a relationship between capital adequacy ratio (CAR) and credit risk of Islamic


    5. Debt to Equity Ratio

    It is a measure of financial leverage of a bank which is calculated by dividing its total liabilities

    with stockholders' equity. It is an indication of the proportion of equity and debt the bank is

    using to finance its assets. Literature illustrates that debt to equity ratio has a positive relationship

    with credit risk (Masoud et al., 2013)

    H4: There exists a relationship between debt to equity ratio (DER) and the credit risk of Islamicbanks.

    6. Asset utilization ratio

    In this study, asset utilization ratio is calculated through by dividing Operating Income with total

    Assets of banks. Literature suggests that there is a negative relationship between credit risk and

    asset utilization ratio (Ahmed et al., 2011).

    H5: There is a relationship between asset utilization ratio (AUR) and credit risk of Islamic banks

    operating in Pakistan.

    5. Data analysis

    Descriptive Statistics

    Table 3: Descriptive Statistics

    Variables Mean Standard deviation

    Credit Risk (CR) 0.7818 0.2766

    Total Debt Equity Ratio (DER) 4.1817 4.3208

    NPL Ratio (NPL) 0.0292 0.0341

    Capital Adequacy Ratio (CAR) 0.3424 0.2786

    Bank size (BS) 6.7892 2.1986

    Asset utilization ratio (AUR) 0.0088 0.0445

    Table 3 shows the descriptive statistics (mean and standard deviation) of data variables. Mean value

    tells us about the central tendency of the data whereas standard deviations gives idea by measuring

    data that how much a typical data value differs or deviate from the mean value. It can be seen from

    the table that banks size has a greater mean value of 6.7892 with a standard deviation of 2.1986. Total

    debt equity ratio has a second highest mean value of 4.1817 with standard deviation of 4.3208. Credit

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    risk has a mean value of 0.7818 with a standard deviation of 0.2766 followed by NPL has mean value

    of 0.0292, capital adequacy has 0.3424 mean value, and asset utilization ratio has 0.0088 as mean value.

    Table 4: Correlation Matrix

    Table 4: Correlation Matrix

    Total Debt

    Equity Ratio


    NPL Ratio






    Bank size






    Total Debt Equity Ratio



    NPL Ratio (NPL) 0.310* 1

    Capital Adequacy Ratio(CAR)

    -0.422** 0.44 1

    Bank size (BS) 0.525** 0.281* 0.316* 1

    Asset utilization ratio


    0.544* -1.11** -3.88** 0.125** 1

    * Correlation is significant at 5% level (2-tailed).

    ** Correlation is significant at 1% level

    Table 4 shows the correlation matrix of the research variables in order to determine the problem ofmulti-co-linearity among variables. Results exhibits that NPL and DER; BS and NPL; BS and CAR; and

    AUR and DER are significant at 5% level whereas CAR and DER; BS and DER; AUR and CAR; AUR

    and BS are significant at 1% level. Moreover, CAR and DER; AUR and NPL; AUR and CAR are

    negative correlated. Overall, results showed that there exists no correlation among predictor variables.

    Table 5: Regression Analysis

    Model Unstandardized Coefficient T Significance (p-


    B Standard


    Constant -0.070 -0.76 -0.211 0.833

    Total Debt Equity

    Ratio (DER)0.066 0.013 5.110 0.000**

    NPL Ratio (NPL) -3.40 1.111 -0.308 0.759

    Capital Adequacy

    Ratio (CAR)0.411 0.200 2.111 0.041*

    Bank size (BS) 0.040 0.014 2.222 0.321

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    Asset utilization

    ratio (AUR)-5.112 2.223 -2.441 0.021**

    R-Square 0.89 F-Statistics 29.11

    Adjusted R2 0.87 Significance 0.000**

    Durbin Watson 1.693

    Dependent Variable:Credit Risk (CR)

    ** Coefficient is significant at 1% level

    * Coefficient is significant at 5% level

    Table 5 illustrates the regression analysis results of research variables. R-square value shows thatmodel is good-fit as 89% of the variation in dependent variable i.e. Credit risk can be explained

    by predictor variables i.e. total debt equity ratio, NPL ratio, capital adequacy ratio, bank size and

    asset utilization ratio and remaining 11% variation is due to other factors. Durbin Watson value is

    1.693 which means there is no problem of serial-correlation between data because rule of thumb

    about Durbin Watson test says that if value is below 2 then there seems no problem of auto

    correlation. F-statistics show the goodness of the model. F-value (29.11) is significant at 1% level.

    So, it can be said that this model is a good fit.

    T-statistic shows that hypothesis 1 is accepted as DER has a positive and significant relationship

    with credit risk ratio at 1% level. This means, the higher the debt equity ratio, the higher will be

    the credit risk ratio of Islamic banks operating in Pakistan. Hypothesis 2 is rejected because p-value is more than 0.05 significance level. But NPL ratio shows a positive relationship with credit

    risk ration of Islamic banks. Hypothesis 3 is significant and accepted because CAR has a positive

    and significant relationship with CR ratio. This means that credit risk ratio will be more when

    capital adequacy ratio of bank is more. Hypothesis 4 is rejected as bank size shows positive but

    insignificant relationship with credit risk ratio of Islamic banks. Hypothesis 5 is accepted as AUR

    has a negative and significant relationship with CR ratio of Islamic banks. This means credit risk

    will be more when asset management of Islamic banks operating in Pakistan is weak.

    Calculated Regression Equation

    CR = -0.070 + 0.066 (DER) - 3.40 (NPL) + 0.411 (CAR) + 0.044 (BS) - 5.112 (AUR) --- ---- (2)

    Table 5 also illustrates the values of beta used in regression equation. Constant value is -0.070

    which means credit risk ratio will decrease by 0.07 degree considering all other explanatory

    variables constant. Beta value of DER is 0.066 which means CR ratio will increase by 0.066 units

    when DER increases by 1 unit. NPL beta value is 3.4 which mean CR ratio will increase by 3.40

    units with 1 unit decrease in NPL ratio. CAR beta value is 0.411 which shows that CR ratio will

    increase by 0.411 units when CAR increases by 1 unit. BS beta value is 0.040 which means CR

    ratio will increase 0.04 units with every single unit increase in banks size. And lastly, AUR beta

    value is -5.112 which means that CR ratio will decrease by 5.112 units with every single unit

    increase in AUR.


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    The aim of this study is to investigate the firm level variables that are affecting credit risk of

    Islamic banks operating in Pakistan. For that purpose secondary data is taken from annual

    reports of Islamic banks for the period of 2007 to 2011. This study has employed credit risk as a

    dependent variable whereas debt equity ratio, NPL ratio, bank size, asset utilization ratio and

    capital adequacy ratio is taken as independent variables. Results show that credit risk has a

    positive and significant relationship with debt equity ratio and capital adequacy ratio. Besides,asset utilization ratio has negative and significant relationship with credit risk.

    The current study was conducted on credit risk management of Islamic banks operating in

    Pakistan. This study can be conduct on different countries using the same methodology. It is

    expected that different countries will have different findings which will be interesting to know.

    Future studies can also be conducted by comparing credit risk of conventional and Islamic banks

    of a country or different countries.


    Ahmad, N. H. and Arif, M. (2007). Multi-country study of bank credit risk determinants. TheInternational Journal of banking & finance, Volume 5, pp. 135-152.

    Ahmed, N., Akhtar, F. M., and Usman, M., (2011). Risk Management practices and Islamic banks:

    As Empirical investigation from Pakistan. Interdisciplinary Journal of Research in Business, 1(6),

    pp. 50-57.

    Altunbas (2005), Mergers and Acquisitions and Bank Performance in Europe- The Role of

    Strategic Similarities. European Central Bank, working paper series, No. 398.

    Brewer, Elijah III, and Thomas H. Mondschean. (1994). An Empirical Test of the Incentive Effects

    of Deposit Insurance. Journal of Money, Credit, and Banking, 26(1): pp. 146-164.

    Bashir, A. H. M. (1999). Risk and Profitability Measures in Islamic Banks: The Case of Two

    Sudanese Banks. Islamic Economic Studies, 6(2), pp: 1-24.

    Cihak, Martin & Hesse, Heiko (2008) Larger Islamic Banks Need Prudential Eye IMF Working

    Paper, European Department

    Drzik, J. 1998. CFO Survey: Moving Towards Comprehensive Risk Management. Bank

    Management, Vol. 71, pp.40.

    Faridah, N.M. (2005). Financing structure, bank specific variables and credit risk: Malaysian

    Islamic banks. Journal of Banking and Finance, 4(2), pp. 36-41.

    Fraser, D., Gup, B. and Kolari, J., (2001). Commercial Banking: The Management of Risk. 2nd Ed.Cincinnati, Ohio: South-Western College Publishing.

    Hayati, N.A. and Shahrul N.A. (2002). Key factors influencing credit risk of Islamic Banks: A

    Malaysian Case. University of Utara Malaysia. Working Paper Series 4012.

    Khan, T. and Ahmed, H., (2001). Risk Management An Analysis of Issues in Islamic Financial

    Industry. Islamic Development Bank-Islamic Research and Training Institute, Occasional Paper

    (No.5), Jeddah.

    Martin. C. and Heiko hesse, (2008). Islamic banks and financial stability: an empirical analysis.

    IMF working paper 2008.

    Masoud, B., Iman, D., Zahra, B., and Samira, Z., (2013). A study of risk management in IranianBanks. Research Journal of Recent Sciences 2(7), pp. 1-7.

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    Ogboi, C., and Unuafe, O. K., (2013). Impact of credit Risk Management and Captal Adequacy on

    the Financial Performance of Commercial Banks in Nigeria. Journal of Emerging Issues in

    Economics, Finance and Banking, 2(3, pp. 703-717.

    State Bank of Pakistan, Islamic Banking Department (June, 2013). Islamic Banking Bulletin (June)


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    3 How do the historical perspective and systemic effects of

    house price movements help to explain the pattern of

    consumption in the U.K?

    Priya Darshini Pun Thapa*, South London College, Equitable House


    The purpose of this research paper is to investigate whether the historical perspective of

    house price movements can help to explain the recent pattern of consumption in the

    UK. In addition to this, the document evaluates how strong the correlation between

    those variables within a specific period of time can be. This paper also attempts to

    investigate to which extent the current crisis, well-known as the subprime market crash,

    could have affected future expectations about house prices and consumer habits,considering the following three housing market hypotheses: 1) The wealth effect: an

    expected increase in house prices raises the desired level of expenditure; 2) the lower

    credit constraints, the higher consumption and 3) common causality model: factors such

    as changes in expected income growth, tax changes or changes in credit market

    conditions lead to increases in both household expenditure and house prices. Its

    findings about the coincidence of house prices and consumption during the last two

    decades have corroborated the hypothesis that an increase in house prices movements

    can help to explain the followed pattern of consumption.

    JEL Classification: C3, E3

    Key words: House prices, consumption, wealth effect, housing



    The global financial downturn in 2007 has remarkably affected the historical perspective of house

    price movements in the UK. Since then, this financial turmoil which had its origins in a previous

    credit crisis called the sub-prime mortgage market crash, can be considered to be the first domino

    in a whole chain. This type of lending practice, which presumably has changed the relationship

    between house prices and consumption, has not only clearly marked a historic turning point in

    the UK economy, but it has also set in motion fundamental changes in the credit market in terms

    of consumer habits, peoplesexpectations and government regulations.

    It is often believed that this phenomenon, accompanied by a strong fluctuation in house prices,has also helped to multiply its devastating snowball effects on the economy, especially for those

    household victims of the credit crunch, who saw their consumer spending fall as a cascade after


    Therefore, the subprime market crash, led by financial institutions in the mortgage market such

    as HBOS, nationwide, Northern Rock etc, could be the most recent explicative fact that

    presumably could have changed the connection between house prices and consumption in the

    UK since the 1980s according to Pricewaterhousecoopers[20]). However, and based on previous

    downturns in the UK economy as seen in 1991, it has not yet occurred. Arguably, one may tacitly

    *Priya Darshini Pun Thapa is Lecturer at South London College, 10 Woolwich New Rd, London SE186AB, [email protected]

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    suggest that the magnitude of this lending practice can only have a major impact on countries

    where the credit market is weak and not well developed; unlike the UK where financial

    institutions are more concerned about financial stability and the well-being of the economy

    through regulation.

    Bearing this in mind, the following three housing market hypotheses will be considered: 1) a

    wealth effect [16]: an expected increase in house prices raises the desired level of expenditure; 2)

    the lower credit constrains, the higher consumption [13] and 3) common causality model: factors

    such as changes in expected income growth, tax changes or changes in credit market conditions

    lead to increases in both household expenditure and house prices [13]. In addition, a historical

    perspective of two decades will also be used to investigate how some observable facts from the

    past have led and influenced the relationship between house prices and consumption, and also

    how some endogenous variables have accentuated the crisiseffects to a larger extent. Finally, the

    analysis will run a test on the response of household consumption to house prices; considering

    both housing as a major component of wealth and credit access as a source of liquidity.


    According to the literature there are three main key housing market hypotheses that could

    explain the link between house prices and consumer spending: 1) a wealth effect

    [16] i. e. an expected increase in house prices raises the desired level of expenditure; 2) the lower

    credit constrains the higher consumption [13]; and 3) common causality model: factors such as

    changes in expected income growth, tax changes or changes in credit market conditions, could

    lead to increases in both household expenditure and house prices [13]

    Recent studies show that consumer expenditure is not only the dominant component of

    aggregate demand, but is also the key factor for understanding the behaviour of the housing

    market. In recent years there has been increasing interest in the role of housing and its interaction

    with consumption. Benito and Haroon *4 point out that houses are a significant part ofhousehold wealth and this higher wealth is typically associated with higher consumption, at

    least among those who own houses.

    In the same way *20 remarks the importance that houses bring to the market by quoting an

    increase in house price arguably makes non-home owners worse off via higher rents or the higher

    savings required for future house purchases. Hence the consumption of this group may

    decrease and the overall wealth effect may be insignificant as a result of being non-home owners.

    Secondly, households may borrow vastly more cheaply if they own housing equity which may be

    used as collateral. Then an increase in house price raises housing equity and cheaper borrowing

    typically results in increased consumption. Thirdly, both house prices and household

    consumption tend to be positively related to household expectations of future earnings.

    Additionally, [4] and [17] assert another view that there is an important causal effect of housing

    in providing collateral which allows credit to be obtained on more favourable terms to finance

    consumption. That role may be particularly strong, or only exist at all, for those who might well

    be less constrained by the availability of easy access to credit