an introduction to non-maturity deposit rate and balance modeling
TRANSCRIPT
An introduction to non-maturity deposit rate and balance modeling
February 2016
kpmg.com
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Exploration of factors that affect deposit rates . . . . . . . . . . . . . . . . . . . . . . . 2
Selective MEVS for consideration in NMD rate modeling (illustrative purposes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Using the runoff, retention and growth estimation for behavioral analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Key challenges to modeling NMDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
How KPMG can help – detailed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Data requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Selective MEVs for consideration in NMD balance modeling (illustrative purposes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Graph depicting the movement of NMD balance w .r .t . MEVs (illustrative purposes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Correlation between sample MEVs and NMDs based on aggregate industry deposit data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Key factors included in the modeling of non-maturity deposit balances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Segmentation – “stable” versus “less-stable” . . . . . . . . . . . . . . . . . . . . 10
Deposit modeling methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
a . Quantitative approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
b . Non-quantitative approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
c . Hybrid approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Deposit modeling in the context of asset/liability management . . . . . . . 14
Chart – 5: KPMG’s robust modeling approach . . . . . . . . . . . . . . . . . . . . . . 15
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Introduction
Non-Maturity Deposits (NMDs) attract a lot of attention from banks as well as regulators. This “demand liability” constitutes a major portion of the liability side of a bank’s balance sheet. NMDs, such as retail savings, interest and non-interest bearing checking, and money-market accounts have no stated maturities and depositors can withdraw their funds at any time without penalty. The depositor’s early redemption option is very challenging to model accurately.
Banks typically view NMDs as a stable source of funding for their credit and investment books on the asset side. There are two primary types of models needed to forecast deposit cash flows: 1) Rate Models for interest-bearing NMDs and 2) Balance Models for “all” NMDs.
Deposit rate models attempt to ascertain the relationship between short-term market interest rates and bank deposit rates, i.e., rate beta ( ).
=Change in Deposit Rate
Change in Market Rate
Betas may differ in rising versus falling market rate scenarios. Historically, deposit rates tend to lag market rates on the way up and adjust quicker than market rates on the way down. Deposit rate beta models are often based on a regression analysis. However, management judgment is sometimes used to derive rate betas. In addition, deposit rates offered by competitors can also influence rate-setting behavior. More advanced deposit rate models attempt to capture competitor deposit rates and other non-market rate factors.
Deposit balance models attempt to determine the relationship between short-term market interest rates (along with non-rate factors) and deposit balances. This paper will primarily focus on Balance Models. The modeling of NMD balances is relatively more difficult to model than rates. NMD balances can be volatile and influenced by numerous factors. The accurate modeling
of NMDs is a very important part of the asset/liability management (ALM) function for any commercial bank or deposit taking Financial Institution.
It is not uncommon for bank assumptions about deposit balances to have an outsized impact on their measurement of interest rate risk and liquidity risk. Since the financial crisis of 2008, bank managements and regulators have increasingly focused their attention on the forecasting and analysis of NMD, especially after an extended period of low short-term market interest rates and the possibility of a FED tightening.
This paper explores leading practice methodologies that can be utilized to robustly model NMDs to assist banks and other financial institutions in managing their interest rate and liquidity risk more effectively. There are two main reasons why banks should focus on developing robust deposit models:
Interest Rate Risk Management
For a bank to control interest rate risk, management of the Asset and Liability sides of the balance sheet is necessary and require assumptions about the behavior of NMDs. NMD assumptions affect interest rate risk measurements related to net interest income simulation and market value of equity or duration analysis. Inaccurate modeling of NMDs can lead to misleading interest rate risk exposure measurement and suboptimal decisions.
Liquidity Management
For a bank to remain solvent, liquidity needs to be managed and for this NMDs play a pivotal role. NMD is one of the most inexpensive forms of funding, but the on-demand nature, i.e., redemption option, requires a financial institution to understand the risk and stability of its deposit base. New regulatory requirements, such as the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR), also require a detailed understanding of NMD behavior.
Exploration of factors that affect deposit ratesVarious Macro Economic Variables (MEVs) impact the movement of NMD rates. There can be cases where the three main categories of NMDs, i.e., Savings, Checking and Money Market, can move in line with different MEVs. A regression analysis of industry data was conducted where NMD rates were considered dependent variables. One and six month Libor rates and three, six and one year Treasury rates were considered predictor or independent variables.
The graph and the correlation study for the same is given below for illustrative purpose to identify the potential drivers which impact deposit rates.
3An introduction to non-maturity deposit rate and balance modeling
MEVs Savings Interest Checking Money Market <100M
Libor 1 Month 0.78 0.79 0.80
Libor 6 Months 0.53 0.57 0.63
Treasury Rate 3 Months 0.72 0.71 0.71
Treasury Rate 6 Months 0.82 0.83 0.86
Treasury Rate 1 Year 0.85 0.87 0.91
Table – 1: Correlation analysis of MEVs with NMD interest rates
As previously mentioned, deposit rate modeling can include other factors beyond a single rate beta. Enhancements to deposit rate models can include lags, multiple betas under rising and falling market rates, and competitive deposit-setting behavior. Now let us turn our attention to deposit balance modeling. We will start with a general framework for analyzing deposit balances.
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Libor 1 Month Treasury Rate 3 Months Treasury Rate 6 Months
Treasury Rate 1 Year Libor 6 Months
Selective MEVs for consideration in NMD rate modeling (illustrative purposes)
Graph-1 | The graph depicting the relationship of NMD A/c interest rates with MEVs | Source for NMD rate data – FDIC
5
Using the runoff, retention and growth estimation for behavioral analysis
Categorization of Deposit Base into
Short term and Long term buckets
Computation of Runoff/Decay, Replacement and Growth
Macroeconomic Scenario
Analysis done using Multiple
Linear Regression
Liquidity Management/ Balance Sheet Optimization/ Asset Liability
Mismatch Management
Behavioral Analysis for the depositors
are done using Categorization of the Depositors:
Consumer, Small Business and Commercial Businesses
Chart – 1: Industry Leading Practices
Developing a more precise runoff profile of the deposit portfolio improves the effectiveness—and potentially reduces the cost—of liquidity risk management.
Modeling for Non-maturity deposit volume requires the following:
– Deposit Decay Rate/Run-off percentage
– Retention Rate and
– Forecasted Growth
An introduction to non-maturity deposit rate and balance modeling
For the above method the Run-off percentage, Retention rate is computed and then the Decay rate is calculated using the following formula:
Decay Rate = Run-off ÷ Total Deposits
0 2 4 6
Time Period
NMDs, $
Non-maturity deposit runoff and replacement
8 10 12
Deposit Base
Forecasted Growth
Run-off Replacement
Graph-2 | The Graph depicting the NMD Runoff, Replacement and forecasted growth | Source: FDIC
These targets are achieved based on a behavioral analysis of depositors. Runoff behavior of deposit accounts varies significantly depending upon a wide range of factors, market segments, and individual customer characteristics. A behavioral assessment focusing on these factors enables the generation of more accurate run-off expectations. For example, for retail customers, factors such as the vintage, ATM visit frequency, types of accounts maintained and so on are important. For small businesses, factors such as Credit Usages, Interest payment frequency, and debt servicing history may be important. For Large Institutions, Treasury trade usages, Industry segment and Net Borrowing positions could be important factors.
Using the runoff, retention and growth estimation for behavioral analysis (cont..)
Key challenges to modeling NMD
While many banks face challenges in developing NMD assumptions, global banks in particular are facing increasing scrutiny over their NMD models. Basel recently released new guidance on interest rate risk and how deposit modeling impacts its’ measurement. Some of the global key challenges which banks are facing that make NMD modeling difficult are listed below.
No significant change in short-term market rates since 2008Post the 2008 financial crisis, there has been no significant changes in short-term market rates. Key interest rates, such as Fed Funds rate, have been very low for many years. Deposit rates have remained very low over this same period. Therefore, regressions based on this time period will be greatly constrained by data from one particular rate regime.
Influx of Deposits into Banking SystemThere was a very large increase in deposits due to a “flight-to-quality” precipitated by the financial crisis of 2008. These deposits have remained in the banking system. There has been much debate on how sticky these deposits will be when market rates begin to rise.
Regulatory Challenges and New Regulations (Basel III)Post the financial crisis, regulatory scrutiny increased substantially for banks. Banks are expected to model their deposits accurately to conduct stress testing. In addition, they are required to hold sufficient capital against interest rate and liquidity risk.
New regulations from Basel, such as the LCR and NSFR, focuses more on the core deposits (NMDs), held by a bank for a particular tenure (30 days and 1 years respectively for LCR and NSFR).
Excess Liquidity at many banks post-financial crisisExcess liquidity poses a problem for banks as the same must be invest NMDs at currently low rates. This has reduced the net interest margin for banks. Deposit rates are at a floor while asset rates have continued to fall over the last number of years.
Small amount of data and in some cases unavailability of historical dataMany banks, especially community banks and de novo banks, do not have sufficient historical data or no historical data at all. This poses problems for conducting robust deposit analysis. We have also found large institutions that face challenges with sourcing granular deposit data over a sufficiently long historical period to analyze.
Difficult to predict the behavior of retail customersIt is difficult to accurately predict the behavior of individuals on a purely economic or rational basis. For example, although deposit rates were low during and after the crisis, depositors accepted low rates in order to safeguard their funds, i.e., flight-to-safety. These non-economic factors complicate statistical analysis of deposits and often require the use of categorical variables.
Potential inaccurate deposit assumptions in stressed scenariosBanks may under or over-estimate the stability of their deposits. This can lead to inaccurate asset and liability management decisions. Model risk makes it necessary to perform assumption sensitivity testing.
7An introduction to non-maturity deposit rate and balance modeling
How KPMG can help – detailed approachKPMG, with its expertise in the field of Deposit Model development, can help banks develop models to accurately estimate the NMD of a bank’s portfolio. KPMG approach includes collecting data, creating a data mart, segmenting data, choosing modeling techniques, and stress testing. These steps are described in detail below.
Data requirementsKPMG explores various sources of data to create a data mart to conduct a detailed exploratory data analysis to define the segmentation and modeling approach.
Data Mart
FDIC
DDA
NOW
MMDA
SavingsKeyMEVs
OtherSources
FederalReserve
Internal Data Pool
Chart – 2: Different Data Sources
Selective MEVs for consideration in NMD balance modeling (illustrative purposes)There are numerous macro-economic variables (MEVs) that may impact a financial institution’s deposit balances. The significance of particular MEVs vary by institution. The following are a few potential MEVs that may impact the expansion or contraction of deposits.
U .S . Unemployment rateThe US unemployment rate affects economic growth and the Fed’s decision making on lowering or hiking the interest rates. Changing unemployment rates may impact deposit balances.
5 Year treasury rateThe 5 year Treasury Rate reflects the long term risk premium required by investors. Changes in long term rates or the yield curve may impact deposit balances or movements among deposit types, e.g., NMD versus Certificates of Deposit.
3 Month LIBOR rateShort-term rates are a key indicator of liquidity in the market. They also serve as a proxy for investments that depositors may use as alternative to bank deposits.
Repo rateThe repo rate is the rate banks receive for short-term lending of investments, i.e., U.S. Treasuries. This is a short-term market rate which, along with LIBOR and other rates, may impact deposit balances.
NegativeCorrelation
Type of Relationship of NMD with the MEVs
VIX
Real GDP growth Rate(Inflation Adjusted)
House Price Index United S.tates unemployment rate
Mortgage Rate
Repo Rate and FedFunds Rate
5-Year Treasury Yieldand 1 M LIBOR Rate
PositiveCorrelation
Chart – 3: Relationship of MEVs with NMD
Dow jones total stock market indexThe Dow Jones Total Stock Market index is a popular proxy for the broad U.S. Stock market. Depositors may see common stocks as an alternative to deposits under certain conditions.
9An introduction to non-maturity deposit rate and balance modeling
Graph depicting the movement of NMD balance w .r .t . MEVs (illustrative purposes)
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Dow Jones Total Stock Market Index (Level) (Scalled up by 1000) NMD
House Price Index (Level) (Scalleup by 100,000)
Graph – 3: Relationship of potential MEVs with NMD
Note: Scaling factors have been used for fitting the MEVs to the graph above.
It can be difficult to visually see the relationship between MEVs and deposit balances in graphs, such as Graph 1 above. Therefore, statistical methods are often used to clearly identify these relationships. For example, Table 1 below shows the relationship between various MEVs and Deposit Balances derived via statistical methods.
Correlation between sample MEVs and NMDs based on aggregate industry deposit data
MEVs Mar 2005-Mar 2015 Dec 2009-Mar 2015 Mar 2012-Mar 2015
Unemployment rate 0.41 -0.99 -0.98
5-year Treasury yield -0.83 -0.27 0.91
Dow Jones Total Stock Market Index
0.70 0.95 0.96
House Price Index -0.44 0.87 0.97
3M Libor -0.80 -0.45 -0.74
Repo Rate (Treasury) -0.73 0.17 -0.45
Table – 2: Correlation analysis of MEVs with NMD balances
Note: The figures above are correlation coefficients ranging from -1.00 to + 1.00 with -1 meaning a perfect inverse relationship and +1.00 meaning perfect direct relationship.
Data requirements (cont..)
Key factors included in the modeling of non-maturity deposit balancesLong term trendFor estimation purposes, the long term deposit trend needs to be taken into account as the short term trend may not provide the correct estimate. In addition, many banks desire to derive long term forecasts.
Underlying or Core deposit trendThe Core deposit trends needs to be taken into consideration as it reflects the relationship between stable and less-stable deposits. It shows the bank deposit stability trend over a period of time with respect to the volatile trend.
SeasonalityNMD movement can be seasonal based on the behavior of the customers of a bank. This can be observed within a span of one year or beyond. For example, this impact can often be seen around holidays that involve increased purchases.
Annualized (Daily) volatilityThe volatility of NMD balances can be attributed to the attrition, acquisition, and/or cross product movement of customers of a bank. This can pose a serious challenge to a bank and should be addressed while building an estimation model.
Stochastic movementThere may be some component of a deposit balance which could be very volatile because of depositor or market conditions. This can be predicted using the stochastic process of balance movement and capture the probability-oriented balance.
Trend BreaksIt has been seen from past experience that there can be a trend break in deposit balances because of no activity in accounts or sudden inflows of deposits that makes a trend model unstable. It is necessary to capture trend break points when applying modeling techniques.
Segmentation – “stable” versus “less-stable”
NMDAccounts
Stable Less-Stable
Chart – 4: NMD Segments
NMD accounts can be classified into “Stable” versus “Less Stable” segments depending on the potential of funds to move into and out of these accounts. There can be withdrawals deposits, or cross migration of funds among multiple NMD account types, e.g., checking, money market and Savings. In addition, funds can move completely out of NMD to longer-term fixed maturity deposits (CDs). On the other hand, some portion of NMD can be considered “Stable.” This portion of NMD tend to be more stable over time due to minimum balance requirements and customers’ needs. Typically, the “Less Stable” balances are modeled with interest rate sensitivity. There are a number of approaches that can be used to segment deposits into “Stable” versus “Less Stable” cohorts. Most of these approaches require analyzing historical deposit data.
11An introduction to non-maturity deposit rate and balance modeling
$-
$200
$400
Q2'2013 Q3'2013 Q4'2013 Q1'2014 Q2'2014 Q3'2014 Q4'2014 Q1'2015
Bal
ance
s (in
Mn) Stable Deposits = $288 Mn (82%)
Stable (Average of Predicted Deposits) Less-stable (Actual Predicted Deposits)
Stable vs. Less – stable component
Graph-4 | Graph depicting the movement of Stable and Less Stable component
The following formula can be used to compute the Stable component ratio:
Stable Component Ratio =Stable Component
99% Confidence Interval of Predicted Deposit Balances
Where,
– Stable component is derived as shown below in Approach 1, Approach 2 or Approach 3; and
– Less-Stable component is the actual predicted deposits
Some Simple approach are listed below:
Approach
Stable component Ratio = Average of Predicted Deposits
Time (Month) Predicted Deposits
1 601
2 616
3 604
4 638
5 674
6 642
7 684
8 704
9 730
10 723
- -
- -
- -
- -
- -
N 798
Table – 3 | Depicting the Stable component ratio
Apart from the above approach the following approaches can be used
Stable Component = Average (Predicted Deposits)
Using Moving Averages:
Stable component ratio = Average (Moving averages of 6 months actual predicted deposits)
Using Moving Medians:
Stable component ratio = Average (Moving medians of 6 months actual predicted deposits)
There are some complicated advanced approaches as well:
– Identify the characteristic of “Stable” vs. “less Stable” using data analysis and then use one of the quantitate approach mentioned below to predict.
– Holt Winter Exponential Smoothing.
Deposit modeling methodologiesMethodologies to model NMD balances be followed can be broadly classified into (a) Quantitative, (b) Non-Quantitative, and (c) Hybrid approach.
a. Quantitative approachQuantitative or statistical modeling approaches can be used for the estimation of deposit balance behavior. There are several techniques which could be used as potential modeling techniques. Pros and cons are given below for each technique:
Method Pros Cons
Multiple Linear Regression
– Simple statistical model that is useful for forecasting and is easy to interpret
– Can use more than one predictor; synergistic relationship can be modeled
– Approach allows easy handling of autocorrelation and heteroscedasticity
– Significant dependency on external/macroeconomic factors
– It is not always possible to get a linear relationship between dependent and independent variables and transformations become complex in nature
ARIMAX approach
– Time dependent linear relationship may provide better forecast
– Better fit, less error, captures time impact and autocorrelation which happens over a period of time
– Research shows better fit on smaller data set and captures trend breaks
– Relies exclusively on past demand data
– Conversion of real equation to evaluate error is complex or complicated to fit
Vintage based approach
– Takes into account the historical data and creates several overlapping time series data with different starting times for prediction
– Used in banking industry for various applications
– Requires long horizon of data
– Less statistical in the nature
Markov Chain approach
– Approach to capture the dynamic nature of balance and probability association of volatility from the past time series
– Captures movement of accounts from one state to another
– It is complex to build and understand
– Need to make some key assumptions before it can be applied
Table – 4: Pros and Cons of different modeling approaches
13An introduction to non-maturity deposit rate and balance modeling
When a bank’s historical data set is small, KPMG may provide an estimation of deposits using ARIMA(X) or Markov chain approach. On the other hand, when the data set is large, i.e., long term historical data is available for a bank, then Multiple Linear Regression or Vintage-based approach can also be employed along with ARIMAX and Markov Chain.
b. Non-quantitative approachQualitative factors after communicating with lines of business and expert judgment may be taken into account for deposit behavior estimation.
c. Hybrid approachA combined solution of quantitative and non-quantitative approaches may be used for estimation. This involves allowing a quantitative model to provide a base estimate of deposit behavior and overlaying results with justifiable management overlays.
Deposit modeling in the context of asset/liability managementRobust modeling of non-maturity deposits can lead to more accurate measurements of interest rate risk and liquidity risk, respectively, within the context of ALM.
Interest rate risk – market value of equity (MVE)A deposit balance model and rate model to project cash flows, as well as an appropriate discount rate, are required to derive a present value of deposits for Risk to MVE purposes. Some practitioners also analyze branch sale premiums to get an indication of the value of deposits. Deriving the initial value of NMDs is the first step in MVE analysis. The change in market value of deposits under market rate changes can be calculated using a full re-valuation or an estimate using the duration and convexity of the deposits.
The assumptions used for deposits, i.e., duration/life estimates, can have a significant influence on the Risk to MVE measurement for a typical bank. The measurement of Risk to MVE helps inform management’s longer-term decisions regarding NMD strategy and interest rate risk hedging.
Interest rate risk – net interest income (NII)The deposit balance and rate model are also needed to project interest expense for measuring interest rate risk to NII. However, due to the often short-term nature of these measurements, deposit rate models tend to be relatively more impactful.
Net Interest Income can be projected under different rate scenarios to assess whether a balance sheet is asset-sensitive, i.e., assets repricing faster than liabilities, or liability-sensitive, i.e., liabilities repricing faster than assets. The measurement of Risk to NII helps inform management’s decisions regarding NMD rate-setting and retention strategies, as well as natural and synthetic interest rate risk hedging decisions.
Liquidity riskThe deposit balance model and rate model are also needed to measure liquidity risk. However, deposit balance models are more impactful for liquidity risk measurement. The primary question that these models help answer is how reliable are NMDs for funding purposes in normal and stressed environments. In the current environment, there is a lot of concern from banks and regulators on whether the deposit influx prompted by the financial crisis will become a rush to the exits if market rates increase significantly.
Assumption sensitivity analysisAll models are subject to model risk. NMD models are no exception. Leading practice is to conduct NMD assumption sensitivity testing. This testing answers the question of how interest rate risk and liquidity risk measures change under alternative NMD assumptions. The assumptions that are typically tested are deposit rate beta and deposit life assumptions.
15An introduction to non-maturity deposit rate and balance modeling
Chart 5: KPMG’s robust modeling approach
Mas
ter
Dat
a
Modeling Data
Validation Data
Validation of Model Final Model Yes Meet Performance Criteria
Data Mart
FDIC
DDA
NOW
MMDA
SavingsKey
MEVs
OtherSources
FederalReserve
Internal Data pool
NO
Initi
al M
odel
Rem
edia
tion
Seg
men
tatio
n
Stable Balances
Quantitative
ARIMA
Linear Regression
Non Quantitative Expert Judgment
Less-StableBalances
Quantitative
ARIMA
Markov Chain
Vintage Based
Hybrid Ensemble Solution
Stress Testing, Sensitivity Analysis
and Parameter Uncertainty
Run Model equation with same beta coefficient across CCAR
and DFAST scenarios
Creation ofDocuments
Benchmarking and Outcome Analysis
ConclusionModeling NMDs is very challenging for many financial institutions. KPMG can assist management in solving key issues and complexities related to the estimation of non-Maturity deposit rate and balance behavior. We have expertise to assist financial institutions in building leading practice deposit rate models that are based on market rate and non-market rate factors. We will utilize an appropriate approach to modeling deposit balances for your organization whether quantitative, non-quantitative, or hybrid.
KPMG has ready approaches to assist organizations in modeling deposits when minimal historical deposit data is available. In addition, we have the capacity to use advanced statistical modeling approaches to facilitate the assessment of the impact of macro-economic variables on deposit rates and balances. KPMG’s deposit modeling approach is designed to assist financial institutions in meeting stringent regulatory requirements and guidance.
Sources
– Federal Reserve Bank Web site, FRB H8 data set for Large Commercial banks in the US Banks, http://www.federalreserve.gov/datadownload/Choose.aspx?rel=H8.
– Federal Deposit Insurance Corporation Web site, Interest Rate data for NMD section, https://www.fdic.gov/.
– Bank for International Settlements Web site, BIS Consultative paper on Interest rate risk in the banking book, https://www.bis.org/bcbs/publ/d319.htm, June 2015.
– Bank for International Settlements Web site, http://www.bis.org/.
– Bureau of Labor Statistics Web site, http://www.bls.gov/.
– Federal Reserve Bank Web site, http://www.federalreserve.gov/.
– Federal Reserve Bank of St. Louis Web site, http://research.stlouisfed.org/fred2/.
– DTCC Web site, http://www.dtcc.com.
The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation.
17An introduction to non-maturity deposit rate and balance modeling
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Key contactsMark Nowakowski Principal, Market and Treasury Risk T: +1 404-222-3192 E: [email protected]
Bin Hong Managing Director, Market and Treasury Risk T: +1 213-430-2127 E: [email protected]
AuthorsRoderick Powell Director, Market and Treasury Risk T: +1 404-222-3145 E-mail: [email protected]
Arunava Banerjee Associate, Financial Risk Management T: +91 981-920-6354 E: [email protected]
Abhaya Kant Srivastava* Financial Risk Management
*Abhaya Kant Srivastava is no longer part of KPMG
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