options on federal funds futures and interest rate volatility

I am grateful to an anonymous referee, Vaishnavi Bhatt, David Gulley, David Nanigian, Kuldeep Shastri,David Simon, and the editor, Robert Webb, for helpful suggestions. I also thank the CME Group for provid-ing data on the volume of fed funds options. I am responsible for all remaining errors.

*Correspondence author, Department of Finance, Bentley University, 175 Forest Street, Waltham, MA02154-4705. e-mail: [email protected]

Received May 2008; Accepted February 2011

■ Jahangir Sultan is a Professor of Finance and the Founding Director of the Hughey Center forFinancial Services, Bentley University, Waltham, Massachusetts.

© 2011 Wiley Periodicals, Inc.

DOI: 10.1002/fut.20524

OPTIONS ON FEDERAL FUNDS

FUTURES AND INTEREST RATE

VOLATILITY

JAHANGIR SULTAN*

This study examines the response of the spot and futures interest rates on the fedfunds, Eurodollar, and Libor to the listing of CME fed funds options. With theexception of the Libor futures, the introduction of options is associated with adecrease in the conditional volatility of the interest rates in the sample. There isalso evidence that the volume of options trading has a negative effect on the fedfunds and the Eurodollar spot rates. In contrast, the fed funds and the Eurodollarfutures rates respond positively to the volume of options trading. Overall, stronggeneralization of the effects of options listing and options trading across the mar-kets is not possible. These results remain robust even after controlling for severalexogenous variables including changes in the Fed’s target for the fed funds rate,the TED spread, the 9/11 terrorist attacks, and day-of-the-week effects. © 2011

1. INTRODUCTION

The CME options on fed funds futures (ticker symbols FFC (call) and FFP(put)) are American options for one unit of fed funds futures. First introduced

Published online April 14, 2011 in Wiley Online Library (wileyonlinelibrary.com).

The Journal of Futures Markets, Vol. 32, No. 4, 330–359 (2012)

Wiley Periodicals, Inc. Jrl Fut Mark 32:330–359, 2012

2 Sultan

Journal of Futures Markets DOI: 10.1002/fut

on March 14, 2003, monthly contracts are available for up to two years.1 Boththe fed funds futures and options allow market participants to track the market’sexpectations of monetary policy by the FOMC. To quote the CME, “Perhaps nosingle financial policy carries more weight than the fed funds target.Consequently, in the weeks preceding an FOMC meeting, the fed fundsfutures and options contracts attract attention from all corners of the financialworld.”2 As of today, the effects of the introduction of fed funds options on theprimary markets have not been discussed in the literature.

This study examines the response of the level and volatility of the spot andfutures interest rates on the fed funds, Eurodollar, and the Libor to the intro-duction of options on CME fed funds futures. A key result in the study is thatoptions listing is associated with a decrease in the conditional volatility of theinterest rates, with the exception of the Libor futures. In addition, the effect ofoptions trading volume on the volatility of spot interest rates (fed funds andEurodollar) is negative, implying that options stabilize the primary markets.These results suggest that the CME fed funds options offer additional informa-tion on monetary policy and thus resolve uncertainty in the spot market. Incontrast, trading on options leads to higher volatility in the futures markets (fedfunds and the Eurodollar), implying that options on fed funds futures destabi-lize close substitutes. This is consistent with the notion that if written optionsare hedged in the futures market, it has the potential to raise volatility in themarket. Overall, these results remain robust even after controlling for severalexogenous variables including changes in the Fed’s target for the fed funds, theTED spread, and day-of-the-week effects. Furthermore, these results are alsorobust to unexpected shocks including the 9/11 terrorist attacks and the recentcredit crisis.

The study is organized as follows. Section 2 reports on the trading activityon the CME options on fed funds futures. Section 3 reviews the literature anddiscusses the hypothesis. Section 4 offers the empirical results. The final sec-tion has concluding remarks.

2. CME FED FUNDS OPTIONS

An American style call option on the CME fed funds futures gives the buyer theright to establish a long position in the fed funds futures. Therefore, if one expects the Fed to raise (lower) the target fed funds rate, then a profitable strategy

1The minimum tick size is one quarter basis point which is equivalent to $10.4175. According to the CME,the monthly (November 2009) volume was 463,670 contracts, which was 5.2% higher than the previous yearvalue for the month. Prior to the merger between the CBOT and CME, options on fed funds futures tradedon the CBOT.2Source: 30-day Federal funds Futures and Options, CME Group. http://www.cmegroup.com/trading/interest-rates/files/IR-143_FEDFUNDSFC_lo-res_web.pdf, accessed on September 22, 2009.

331Federal Funds Futures and Interest Rate Volatility


Federal Funds Futures and Interest Rate Volatility 3


would be to buy a put (call) option. The strike prices are set around the previ-ous day’s closing price for the futures contract. So, there would be 21 strikeprices around the closing price at 6.25 basis point intervals. In addition, therewould be 10 more strike prices outside of the band (5 increments of 12.50basis points above the fed funds futures price and 5 decrements of 12.50 basispoints below the fed funds futures price). Options are quoted with a quarterbasis point tick size, which is worth $10.4175.

The underlying instrument for the CME fed funds options is the CME 30-day fed funds futures contract which is a contract for the delivery of theinterest paid on $5 million overnight fed funds held for 30 days. The futurescontract is cash settled against the average of the daily fed funds effective ratesfor the delivery month. Since their introduction, these contracts have becomepopular for hedging, speculation, as well as for predicting Fed policy outcomes.As Carlson, Melick, and Sahinoz (2003) claim, the fed funds futures rate is based upon a “deliberative” policy outcomes of the Fed because on averagethe fed funds rate moves with the target fed funds rate. Since the target fedfunds rate is effectively managed by the actions of the FOMC, the fed fundsfutures contracts provide quite a reasonable estimates of the policy outcomeson average.

It is critical to note that the fed funds futures contracts offer market participants only two outcomes, the probability of a rate increase and the prob-ability of a rate cut. Therefore, a single futures contract cannot adequately cap-ture the entire distribution of expectations about the target path of the fedfunds rate. With the introduction of CME fed funds options on March 14,2003, options prices at different strike prices offer market participants an addi-tional tool to survey varying market expectations regarding monetary policystance. According to Carlson, Craig, and Melick (2005), if market participantshave a wide range of expectations on the next policy outcomes, the fed fundsoptions prices can adequately incorporate such expectations. The authors showthat options prices at different strike prices and for different maturities can beutilized to capture the probability density function for the target path of the fedfunds rate.

Daily data on volume and open interest on options for the period12/1/2003 to 5/29/2009 are obtained from the CME group. The daily averagevolume for call options for the period is 15,693 contracts with a standard devi-ation of 16,825 contracts. The average open interest on call options are450,491 with a standard deviation of 223,544 contracts. For put options, thedaily average volume for the period is 10,945 contracts with a standard devia-tion of 13,427 contracts. The average open interest on put options is 338,863contracts with a standard deviation of 169,171 contracts. Finally, daily averagevolume (call plus put volume) is 26,638 contracts, with a standard deviation of

332 Sultan


4 Sultan


24,621 contracts. The daily average open interest is 789,355 contracts and thestandard deviation is 342,074 contracts.

In Figures 1 and 2, daily options volumes are plotted against the fed fundsspot rate. As Figure 1 illustrates, the volume of calls fluctuated during theentire period, rising to a record level of 208,637 contracts on October 31,2007. Interestingly, this was also the period when interest rates gradually start-ed to decline, perhaps in response to the massive liquidity injections by the Fedand other monetary authorities to stave off the credit crisis. A declining interestrate would prompt buying call options on the fed funds futures as futuresprices are expected to rise.

In contrast (Figure 2), trading on puts was more active trading on callsduring 2003–2006, although the average number of contracts traded was lowerthan call options. As interest rates were rising since 2004 and then stabilized inthe early summer of 2006, there was heavy trading on puts. This is expectedwhen rates rise, fed funds futures price is expected to fall, and buying putoptions on futures is profitable. Trading on put options peaked on January 30,2008, when volume reached to a record level of 121,318 contracts.

Overall, trading on CME options on fed funds futures is evolving. As options, futures, and spot instruments are combined for trading and risk

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FIGURE 1Options on fed funds futures (call option volume and fed funds rate).





management, the link between these markets is expected to strengthen. In par-ticular, the information content of the fed funds options market may offer valu-able insights for forecasting the future policy stance of the Fed.

3. REVIEW OF THE LITERATURE ON THEEFFECTS OF DERIVATIVES LISTING ON PRIMARY INSTRUMENTS

The effects of options and futures trading on the underlying markets have beendiscussed in a number of studies. In one strand of the literature, derivatives areconsidered as substitutes for primary securities; they reduce trading activity inthe primary markets, promote speculation, and subsequently reduce social wel-fare (Stein, 1987). A contrasting view is that derivatives allow for an efficienttransfer of risk from the informed to uninformed traders and are essential forstabilizing the primary markets (Grossman, 1988). A brief review of the litera-ture is provided next.

3.1. Effects of Futures Listing

Futures markets allow market participants greater risk management capabili-ties. So, as spot positions are hedged using futures contracts, spot markets may

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FIGURE 2Options on fed funds futures (put option volume and fed funds rate).

334 Sultan


6 Sultan


respond to innovations in the futures markets, and vice versa. Clifton (1985)notes that the listing of currency futures is associated with an increase in spotmarket volatility for major currencies. Edwards (1988) studies the impact of anintroduction of futures on stock index and finds that the listing of futures isassociated with a decline in the volatility of stock index. Bessembinder andSeguin (1992) find that the introduction of futures on stock index is associatedwith lower volatility in the equity market. Jochum and Kodres (1998) examinethe impact of the introduction of futures on Mexican peso and find that spotmarket volatility is lower following the introduction of futures.

In contrast, Koutmos and Tucker (1996) offer evidence that futures onS&P500 leads to higher volatility in the S&P500 spot market. Chatrath,Ramchander, and Song (1996) find an increase in the conditional volatility ofspot exchange rates for the British pound, the yen, Canadian dollar, Swissfranc, and the Deutsche mark following an introduction of futures on thesecurrencies. Gulen and Mayhew (2000) examine the effects of index futureslistings in 25 countries and find that the post-listing volatility is higher only inthe US and in Japan. Becketti and Roberts (1990) find that the stock marketvolatility did not increase due to futures trading. Ely (1991) finds that the list-ing of interest rate futures has no effect on the underlying markets. Overall, theevidence is mixed.

3.2. Effects of Options Listing

With regard to the options listing effects, there are three strands in the litera-ture. In the first strand, critics argue that options may act as substitutes for theunderlying security, and divert trading volume away from the primary marketinto the options market (Stein, 1987). As a result, the introduction of optionsreduces liquidity and increases volatility of the underlying market. Severalempirical studies on options introduction confirm such effects. For instance,Heer, Trede, and Wharenburg (1997) and Mayhew and Mihov (2000 and2004) find that the volatility of the underlying stocks increases after options onthe stocks were listed; however, Mayhew and Mihov (2000) suggest thatexchanges list options in anticipation of an increase in volatility.

The second strand of the literature emphasizes the informational efficien-cy of options. Proponents argue that the introduction of options makes the market more complete. This, in turn, improves liquidity and stabilizes theunderlying asset market.3 According to Grossman (1988), option prices revealinformation about the underlying markets. However, Stein (1987) claims thatif the information from the options market contaminates the information content

3See Ross (1976) and Grossman (1988). Also, Kumar, Sarin, and Shastri (1998) report that the adverseselection component of the bid-ask spread declines following option listing.





in the underlying market, then volatility may actually rise. Figlewski and Webb(1993) suggest that options may actually improve the welfare of traders whocould not conduct short sales earlier; thus, options trading improves bothtransactional and informational efficiency in the stock market. Diamond andVerrecchia (1987) also suggest that short selling restrictions can be circum-vented by trading options. As a result, their model predicts that options listingcan attract more informed trading and increase informational efficiency of theprimary market. Jennings and Stark (1986) suggest that options allow for aquick dissemination of earnings news in the market. This is also noted by Cao(1999) who shows that options listing leads to an increase in the amount ofinformation collected on the primary assets. Cao suggests that by aggregatinginformation, prices become more informative and they end up being higher,with lower volatility. Ross (1976) suggests that trading in options on equity canimprove pricing efficiency by eliminating noise.

The final strand of the literature offers empirical evidence on the effects ofoptions listings. It has been reported that compared to non-optioned stocks,post-listing volatility for optioned stocks is lower. Bansal, Pruitt, and Wei(1989) claim that option listing leads to a reduction in the total risk of firmswith options. Conrad (1989) finds that options listings for 97 firms are associ-ated with an increase in the stock price and a decrease in volatility. Detempleand Jorion (1990) document an increase in the stock price of the underlyingfirms’ as well as a decrease in volatility in response to options listing.Damodaran and Lim (1991) find a decrease in stock volatility following optionslisting. Shastri, Sultan, and Tandon (1996) find that the introduction ofoptions on major currencies has no effect on mean returns in the currencymarket (spot and futures) but volatility declines.

3.3. Shortcomings of Options Listing Studies

There are several potential shortcomings of the earlier empirical studies. Forexample, Bolen (1998) and Freund, McCann, and Webb (1994) claim thatsince options are introduced for stocks with high variance, options listing onthese stocks tend to reduce volatility. Using a controlled sample the authorsfind that a reduction in variance following options listing is also evident forstocks without options. So, it is not surprising that options listings lead tolower volatility of the optioned stocks. Another potential problem with optionslisting studies is that they do not control for market-wide decline in volatilityaround the time when options are listed. Krauss and Zimmerman (2002) findevidence that options listings on Swiss equities led to ambiguous effects on theprimary market. Similarly, Klemkovsky and Maness (1980) find ambiguouseffects of options listing on the beta and volatility of stocks. Freund, McCann,

336 Sultan


8 Sultan


and Webb (1994) show that options listings are associated with lower volatilityfor the first batch of options introductions. Subsequent options listings did notlead to lower volatility.

The design of the options listing dummy variable in previous studies maybe problematic. Typically, options listing studies specify a dummy variable thattakes a value of one on listing date and zero otherwise. However, such a narrowevent window does not allow market participants adequate time to fully com-prehend the importance and the information content of this new product fortrading and risk management. So, expanding the event window may help cap-ture the effects of options listing on the level and the volatility of interest ratespreviously undetected.

Another shortcoming of the existing empirical literature is that the exacttransmission mechanism through which options trading affects the underlyingmarkets is not fully specified. From a practitioner point of view, the effects ofoptions listing on the volatility of the primary markets depend on the demandfor liquidity in the primary markets to hedge options (see Pearson, Poteshman,and White, 2008). Such hedging activities may lead to positive or negativeprice changes in the underlying markets. This is known as the “feedback effect”(Kambhu, 1998). This particular link is discussed next to suggest a convenientmechanism to study the effects of options trading on the underlying markets.

4. EMPIRICAL ANALYSIS

4.1. Hypothesis

There is a specific avenue through which the information from options is trans-mitted to the underlying markets. For example, in the equity market, deltahedgers hedge their short options positions by buying and selling the underly-ing stocks, and, in order to be delta-neutral, the option writer must buy theunderlying stock when the price rises and sell the stock when the price falls.The option buyer’s behavior is exactly opposite. Since purchased options posi-tions have positive gammas, they are dynamically hedged by selling the stockwhen the price rises and buying the stock when the price falls. If the aggregategamma for those inclined to hedge is positive, then delta-hedging has thepotential to reduce the volatility in the primary market (Pearson, Poteshman,and White, 2008). The authors use daily data on all equity options traded onthe CBOE during 1990–2001 and find evidence of an increase in volatility dueto options trading.

Similarly, an analysis of how interest rate options dealers hedge their inter-est rate risk can also provide insights into the link between the fed funds optionsand the underlying markets. First, dealers calculate the delta of their portfolio,





which contains written options, and then add an offsetting position in the under-lying market to make the aggregate portfolio delta equal to zero. If a dealer hassold a call on fed funds futures, which has a negative delta, an increase in the futures price (decrease in interest rate) will push the call into money. To pro-tect the position, the dealer buys futures when the price rises and sells futureswhen the price falls. In this framework, changes in the demand for the underly-ing instruments may lead to changes in their prices. According to Kambhu(1998), the feedback mechanism suggests that “dynamic hedging would have thepotential to amplify the volatility of asset prices when prices fall abruptly.”

In light of the preceding discussion, the central hypothesis in this study isthat the introduction of CME fed funds options increases the amount of infor-mation market participants can use to forecast monetary policy. As a result, itis expected that information aggregation across options contracts would leadto reduced volatility in the underlying markets, i.e., there is a resolution ofuncertainty about future monetary policy stance. Furthermore, options listingmay also affect other short-term interest rates such as the Eurodollar and theLibor. These markets are closely linked to innovations in the fed funds mar-kets for a variety of reasons including inter-market arbitrage, spread trading,and hedging.

4.2. Summary Statistics

Daily data on all spot interest rates and interest rate futures contracts (nearby)for the period January 1, 1990 to May 29, 2009 are obtained from Datastream.In Table I, diagnostic statistics are reported. The daily spot rates are simpleyield (%) to maturity. The yield (%) on futures contracts are simple add on yield(100-futures price). Descriptive statistics on raw data are presented in Panel Aof Table I. Both the mean and the standard deviation of the interest rates aresimilar, with the fed funds spot rate being the most volatile. Some of thedescriptive statistics for options trading volume have been discussed earlier.Skewness, kurtosis, and Jarque-Bera values all indicate the presence of non-normality in the data.

The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is utilized fordetecting unit roots (with a trend and intercept). The choice of the KPSS teststems from the fact that the other tests, including the Dickey–Fuller,Augmented Dickey–Fuller, and Phillips–Perron, may suffer from low power ofthe test (see Kwiatkowski et al., 1992). The results reported in Panel A showthat all interest rates (including the TED spread), option volume (put � call),futures yields, and the target rate for the fed funds for the sample are nonsta-tionary in the levels and require first differencing to induce stationarity. In eachcase, the KPSS statistic exceeds the critical value of 0.216 at the 1% level.

338 Sultan


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3.01



Next, the Johansen’s test is applied to detect cointegration4 between thespot and futures rates for the fed funds, the Eurodollar, and the Libor. There isevidence that the spot and futures interest rates are cointegrated with a cointe-grating vector (1, �1). Cointegration implies that any regression model of thespot or the futures interest rate needs to include the error correction term(lagged basis) as a predictor.

In Panel B, distributional statistics such as skewness, excess kurtosis,Jarque–Bera, Ljung–Box, and Engle’s TR2 (LM) statistics are reported.Skewness suggests that centrality in distribution is lacking. Kurtosis valuesindicate that the distribution is either leptokurtic (excess kurtosis), suggestingthat the distribution for these variables have acute peaks around the means, orplatykurtic, with wider peaks around the means. The Jarque–Bera statistic, atest of goodness of fit based upon skewness and kurtosis, confirms that thesevariables are not normally distributed. The test Ljung–Box (36 lags) confirmsserial correlation in first-differenced variables. Similarly, Engle’s LM, which isa test of 5th order ARCH effects in the first differenced series, suggest thatthese variables have time varying variances; thus ordinary least squares estima-tion of the regression equations may not provide a parsimonious representationof the distribution of the variables.

4.3. Empirical Model

The GARCH error correction model in this study is similar to Shastri, Tandon,and Sultan (1996). This particular approach makes several contributions to theempirical literature. First, the joint distribution of the spot and the futures mar-kets is specified with the error correction mechanism acting as the anchor thatties both the level of interest rates and the volatility together. Second, themodel makes it possible to account for the effects of information that are dis-tributed across markets, allowing the level and the volatility in each market toindividually respond to the information content, but not independently of oneanother. Finally, the model captures the joint distribution of the spot andfutures rates, which may be non-normal with time varying volatility and volatil-ity clustering. The model is:

4Brenner and Kroner (1995) suggest that cointegrated variables are linked to one another in a fundamentalway such that in the short run they may deviate from each other but in the long run they are held together insteady-state equilibrium. In the futures markets, the cost-of-carry theory suggests that cash and futuresprices are related such that arbitrage activity will force them to move within the no-arbitrage band.Intuitively, the coefficient measures the degree to which each market is responsible for restoring the arbi-trage-free equilibrium.

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(1)

(2)

(3)

where ct�1 is the information set at time t � 1. Equation (1) describes themean equations, Equation (2) specifies the joint distribution of the error termsin the mean equations, and Equation (3) defines the full functional form of thevariance and covariance of the error terms. Exogenous variables (definedbelow) that appear in the conditional mean equations are also included in theconditional variance–covariance matrix. The objective is to examine whetherthese variables contribute to the time varying variances and volatility clusteringin these interest rates.

Equation (3) models the conditional variance as GARCH(p, q) processwhere p and q denote the lag length. tij are the intercept terms, aij are theARCH terms and bij are the GARCH terms. Notice that the exogenous vari-ables are also included in the conditional variance equation to examine theireffects on interest rate volatility.

Among the exogenous variables, ECMt (defined as Ft�1 – St�1) is the errorcorrection term that accounts for the cointegration between the spot andfutures rates. This presumes that the cointegrating vector is (1, �1). The spe-cific form of this cointegrating vector follows from the notion that the spot and

� £

b11 b12 b13

b21 b22 b23

b31 b32 b33

§ £

hst�1

hsft�1

hft�1

§ � f(Exogenous Variables)

Ht � £

hst

hsft

hft

§ � £

tS

tSf

tf

§ � £

a11 a21 a13

a12 a22 a23

a31 a32 a33

§ £

e2st�1

est�1eft�1

e2ft�1

§

ces

efd ƒ ct�1 � N(0, Ht)

� b9PMT � b10LT � b11OPT � b12VOL � eft

¢Ft � b0 � b1ECMt � b2TARGET � b3TED � a7

i�4biDW � b8MKT

� a9PMT � a10LT � a11OPT � a12VOL � est

¢St �a0 � a1ECMt �a2TARGET � a3TED � a7

i�4aiDW � a8MKT





the futures rates are tightly linked through monetary policy as well as institu-tional structure.5

The remaining exogenous variables are: TARGET (changes in the Fed’starget rate), TED (TED spread), DW (day-of-the-week dummy variables), MKT(market open following 9/11 attacks), PMT (payment disruption due to 9/11attacks), LT (long-term effect of the 9/11 attacks), OPT (listing dummy), andVOL (total options volume). The objective of including these exogenous variables is to account for the variability in key interest rates due to macroeco-nomic shocks, important global economic shocks, as well as the regulatoryenvironment. These variables are now discussed.

Target Rate: The Fed’s target rate (TARGET) is an important determinantof the interest rates because historically the Fed has successfully used openmarket operations to achieve a desired target rate for the fed funds market(Carlson, Craig, and Melick, 2005). However, these desired changes in the tar-get rate reveal macroeconomic conditions which influence the Fed’s decisionsto alter the rate. Therefore, TARGET is a good proxy of key macroeconomicvariables that are omitted from the model.

TED Spread: The sample period includes several economic shocksincluding the Russian debt crisis, the Latin American and Asian currencycrises during 1994–1997, the 1997 stock market crash, the dot-com bub-ble of 1995–2000, the 9/11 terrorist attacks, and the recent financial crisisthat started in the summer of 2007 and lasted until February 2009. Thefinancial markets worldwide have responded to these events according to the information content of these events and how various monetary author-ities have reacted to restore confidence in the financial system. For exam-ple, the recent financial crisis raised the overall uncertainty in the marketand much of it was reflected in the level and volatility of key interest rates.The result of this systemic failure quickly spread to other sectors of theeconomy, pulling down financial markets. Financial institutions reacted toincreased foreclosures and defaults by tightening lending standards,despite liquidity injections by the Fed. As a result, the Treasury-Eurodollarspread (the TED6 spread), which is an indicator of credit risk in the econ-omy, rose to a record level. While a complete analysis of the effects ofthese events is beyond the scope of the study, it is believed that the TEDspread should be able to account for the interest rate volatility during theentire sample period.

5For example, in the stock market, the stock index and futures on the stock index would be related throughindex arbitrage (see Stoll and Whaley, 1990).6As a popular indicator for liquidity in the credit market, the TED spread should capture the sentiment in thefinancial market during tranquil and turbulent times. I thank an anonymous referee for pointing this out.

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Since the sample period includes the 9/11 terrorist attacks, it is importantto account for the cost7 of reconstruction, disruption of the infrastructure, andthe psychological effects on investor sentiment due to the 9/11 events. All thesefactors have had some impact on the level and the volatility of key interestrates. For example, in the aftermath of the attacks, the Fed and several key cen-tral banks around the world implemented important policy measures toimprove liquidity in the financial markets and help restore confidence in theglobal financial network.

While a detailed analysis of the effects of these terrorist acts on interestrates is beyond the scope of this study, several dummy variables are included toaccount for the disruption of payments systems among banks to process incom-ing and outgoing fund transfers, subsequent liquidity injections by the Fed, thereopening of the financial markets, and the long-term effects on liquidity.8

Market Reopening: The first 9/11 related dummy variable (MKT) takes avalue of 1 when the markets reopened on 9/17/2001, and 0 otherwise. OnSeptember 17, after 4 days of closure, the markets reopened and the Dow fell684 points to 8,920 (a 7.1% drop), one of the largest declines in history. Thisdownward spiral continued until the end of the week, and by then the totaldecline was 1,369.1 points or a 14.3% drop. This unusual drop reflectedheightened concerns for the economy, a failure of the communication systems,and overall pessimism among market participants.

Payments Imbalance: The second 9/11 related dummy variable (PMT)represents loss of liquidity and payments delays due to the 9/11 attacks thatinflicted significant damage to the regular flow of large-value payments amongbanks through the Fedwire system (an electronic payment settlement systemused by banks). The infrastructure housing electronic communications systemsin Lower Manhattan was damaged, which resulted in a drastic fall in the vol-ume of Fedwire funds transfer (McAndrews & Potter, 2002). According to theNew York Fed, the value of Fedwire transfer was $1.59 trillion on September10, representing 436,312 transfers, and the aggregate balances of the banking sys-tem were $13 billion. On September 11, the value of Fedwire transfers decreasedto $1.2 trillion and the volume had fallen to 249,472 transfers. By September 12,

7According to Bruck and Wickstrom (2004), direct costs arise from loss of lives, destruction of property,search and rescue effects, rebuilding of the infrastructure, restoring the quality of life through governmentassistance, and improved security systems to prevent terrorist attacks. The OECD estimates of the directcosts associated with the September 11 attack in the US are as follows: $14 billion (private sector), $1.5 bil-lion (state and local government), $700 million (Federal government), and $11 billion (private and publicsector costs for search and rescue operations). See Lenain, Bonturi, and Koen (2002). Indirect costs of ter-rorist attacks are more difficult to measure. According to the IMF, indirect costs of terrorism are psychologi-cal, resulting from pessimism among consumers and investors. This has the potential to depress asset pricesand promote flight to quality (IMF, 2001). In addition, longer-term costs of the terrorist attacks would reflecta loss of productivity, additional costs of developing improved security systems, and increased cost of doingbusiness in general (Bruck and Wickstrom, 2004).8I thank an anonymous referee for suggesting this.





both the value of transfers and volume increased. Between September 13 and17, the Fed injected liquidity in the banking system through open market oper-ations.9 However, the speed at which banks processed incoming receipts andoutgoing payments fell sharply, suggesting a major disruption to the paymentssystem (McAndrews & Potter, 2002). According to Fleming and Garbade(2002), by September 19, aggregate balances among banks increased to over$190 billion, suggesting long payment delays (settlement “fails”) (also seeLacker, 2003). Reflecting this payment disruption is the dummy variable(PMT) which takes a value of 1 during the period 9/11/2001 to 9/21/2001, and0 otherwise.

Long-term Effects: The next dummy variable represents the long-termeffects of the 9/11 attacks. Since the economic and psychological effects of9/11 terrorist attacks would be felt for some time, a period of six months maybe adequate for the market to return to some level of normalcy. As a result, thethird dummy variable (LT) takes a value of 1 everyday for the next 6 monthsfollowing the 9/11 attacks and 0 otherwise.10

Day-of-the-Week Effects: The Fed requires periodic settlement (currently,every other Wednesday) of deficit positions of commercial banks’ reserves withthe Fed. Deficit banks receive a penalty and are forced to borrow the reservesfrom the Fed. Several studies exist that concentrate on empirical regularitiessuch as the day-of-the-week effects in the level and daily variability of the fedfunds rate (for example, see Cyree and Winters, 2001 and references therein).For instance, Spindt and Hoffmeister (1988) analyze the effects of institution-al regularities such as the reserve maintenance and reserve settlement featureon the fed funds market. Using data for the period 1984–1986, they show thatthe daily variance of the fed funds rate increases towards the end of daybecause of end-of-the-day accounting of closing balances; and that the vari-ance is highest on settlement Wednesdays. See, for example, Spindt andHoffmeister (1988), Griffith and Winters (1995), and Hamilton (1996).Therefore, day-of-the-week dummy variables are included to account for theseempirical regularities.11

Options Listing: The final dummy variable is OPT (options listing dummyvariable). The listing dummy is set equal to 1 during 3/14/2003 to 3/21/2003(listing date and 1 week post-listing), and 0 otherwise. The construction of thedummy variable is in contrast to most options listing studies.12 The rationale

9According to Coleman (2002), immediately following the 9/11 attacks, the Fed increased liquidity by morethan $100 billion through open market operations and the discount window.10I thank an anonymous referee for suggesting this.11Ashcraft and Duffie (2007) use transactions data from the Fedwire to study market microstructure of trad-ing on fed funds market.12Typically, option listing studies specify a dummy variable that takes a value of 1 on listing date and zero oth-erwise.

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for specifying a longer window is that it takes at least some time to becomeaware of the introduction of options and respond to this innovation. Implicit inthis approach is the notion that the introduction of options would be associat-ed with an impact (shift in the intercept) on the conditional mean and the con-ditional volatility. The design of the dummy variable in this manner would thuscapture the average effects during the listing week.

Finally, daily options volume is also included as an exogenous variable.13

The inclusion of the volume accounts for the fact that OPT may not accountfor the dynamic effects of options trading on the level and the volatility of theinterest rates. In particular, with the passage of time, does options trading con-tribute to the volatility in the primary markets? Furthermore, the results basedupon volume may be consistent with a notion that volume is a conveyor ofinformation, and as such, may offer more direct evidence on the effects of options trading. Therefore, I include VOL, which measures daily volume ofoptions on fed funds futures (call volume the put volume).14 Because of dataunavailability,15 the volume series begins on 12/1/2003.

As noted earlier, the rationale for including volume, especially in the con-ditional variance equations, is consistent with the literature on the relationshipbetween volume and volatility. It is important to note that volume and volatilitycan be endogenous (see Karpoff, 1987 and references therein), and as such,including volume as an exogenous determinant of volatility may be trouble-some. For example, Clark (1973) suggests that increases in the rate of informa-tion flow simultaneously lead to increases in volatility and volume. However,several authors also note that volume can be a proxy for information and thusis a reasonable determinant of price volatility (see Lamoureux and Lastrapes,1990). In this study, consistent with the “mixture of distributions hypothesis”with volume as the mixing variable, cash and futures interest rate changes aremodeled as subordinate processes in which the rate of information flow acts asthe directing variable (see Clark, 1973; Epps & Epps, 1976; Tauchen & Pitts,1983). As Lamoureux and Lastrapes (1990) suggest, since the rate of informa-tion arrival is not observable, volume is used as a reasonable proxy.

A priori, volume may be positively related to volatility. Admati andPfleiderer (1988) and Kyle (1985) assume a noisy rational expectations equilib-rium framework and predict a positive relationship between trading volume andprice volatility. So, if options on fed funds futures convey noisy information, volume may be positively related to volatility. In contrast, if trading volume reflects

13There is a voluminous literature on the role of volume as a conveyor of information. I will discuss thisshortly.14I thank an anonymous referee for suggesting this.15The volume series prior to 12/1/2003 is missing from the database provided by the CME. This could also bereflecting the fact that trading did not pick up until late 2003. To avoid losing observations, on few occasions,zero volume days are replaced with a value of 1 for call and 1 for put options.





resolution of uncertainty, volume may be negatively related to the volatility.Also, if trading on derivatives allows for an efficient risk transfer among marketparticipants, volume may be negatively linked to volatility.

Convergence of the above model is problematic because of an abundanceof parameters. For instance, excluding the exogenous variables, there are 21parameters: three parameters in the unconditional variances/covariance matrix(tij), nine parameters for the ARCH matrix (aij), and nine parameters for theGARCH matrix (bij). In addition, each exogenous variable adds another threeparameters in the conditional covariance matrix, and given that there are 12exogenous variables, this translates to 36 parameters.16 Now, the Ht matrix has57 parameters. Therefore, a diagonal specification of the Ht matrix is assumed,which economizes on the parameters in the conditional variance/covariancematrix. By setting all off-diagonal elements to zero and assuming a zero covari-ance term, there are now six parameters for the conditional covariance matrix.In addition, given that there are 24 parameters associated with 12 exogenousvariables, the conditional variance matrix now has 30 parameters. This specifi-cation assumes that the exogenous variables do not affect the conditionalcovariance term, an assumption that is needed to avoid convergence problems.The model is estimated using the Marquardt maximum likelihood function withBollerslev and Wooldridge (1992) robust standard errors that improve the qual-ity of statistical inferences based upon t-statistics.

4.4. Regression Results

In Table II, the regression results are reported. The columns listed under FedFunds report the results for the fed funds spot and futures rates, columns list-ed under Eurodollar report the results for the three-month Eurodollar spot andfutures rates, while columns listed under Libor report the estimates for theone-month Libor spot and futures rates.

4.4.1. Effects of options listing on the interest rate level

In Panel A of Table II, the error correction term (ECM) is negative and signifi-cant in the spot and futures equations for all markets except in the case of the fed funds futures. This may suggest that a deviation from a fundamentalrelationship forces the spot and the futures rates to change in the same direc-tion to restore the equilibrium. The ECM, which is essentially the lagged basisterm, has a large coefficient of almost 39 basis points in the fed funds spotequation, suggesting that the bulk of the adjustments falls on the spot market.

16It is important to note that multicollinearity is not a problem in the model. The highest correlation is 0.35between PMT and MKT. The remaining correlations are below 0.06.

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TABLE II

Multivariate GARCH Error Collection Model

Fed Funds Eurodollar Libor

Coefficients Spot Futures Spot Futures Spot Futures

Panel A: Conditional Mean Equations

Intercept �0.0503 0.0012 �0.0056 �0.0057 �0.0002 �0.0049(�4.6230)c (0.1378) (�2.2242)b (�2.0507)b (�0.0786) (�2.3354)b

ECM �0.3943 �0.0028 �0.0489 �0.0192 �0.0812 �0.0161(�24.8448)c (�1.5480) (�18.8100)c (�5.5896)c (�32.2438)c (�3.1415)c

TARGET 0.1275 0.0045 0.1024 0.1549 0.0444 0.1752�1.4516 (2.5564)b (10.0325)c (11.3081)c (1.9589)a (17.5936)c

TED �0.0896 0.2043 0.2292 �0.0980 0.2047 �0.0202(�1.6937)a (21.8352)c (27.1875)c (�7.8483)c (16.9754)c (�2.0943)b

Monday 0.1047 �0.1001 0.0077 0.0070 0.0006 0.0093(6.9958)c (�12.3529)c (2.6138)c (1.9397)a �0.1124 (3.3507)c

Tuesday 0.0249 0.0056 0.0059 0.0007 0.0006 0.0036(1.7943)a (2.3026)b (1.9447)a (0.2154) �0.1200 (1.1737)

Wednesday 0.0918 0.0027 �0.0026 0.0042 �0.0028 0.0035(6.6558)c �1.0782 (�0.8513) (1.0262) (�0.5284) (0.8980)

Thursday 0.0670 0.0028 0.0005 0.0051 �0.0048 0.0035(4.5712)c �1.0447 �0.1665 (1.2496) (�0.9501) (0.9774)

MKT �1.0527 0.0017 �0.3070 �0.3440 0.0279 �0.3455(�0.0009) �0.6307 (�0.0020) (�0.0023) (0.0000) (�0.0003)

PMT �0.1854 �0.3508 �0.1052 �0.0565 �0.1557 �0.0469(�0.2465) (�0.0013) (�0.2241) (�0.0496) (�0.3498) (�0.4924)

LT 0.0314 �0.0381 �0.0016 �0.0063 0.0018 �0.0038(2.5746)b (�0.1699) (�0.4540) (�1.3565) �0.3176 (�0.5110)

OPT 0.0140 �0.0059 0.0046 0.0357 0.0090 0.0121�0.1037 (�0.8601) �0.5216 �1.3851 (0.0976) �0.2324

VOL 0.0012 �0.0003 0.0023 �0.0002 �0.0000 �0.0010�0.1378 (�0.2751) �1.6054 (�0.1355) (�0.0187) (�0.7195)

Panel B: Conditional Variance Equations

Intercept 0.0240 0.0008 0.0014 0.0017 0.0016 0.0022(13.1195)c (10.0756)c (10.1541)c (17.8409)c (12.4860)c (16.3430)c

ARCH 0.3916 0.2974 0.5310 0.3318 0.4026 0.4302(34.5856)c (18.6367)c (37.0375)c (34.1339)c (26.0965)c (33.8548)c

GARCH 0.7357 0.7515 0.7278 0.7431 0.7632 0.6022(50.3488)c (32.9242)c (81.2212)c (52.4603)c (49.1092)c (28.9577)c

ECM �0.0024 0.0000 0.0005 0.0002 �0.0000 �0.0000(�1.5051) (�0.7896) (9.8488)c (1.9776)b (�0.3566) (�0.2088)

TARGET �0.0104 �0.0003 �0.0013 �0.0019 �0.0018 �0.0002(�0.9467) (�2.0254)b (�7.1448)c (�2.9741)c (�2.5056)b (�0.7527)

TED 0.0069 0.0000 0.0005 0.0010 �0.0001 0.0000(�0.9089) (�0.4800) (4.5558)c (2.4264)b (�0.2746) (�0.2191)

Monday �0.0026 0.0000 �0.0009 �0.0004 �0.0001 0.0000(�1.1541) (�1.0645) (�4.7354)c (�4.0282)c (�1.1029) (0.1540)

Tuesday �0.0070 0.0000 �0.0007 �0.0002 �0.0002 �0.0000(�3.2439)c (�0.1766) (�4.2958)c (�2.0080)b (�1.0118) (�0.0874)

Wednesday �0.0013 �0.0002 �0.0005 0.0000 0.0000 �0.0000(�0.7000) (�3.8297)c (�3.4981)c (�0.7344) (�0.1644) (�0.7279)

(Continued)





For the remaining markets, the magnitude of the impact ranges between two toeight basis points, with the spot markets showing higher sensitivity than thefutures markets. The lack of a contrasting effect between the spot and futuresmarket indicates that a deviation from the equilibrium prompts both the spotand the futures to change in the same direction, thus reducing the potential forconducting arbitrage between spot and futures contracts.

Next, in five out of six cases, TARGET has a positive and significant (at the5% level) effect on the level of the interest rates. The size of the coefficient

TABLE II (Continued)

Fed Funds Eurodollar Libor

Coefficients Spot Futures Spot Futures Spot Futures

Thursday �0.0138 �0.0001 �0.0005 0.0000 �0.0011 �0.0005(�6.7697)c (�1.4651) (�3.2264)c (�0.6840) (�7.3380)c (�2.8840)c

MKT 0.0151 �0.0019 �0.0013 �0.0011 0.0018 �0.0022(0.0018) (�0.0118) (�0.0004) (�0.0009) (0.0003) (�0.0001)

PMT 0.0074 0.0005 �0.0006 �0.0003 0.0006 0.0004(0.0441) �0.0352 (�0.0205) (�0.0106) (0.0095) (0.0044)

LT �0.0152 �0.0001 �0.0005 �0.0009 �0.0009 �0.0008(�14.5960)c (�2.1609)b (�7.3850)c (�9.1152)c (�9.4756)c (�3.6078)c

OPT �0.0168 �0.0007 �0.0010 �0.0015 �0.0012 �0.0014(�11.6793)c (�6.0385)c (�15.2942)c (�2.3984)b (�28.6012)c (�1.5769)

VOL �0.0032 0.0000 �0.0002 0.0003 0.0000 0.0000(�2.0253)b (2.1788)b (�8.1500)c (22.6304)c �0.0088 �0.1290

In this table, the multivariate GARCH error correction model is of the following kind:

Mean equations

Conditional Variance Equations

Note. Results from multivariate GARCH models are shown. t-statistics (in parentheses) are based upon Bollerslev–Wooldridge(1992) robust standard errors. All dependent variables, otherwise noted, are in first difference. Significance levels are indicated as fol-lows: a (10%), b (5%), and c (1%). Among the independent variables, ECM (St�1 � Ft�1) is the error correction term which accountsfor cointegration between the spot and futures rates in each market. TARGET refers to the fed funds target interest rate. TED ischanges in the Treasury–Eurodollar futures spread. Day-of-the-week dummy variables (DW) are binary (1, 0). MKT refers to the post9/11 opening of the equity market on September 17, 2001. PMT refers to the failure of flow of funds of New York Clearing Housebanks and other settlement agencies due to technological and communication breakdown due to the 9/11 attacks. LT measures theeffects of 9/11 on interest rates. The variable takes a value of 1 between 9/11/2001–3/13/2002, and 0 otherwise. OPT, the options list-ing dummy variable, takes a value of 1 during 3/14/2003 to 3/21/2003, and 0 otherwise. Volume is the sum of put and call volumes.Position of the coefficients of independent variables in the variance–covariance matrix is indicated by the subscripts: s (spot) and f (futures). This model assumes all off-diagonal elements of the A and B matrices are zero, which is convenient for convergence ofthe model.

Ht � C �0C0 � ca11 a12

a21 a22d�ce2

1,t�1 e1,t�1,e2,t�1

e1,t�1,e2,t�1 e22,t�1d c

a11 a12

a21 a22d � c

b11 b12

b21 b22d�Ht�1 c

b11 b12

b21 b22d � f (Exogenous Variables)

� b11OPT � b12VOL � eft¢Ft � b0 � b1ECMt � b2TARGET � b3TED � a7

i�4biDW � b8MKT � b9PMT � b10LT

�a11OPT � a12VOL � est¢St � a0 � a1ECMt � a2TARGET � a3TED � a7

i�4aiDW � a8MKT � a9PMT � a10LT

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varies from a low of one (fed funds futures) to a high of 18 basis points (Liborfutures). A positive coefficient suggests that these interest rates follow innova-tions in the Fed’s target rate for the fed funds market. It is surprising to observethat the effects of changes in TARGET are not significant for the fed fundsspot market given that innovations in this market are expected to closely trackchanges in the Fed’s target rate.

The effects of the TED spread are mixed across the six interest rates. Theestimated coefficient is negative and significant for the fed funds spot andfutures on the Eurodollar and Libor. The effects are positive and significantfor the remaining interest rates. As noted earlier, a priori, an increase in theTED spread reflects a rising interest rate environment and a worsening creditrisk in the market. However, a rising interest rate environment could alsoprompt futures market participants to expect expansionary monetary policy inthe near future. As a result, it is not surprising that the TED spread has a negative sign for its coefficient in the Eurodollar and the Libor futuresrates.

The day-of-the-week dummy variables account for some of the stylizedfeatures that have been documented in earlier studies (see Hamilton, 1996).The results show that the Monday dummy is significant and positive for the fedfunds spot, the Eurodollar spot and futures, and the Libor futures rates. TheMonday dummy is also significant and negative for the fed funds futures rate.For the remaining markets, the effects are insignificant. The Tuesday dummy ispositive and significant in three out of six cases. The Wednesday dummy is sig-nificant only in the case of the fed funds spot, perhaps reflecting the notionthat an increased demand for liquidity on Wednesdays (as reserve settlementstake place among the banks) pushes the spot rate higher. The Thursday dummyis significant and positive only in the case of the fed funds spot rate andinsignificant for the remaining markets. Overall, a strong generalization of theday-of-the-week effects on the level of interest rates is difficult.17

With regard to the dummy variable MKT (market reopening after 9/11attacks), it is assumed that the interest rates may decline in response to post9/11 liquidity injections by the Fed (Coleman, 2002). In fact, on September 17,the FOMC lowered the TARGET rate for the fed funds by 50 basis points to3%. The results show that MKT is insignificant across all markets. With respectto PMT, which captures system-wide payments disruptions, the variable hasinsignificant coefficients across all markets, suggesting that a decreaseddemand for wire transfers immediately after the 9/11 attacks did not lead to higher interest rates. Recall that PMT takes a value of 1 during 9/11/2001 to

17Finally, there are some indications that the interest rates are sensitive to Fridays. As the intercept term indi-cates, several interest rates are slightly lower on Fridays.





9/21/2001, a period which also coincides with liquidity injections by the Fed.Therefore, the evidence of a negative coefficient may be accounting for anincrease in liquidity during this period.

The six-month effect of the 9/11 attacks (LT) is largely absent with onlyexception being the fed funds spot rate, which ends up 3.1 basis points higherpost 9/11. This could be indicative of market perceptions of increased econom-ic uncertainty, thus resulting in higher interest rate. For the remaining interestrates, a lack of sensitivity to LT may be indicative of the resilience of the USfinancial markets to terrorism.

The effects of options introduction (OPT) on the conditional mean ofthese six interest rates are illustrated in Panel A. Recall that, OPT is a dummyvariable taking a value of 1 during 3/14/2003 to 3/21/2003 (which includes theoption listing date) and 0 otherwise. The results suggest that OPT is insignifi-cant across all markets. In five out of six cases, the variable has positive coeffi-cients, implying that options introduction in the fed funds market is associatedwith an increase in the demand for liquidity in key interest rate markets.However, a low significance of the estimated coefficients prohibits drawingrobust conclusions on the effects of options introduction on the level of thesekey interest rates. There are several plausible explanations for this lack of a sig-nificant relationship between options listing and the primary markets.Obviously, the small size of the fed funds options market makes it quite unlikelythat changes in the demand for liquidity in response to options trading exert anoticeable impact on the level of interest rates. Another possible explanation isthat the listing window is too short and does not account for an increase infamiliarity and usage of options for trading and risk management. It is possiblethat with the passage of time, the growth of the market and increased familiar-ity with options, the information content of the options market may affect thelevel of the interest rates, although the effect is still expected to be quite small.

Finally, the results of trading on options on fed funds futures contracts arereported in Table II. As can be seen, the variable VOL is insignificant across allmarkets, suggesting that options volume is not a statistically significant deter-minant of the level of the interest rates. This should not come as a surprisebecause compared to the spot and futures markets, the fed funds options mar-ket is quite small. Therefore, it is unlikely that the demand for liquidity fromoptions trading would have much effect on the level of the interest rates.

4.4.2. Effects of options listing on interest rate volatility

In Panel B of Table II, the columns labeled as Spot report estimates for theconditional variance of the spot rate, and the columns labeled as Futures report

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the estimates for the conditional variance of the futures rates. First, the uncon-ditional variance (intercept) terms are positive and statistically significant foreach market, suggesting that the baseline volatility, although small, is positivefor each interest rate. Next, the GARCH modeling of the pairs of interest rates(spot and futures) seems to fit the data well. For each market, the ARCH andGARCH parameters are positive and statistically significant determinants ofthe time varying volatility. The results clearly indicate that the bivariateGARCH model appears to provide a parsimonious presentation of the joint dis-tribution of the spot and futures rates.18

With respect to the effects of the exogenous variables, the ECM term(lagged basis term) has a positive and significant effect on the conditionalvolatility of the Eurodollar spot and futures markets. A positive sign for theECM reflects the fact that as the Eurodollar markets deviate from the equilib-rium, there is more uncertainty in the Eurodollar rates as spot and futures ratesadjust in the same direction to restore equilibrium. It is difficult to rule out thepossibility that such unidirectional adjustments may be perceived riskier, thusleading to higher volatility in the market. For the remaining markets, anincrease in the basis has no effect on the volatility of the interest rates.

Among the remaining exogenous variables, TARGET has a negative effecton the conditional volatility of the fed funds futures, the Eurodollar spot andfutures rates, and the Libor spot rate, which can be interpreted as a result of aresolution of uncertainty regarding monetary policy. For the remaining interestrates, the coefficients are insignificant. With respect to the effects of changesin the TED spread, the variable is positive and significant in the case of theEurodollar spot and futures, indicating higher uncertainty when the overallcredit risk rises. The reason why the TED spread is insignificant in most casesis perhaps due to the fact that its effects are already captured by other indica-tors such as changes in TARGET. However, this does not imply that the TEDspread should be dropped from the model. While TARGET is a good indicatorof the US monetary policy, the TED spread is a proxy for measuring the effectsof some of the significant global economic shocks including the most recentfinancial crisis.

With respect to the day–of-the-week dummy variables, the effects are notuniform. The Monday dummy is negative and significant in two out of sixcases. In three out of six cases, there is evidence that interest rate volatility islower on Tuesdays. The Wednesday dummy is negative and significant only in

18The sum of the ARCH and GARCH terms clearly exceed one for all the models, indicating that volatilityprocess has persistence. However, this could be a consequence of not estimating the off-diagonal elements(covariance terms). If the coefficients of the conditional covariance terms are negative, it would certainly off-set some of the persistence detected here.





the conditional volatility equations for the fed funds futures and the Eurodollarspot rates. Finally, the Thursday dummy is statistically significant and negativein the conditional volatility equations in four out of six cases. As discussed ear-lier, the day-of-the-week dummy variables are expected to account for institu-tional features in the market for fed funds. While the Wednesday dummy isexpected to account for the reserve settlement features, the remaining dummyvariables may simply be accounting for empirical regularities.

With respect to the 9/11 attacks, MKT (market reopening after 9/11attacks) is insignificant for all interest rates volatility equations. Similarly, itappears that payment (PMT) system imbalance also did not have a significantimpact on the interest rate volatility. In contrast, LT is significant and negativein six out of six cases, indicating that the conditional volatility of these keyinterest rates actually declined post 9/11 attacks. Again, this can be indicativeof the resilience of the US financial market in response to the terrorist attacks.It is also possible that this is a reflection on the effects of the response of theFed to keep the financial market volatility in check. A negative sign may also beindicative of a resolution of uncertainty in the market, suggesting a reducedlikelihood of further terrorist attacks immediately following the 9/11 attacks.

The effects of options listing (OPT) on the volatility of these six interestrates are also reported in Panel B. The results suggest that OPT has a negativeand significant coefficient in five out of six cases (Libor futures is the onlyexception), implying that options listing is associated with a decrease in volatil-ity. This is a key finding as it suggests that options listing leads to a moreinformed participation in the market and helps in an efficient allocation of riskamong market participants. These results are consistent with Grossman(1988). As risk allocation and price discovery improves, the underlying marketsbecome more stable.

Finally, the effects of trading of options on the fed funds futures are also dis-played in Panel B. While OPT may be capturing a one-time effect of options list-ing during the listing week, the inclusion of VOL captures the dynamic effect ofoptions trading. The results suggest that both the fed funds spot and theEurodollar spot markets respond negatively to the trading of options on fed fundsfutures. For a one percent increase in trading volume, the conditional volatility ofthe fed funds and the Eurodollar spot rates declines by less than a basis point. Incontrast, we find that the effect of volume on the conditional volatility of the fedfunds futures and the Eurodollar futures is positive and significant.

Overall, it is difficult to generalize these results. A negative effect of vol-ume on volatility is consistent with the hypothesis that derivatives are neces-sary for stabilizing the primary markets. However, the positive effect may beindicating that a significant amount of risk from the options markets is transferred

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to the futures markets. This may also be an indication of the fact that optionson fed funds futures may be hedged by trading futures and as such, demandfor liquidity in the futures markets may lead to higher volatility in the market.It could also mean that options volume is a noisy conveyor of information inthe market and as such does not lead to a resolution of uncertainty in the mar-ket. Finally, as discussed earlier, the positive feedback effect suggests that asoptions are hedged in the futures markets, it tends to amplify volatility in thefutures instruments as dealers buy futures when prices rise and sell futureswhen prices fall. Overall, both OPT and VOL may be indicating that optionslisting is important for stabilizing some of the key interest rates, althoughstrong generalizations across spot and futures markets cannot be made.19

The estimated conditional variances of the spot and futures are plotted. InFigure 3A, the conditional variances are very similar for the fed funds markets.The results indicate that there is a substantial time variation in conditionalvolatilities, although the post-options introduction period is associated withlower volatility. As expected, the conditional volatility rises during the credit cri-sis period. Similar results hold for the Eurodollar (Figure 3B) and the Liborrates (Figure 3C).

4.4.3 Robustness checks

It is possible that these results may be sensitive to the manner in which OPT isdefined. Therefore, a second set of GARCH model is estimated by redefining theOPT dummy which now takes a value of 1 on the listing date and 0 otherwise.The results, though not reported, can be summarized as follows. The options list-ing dummy is insignificant in the conditional mean and volatility equationsacross all markets. The volume of options trading is negative in the conditionalvariance of the fed funds spot and positive in the conditional variance of the fedfunds futures. For the remaining cases, VOL is not statistically significant.

As discussed earlier, this alternative specification for OPT is problematicbecause it assumes that options introduction would be associated with animpact only on the listing day. It also assumes a narrow event window, whichdoes not allow market participants adequate time to become familiar with this

19As noted earlier, there may be simultaneity between volume and volatility. To address this issue, I estimate anadditional model by lagging volume by one period in the conditional mean and volatility equations. There is evi-dence that volume is not a significant determinant of the volatility of the fed funds markets. For the Eurodollarfutures, I find lagged volume to be positive determinant of the level of interest rates. In contrast, the resultsindicate that lagged volume is negatively related to the volatility in both the Eurodollar spot and futures mar-kets. The estimated coefficients are significant at the 1% confidence level. For the Libor markets, the laggedoption volume is positively related to the level of Libor futures rate, but not significant in the conditional volatil-ity equations. Overall, while these results do indicate some level of endogeneity between volume and volatilityfor the Eurodollar markets, for the majority of the cases, the lagged volume is insignificant in the volatility equations.





major innovation, as well as respond to the information content of this product.Therefore, a third set of models is estimated by redefining the option listingvariable. OPT is now set equal to 1 on listing day and onward, and 0 other-wise. This alternative specification assumes that option introduction has had a one-time permanent effect on the market. The only problem with this specification is that it is difficult to rule out the possibility that the listing

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1990

0808

1990

0104

1991

0313

1991

1015

1992

0519

1992

1221

1993

0727

1994

0288

1994

1003

1995

0508

1995

1208

1996

0712

1997

0213

1997

0918

1998

0424

1998

1125

1999

0702

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0204

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0908

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0416

2001

1121

2002

0628

2003

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0908

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0413

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1115

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0621

2006

0125

2006

0829

2007

0405

2007

1107

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0613

2009

0116

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1990

0808

1990

0104

1991

0313

1991

1015

1992

0519

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1221

1993

0727

1994

0288

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1003

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0508

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1208

1996

0712

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0213

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0918

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0424

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1125

1999

0702

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0204

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0908

2001

0416

2001

1121

2002

0628

2003

0203

2003

0908

2004

0413

2004

1115

2005

0621

2006

0125

2006

0829

2007

0405

2007

1107

2008

0613

2009

0116

Date

Co

nd

itio

nal

Var

ian

ce

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

Spot Variance

Futures Variance

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

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nd

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ian

ce

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Spot Variance

Futures Variance

(A)

(B)

FIGURE 3 (Continued)

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dummy may be capturing the effects of exogenous variables that are excludedfrom the model.

The results, although not reported, are interesting.20 OPT is associatedwith a positive and significant coefficient in the mean equations for four out ofsix interest rates. In the case of the fed funds spot, the coefficient is significantand negative. In three out of six cases, OPT has significant and negative effectson the spot market volatility. For the remaining cases, the effects are insignifi-cant. The volume of trading (VOL) is positive and significant only in the case ofthe conditional mean of the fed funds spot interest rate. Turning to the condi-tional volatility equations, VOL has negative impact on the fed funds spot andthe Eurodollar spot rates. For the futures on the fed funds and the Eurodollar,the effect is positive. Again, a strong generalization is not possible. As discussedearlier, this alternative definition of the listing dummy can be also criticized onthe ground that it assumes options introduction leads to a permanent effect.Overall, it apears that the previous results, based on OPT being set equal to 1for the listing week, offer credible results of the effects of options introductionwithout being too restrictive or too broad.

20Complete results are available upon request.

1990

1109

1990

0409

1991

0617

1992

0120

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0821

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0326

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0

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0.2

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Spot Variance

Futures Variance

(C)

FIGURE 3(A) Conditional variance of the fed funds spot and futures January 4, 1990 to May 29, 2009.

(B) Conditional variance of the Eurodollar spot and futures January 4, 1990 to May 29, 2009. (C) Conditional variance of the USD Libor spot and futures April 9, 1990 to May 29, 2009.





5. CONCLUSIONS

This study examines the effects of the introduction of the CME options on thefed funds futures on the volatility of popular interest rates with futures. Thekey result is that in several instances, there is evidence to suggest that optionsintroduction is associated with a decrease in the conditional volatility of theinterest rates in the sample. With respect to the effects of options trading vol-ume, there is evidence that volatility of two out of three spot interest rates endsup being lower. In contrast, in two out of three cases, options trading volumeleads to higher volatility in the futures markets. This could be a result of hedg-ing options positions in the futures market where dealers buy futures whenprices rise and sell futures when prices fall. As Kambhu (1998) suggests, sucha positive feedback mechanism has the potential to exacerbate the aggregatevolatility in the primary market.

Note that the listing effects documented in this study are robust becausethe analysis controls for market-wide variations in interest rates levels andvolatility during the sample period and still finds that options listing leads to areduction in volatility in the primary markets. For example, the present analysisaccounts for a relatively robust economic expansion that took place during2003-summer of 2007. This was a period of relatively stable economic growthand prosperity, driven largely by rising productivity, a rising real estate market,and a low interest rate volatility regime, among other factors. It is assumed thatthe variable TARGET (target rate for the fed funds market) and the TEDspread are good proxies of the macroeconomic conditions that may have influ-enced the Fed’s decision making during this period.

Furthermore, this study takes into consideration several catastrophic eventsincluding the 9/11 terrorist attacks and the recent credit crisis. All theseevents have had significant impact upon monetary policy and subsequently thelevel and the volatility of interest rates during this period. Finally, dummy vari-ables are included to also account for the day-of-the week effects and other styl-ized variability in the interest rates due to institutional matters such as reservesettlements among banks. Overall, the evidence that options introduction leadsto a decline in the volatility of key interest rates remains robust to exogenousdeterminants of the level and volatility of the interest rates.

BIBLIOGRAPHY

Ashcraft, A. B., & Duffie, D. (2007). Systemic illiquidity in the Federal Funds Market.American Economic Review, 97(2), 221–225.

Bansal, V. K., Pruitt, S. W., & Wei, K. C. J. (1989). An empirical examination of theimpact of CBOE option initiation on the volatility and trading volume of the underlying equities, 1973–1986. Financial Review, 24, 19–29.

356 Sultan


28 Sultan


Becketti, S., & Roberts, D. J. (1990). Will increased regulation of stock index futuresreduce stock market volatility? Economic Review, The Federal Reserve Bank ofKansas, November/December, 33–45.

Bessembinder, H., & Seguin, P. J. (1992). Futures-trading activity and stock pricevolatility. Journal of Finance, 47, 2015–2034.

Bollen, N. P. B. (1998). A note on the impact of options on stock return volatility.Journal of Banking and Finance, 22, 1181–1191.

Bollerslev, T., & Wooldridge, J. (1992). Quasi-maximum likelihood estimation andinference in dynamic models with time-varying covariances. EconometricReviews, 11(2), 143–172.

Brenner, R., & Kroner, K. (1995). Arbitrage and cointegration. Journal of Financial andQuantitative Analysis, 30, 23–34.

Bruck, T., & Wickstrom, B. A. (2004). The economic consequences of terror: Guesteditors’ introduction. European Journal of Political Economy, 20, 293–300.

Cao, H. (1999). The effect of derivative assets on informational acquisition and pricebehavior in a rational expectations equilibrium. Review of Financial Studies, 12,131–161.

Carlson, J. B., Craig, B., & Melick, W. (2005). Recovering market expectations ofFOMC rate changes with options on federal funds futures. Journal of FuturesMarkets, 25(12), 1203–1242.

Carlson, J. B., Melick, W., & Sahinoz, E. Y. (2003). An option for anticipating fedactions. Economic Commentary, Federal Reserve Bank of Cleveland, September 1.

Chatrath, A., Ramachander, R., & Song, F. (1996). The role of futures trading activityin exchange rate volatility. Journal of Futures Markets, 16, 561–584.

Clark, P. (1973). A subordinated stochastic process model with finite variance for spec-ulative prices. Econometrica, 41, 135–155.

Clifton, E. (1985). The currency futures market and interbank foreign exchange trad-ing. Journal of Futures Markets, 5, 375–384.

Coleman, S. P. (2002). The evolution of the federal reserve’s intraday credit policies.Federal Reserve Bulletin, 88(2), 67–84.

Conrad, J. (1989). The price effect of option introduction. Journal of Finance, 44,487–498.

Cyree, K., & Winters, D. (2001). Analysis of federal funds rate changes and variancepatterns. Journal of Financial Research, 24, 403–418.

Damodaran, A., & Lim, J. (1991). The effects of option listing on the underlying stocks’return process. Journal of Banking and Finance, 15, 647–664.

Detemple, J., & Jorion, P. (1990). Option listing and stock returns: An empirical analy-sis. Journal of Banking and Finance, 14, 781–801.

Diamond, D. W., & Verrechhia, R. E. (1987). Constraints on short-selling and asset priceadjustment to private information. Journal of Financial Economics, 18, 277–311.

Edwards, F. R. (1988). Futures trading and cash market volatility: Stock index andinterest rate futures. Journal of Futures Markets, 8, 421–439.

Ely, D. (1991). Derivatives securities and cash market stability. Applied Economics, 23,391–402.

Epps, T. M., & Epps, M. L. (1976). The stochastic dependency of security pricechanges and transaction volumes: Implications for the mixture–of–distributionshypothesis. Econometrica, 44, 305–321.





Figlewski, S., & Webb, G. P. (1993). Options, short sales, and market completeness.Journal of Finance, 48, 761–777.

Fleming, M., & Garbade, K. (2002). When the back office moved to the front burner:Settlement fails in the treasury market after 9/11. Federal Reserve Bank of NewYork Economic Policy Review, 8(2), 35–57.

Freund, S. P., McCann, D., & Webb, G. P. (1994). A regression analysis of the effectsof option introductions on stock variances. Journal of Derivatives, 1, 25–38.

Griffiths, M., & Winters, D. (1995). Day-of-the-week effects in federal funds rates:Further empirical findings. Journal of Banking and Finance, 19, 1265–1284.

Grossman, S. (1988). An analysis of the implications for stock and futures price volatil-ity of program trading and dynamic hedging strategies. Journal of Business, 61,275–298.

Gulen, H., & Mayhew, S. (2000). Stock index futures trading and volatility in interna-tional equity markets. The Journal of Futures Markets, 20, 661–685.

Hamilton, J. D. (1996). The daily market for federal funds. Journal of PoliticalEconomy, (February), 26–56.

Heer, B., Trede, M., & Wahrenburg, M. (1997). The effect of option trading at the DTBon the underlying stocks’ return variance. Empirical Economics, 22, 233–245.

International Monetary Fund. (2001). World economic outlook—The global economyafter September 11. Washington: International Monetary Fund.

Jennings, R., & Starks, L. (1986). Earnings announcements, stock price adjustment,and the existence of option markets. Journal of Finance, 41, 107–125.

Jochum, C., & Kodres, L. (1998). Does the introduction of futures on emerging marketcurrencies destabilize the underlying currencies? IMF Staff Papers, 45, 486–521.

Kambhu, J. (1998). Dealers’ hedging of interest rate options in the US Dollar fixedincome market. Federal Reserve Bank of New York Economic Policy Review, 4,35–57.

Karpoff, J. (1987). The relation between price changes and trading volume: A survey.Journal of Financial and Quantitative Analysis, 22, 109–125.

Klemkovsky, R. C., & Maness, T. S. (1981). The impact of options on the underlyingsecurities. Journal of Portfolio Management, 9, 12–18.

Koutmos, G., & Tucker, M. (1996). Temporal relationships and dynamic interactionsbetween the spot and futures stock markets. Journal of Futures Markets, 53,717–732.

Kraus, T., & Zimmerman, H. (2002). Stock option listings: Information versus liquidi-ty effects. Swiss Journal of Economics and Statistics, 138, 83–97.

Kumar, R., Sarin, A., & Shastri, K. (1998). The impact of options trading on the mar-ket quality of the underlying security: An empirical analysis. Journal of Finance,53(2), 717–732.

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypoth-esis of stationary against the alternative of a unit root. Journal of Econometrics, 54,159–178.

Kyle, A. (1985). A continuous auction and insider trading. Econometrica, 53,1315–1335.

Lacker, J. (2003). Payment system disruptions and the federal reserve followingSeptember 11, 2001. Federal Reserve Bank of Richmond Working Paper, 03–16.

Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data:Volume versus GARCH effects. Journal of Finance, 45, 591–601.

358 Sultan


30 Sultan


Lenain, P., Bonturi, M., & Koen, V. (2002). The economic consequences of terrorism.OCED Working Paper 334. Paris: Organization for Economic Cooperation andDevelopment.

Mayhew, S., & Mihov, V. (2004). How do exchanges select stocks for option listing?Journal of Finance, 58, 447–471.

Mayhew, S., & Mihov, V. (2000). Another look at option listing effects. Working Paper,Texas Christian University. http://papers.ssrn.com/sol3/papers.cfm?abstract_id�223128.

McAndrews, J., & Potter, S. (2002). Liquidity effects of the events of September 11,2001. Federal Reserve Bank of New York Economic Policy Review, 8(1), 59–79.

Pearson, N. D., Poteshman, A. M., & White, J. (2008). Does option trading have a per-vasive impact upon underlying stock prices. Working Paper, University of Illinoisat Urbana-Champaign.

Platen, E., & Schweizer, M. (1998). On feedback effects from dynamic hedging.Mathematical Finance, 8, 67–84.

Ross, S. (1976). Options and efficiency. Quarterly Journal of Economics, 90, 75–89.Shastri, K., Tandon, K., & Sultan, J. (1996). The impact of the listing of options in the

foreign exchange market. Journal of International Money and Finance, 15, 37–64.

Spindt, P. A., & Hoffmeister, R. J. (1986). The micromechanics of the federal fundsmarket: Implications for day-of-the-week effects in funds rate variability. Journalof Financial and Quantitative Analysis, 23, 401–416.

Stein, J. (1987). Information externalities and welfare-reducing speculation. Journal ofPolitical Economy, 95, 1123–1145.

Tauchen, G. E., & Pitts, M. (1983). The price variability–volume relationship on spec-ulative markets. Econometrica, 51, 485–505.



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