Treasury Futures Research Articles

A High-Frequency Analysis of Trading Activity in the Corporate Bond Market: Do Macro Announcements Drive Activity?

Herein we explore whether macroeconomic announcements are a driving factor in the trading activity of US corporate bond market. Prior studies have documented a strong response of bond returns and interest rates to surprises in macroeconomic data in the US Treasury and Treasury futures markets. Likewise, studies have also documented that trading activity changes sharply, based on informational impulses provided by the release of economic data. We contribute to the existing literature by examining how both daily and intraday measures of trading activity are impacted by surprises in macro data. Our main findings are that the thinly-traded market for corporate bonds is largely unaffected by surprises in individual economic reports and that the market is dominated by day-of-week and time-of-day affects. The results stand in contrast to prior studies documenting increased financial market activity, such as Andersen, Bollerslev, Diebold and Vega (2003). The existence of intraday or “diurnal” trading patterns is consistent with earlier findings in Anderson and Bollerslev (1997,1998) studies with respect to intraday asset returns and volatility. Engle and Ng (1993) and Engle and Solkasa (2012)

Management Insights

Management Insights

The fed and the term structure: Addressing simultaneity within a structural VAR model

Abstract This paper applies a new identification approach to estimate the contemporaneous relation between the term structure and monetary policy within a VAR framework. To achieve identification, we combine high-frequency Treasury futures and fed funds futures data with the VAR methodology. Results indicate that policy actions have a slope effect in the yield curve. We also find that the Fed responds to Treasury yields and that this response is stronger for the short and intermediate rates and less aggressive for long-yields. All estimated parameters are significant and robust to various model specifications.

Bond Variance Risk Premia

This paper studies variance risk premiums in the Treasury market. We first develop a theory to price variance swaps and show that the realized variance can be perfectly replicated by a static position in Treasury futures options and a dynamic position in the underlying. Pricing and hedging is robust even if the underlying jumps. Using a large options panel data set on Treasury futures with different tenors, we report the following findings: First, the term structure of implied variances is downward sloping across maturities and increases in tenors. Moreover, the slope of the term structure is strongly linked to economic activity. Second, returns to the Treasury variance swap are negative and economically large. Shorting a variance swap produces an annualized Sharpe ratio of almost two and the associated returns cannot be explained by standard risk factors. Finally, the returns remain highly statistically significant even when accounting for transaction costs and margin requirements.

Customer Order Flow, Intermediaries, and Discovery of the Equilibrium Risk-free Rate

Macro announcements change the equilibrium riskfree rate. We find that treasury prices reflect part of the impact instantaneously, but intermediaries rely on their customer order flow after the announcement to discover the full impact. This customer flow informativeness is strongest when analyst macro forecasts are most dispersed. The result holds for 30-year treasury futures trading in both electronic and open-outcry markets. We further show that intermediaries benefit from privately recognizing informed customer flow, as their own-account trading profitability correlates with customer order access.

Regime‐switching in stock index and Treasury futures returns and measures of stock market stress

AbstractWe investigate bivariate regime‐switching in daily futures‐contract returns for the US stock index and ten‐year Treasury notes over the crisis‐rich 1997–2005 period. We allow the return means, volatilities, and correlation to all vary across regimes. We document a striking contrast between regimes, with a high‐stress regime that exhibits a much higher stock volatility, a much lower stock–bond correlation, and a higher mean bond return. The high‐stress regime is associated with higher average values of stock‐implied volatility, stock illiquidity, and stock and bond futures trading volume. The lagged implied volatility from equity‐index options is useful in modeling the time‐varying transition probabilities of the regime‐switching process. Our findings support the notions that: (1) stock market stress can have a material influence on Treasury bond pricing, and (2) the diversification benefits of combined stock–bond holdings tend to be greater during times with relatively high stock market stress. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:753–779, 2010

Delivery options and convexity in Treasury bond and note futures

Using Treasury bond and note futures to hedge fixed-income portfolios is complicated by the large number of bonds that are eligible to deliver against the contract. Grieves and Marcus [Grieves, R. and A. Marcus. (2005). Delivery options and Treasury bond futures hedge ratios. Journal of Derivatives, 13, 70–76.] show that, in some circumstances, only two bonds—those with the highest and the lowest duration—are relevant for the hedging problem, which makes computation of analytic hedge ratios tractable. We evaluate the empirical efficacy of their two-relevant-bonds model. We compare the maturities of actual cheapest-to-deliver bonds to the prediction of the two-deliverables model and calculate empirical price values of a basis point for Treasury futures contracts to determine whether contract prices display the negative convexity predicted by the model. The model has worked very well for the note contract and very poorly for the bond contract. We show that the difference in model performance is related to the shape of the yield curve.

The Price of Interest Rate Variance Risk and Optimal Investments in Interest Rate Derivatives

Recent research on unspanned stochastic variance raises the possibility that interest rate derivatives constitute an important component of optimal fixed income portfolios. In this paper, I estimate a flexible dynamic term structure model that allows for unspanned stochastic variance on an extensive data set of swaps and swaptions. I find that variance risk is predominantly unspanned by bonds, and that the price of risk on the unspanned variance factor is significantly larger in absolute value than the prices of risk on the term structure factors. Consequently, Sharpe ratios on variance sensitive derivatives are about three times larger than Sharpe ratios on bonds or short-term bond futures. These findings are corroborated by an analysis of the Treasury futures market, where the variance risk premium is estimated with a model independent approach. I then solve the dynamic portfolio choice problem for a long-term fixed income investor with and without access to interest rate derivatives and find substantial utility gains from participating in the derivatives market.

Price discovery in the round-the-clock U.S. Treasury market

Abstract We evaluate the efficacy of price discovery in the round-the-clock U.S. Treasury market. Using a comprehensive intraday database, we explore informational role of trades over the 24-hour day. We find that information asymmetry is generally highest in the preopen period and lowest in the postclose period. Information asymmetry in the overnight period is comparable to that in the regular trading period. However, on days with macroeconomic announcements, information asymmetry peaks shortly after the news release at 8:30. Moreover, information asymmetry is higher on Monday morning and higher immediately before than after the open of U.S. Treasury futures trading. Although volume is low after hours and trading cost is relatively high, overnight trading generates significant price discovery. Results suggest that overnight trading activity is an important part of the Treasury price discovery process.

Price Discovery in the Round-the-Clock U.S. Treasury Market

We evaluate the efficacy of price discovery in the round-the-clock U.S. Treasury market. Using a comprehensive intraday database, we explore informational role of trades over the 24-hour day. We find that information asymmetry is generally highest in the preopen period and lowest in the postclose period. Information asymmetry in the overnight period is comparable to that in the regular trading period. However, on days with macroeconomic announcements, information asymmetry peaks shortly after the news release at 8:30. Moreover, information asymmetry is higher in Monday morning and higher immediately before than after the open of U.S. Treasury futures trading. Although volume is low after hours and trading cost is relatively high, overnight trading generates significant price discovery. Results suggest that overnight trading activity is an important part of the Treasury price discovery process.

Delivery Options and Convexity in Treasury Bond and Note Futures

Using Treasury bond and note futures to hedge fixed income portfolios is complicated by the large number of bonds that are eligible to deliver against the contract. Grieves and Marcus (2005) show that in some circumstances, only two bonds - those with the highest and the lowest durations - are relevant for the hedging problem, which makes computation of analytic hedge ratios tractable. We evaluate the empirical efficacy of their two-relevant-bond model. We compare the maturities of actual cheapest-to-deliver bonds to the prediction of the two-deliverable model, and calculate empirical price-values-of-a-basis-point for Treasury futures contracts to determine whether contract prices display the negative convexity predicted by the model. The model worked very well for the note contract and very poorly for the bond contract. We show that the difference in model performance is related to the shape of the yield curve.

An analysis of the failed municipal bond and note futures contracts

AbstractThis study analyzes the failure of the municipal bond and municipal note futures contracts. The municipal bond contract is shown to have been the most effective hedge in the municipal market over its tenure. Changes in volume in the municipal bond contract were closely related to changes in the volume in the U.S. Treasury bond futures contract, the spot–municipal‐over‐bonds (MOB) ratio, and visible supply. The failure of the municipal bond contract is mainly attributed to a decrease in trading volume in the U.S. Treasury futures market. This was impacted by the onset of electronic trading, which the municipal futures market was reluctant to embrace. The municipal note contract was a less effective hedge than U.S. Treasury note futures and ten‐year London Interbank Offered Rate swaps. The failure of the municipal note futures contract is attributed to the existence of well‐established alternative hedges, and segmentation in the municipal market. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:656–679, 2008

Investor Inattention and the Market Impact of Summary Statistics

We show that U.S. stock and Treasury futures prices respond sharply to recurring stale information releases. In particular, we identify a unique macroeconomic series—the U.S. Leading Economic Index® (LEI)—which is released monthly and constructed as a summary statistic of previously released inputs. We show that a front-running strategy that trades S&P 500 futures in the direction of the announcement a day before its release and then trades in the opposite direction of the announcement following its release generates an average annual return of close to 8%. These patterns are more pronounced for high beta stocks, for stocks that are more difficult to arbitrage, and during times when investors' sensitivity to firm-specific stale information is high. Treasury futures exhibit similar, albeit less pronounced, price patterns. Other measures of information arrival, such as price volatility and volume, spike following the release. These empirical findings suggest that some investors are inattentive to the stale nature of the information included in the LEI releases, instead interpreting it as new information, and thereby causing temporary yet significant mispricing. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.

Treasury Futures Trading and Differences of Opinion: A Note on Volume-Volatility Models

I review in this paper the classes of models in theorizing the volume-volatility relation. The strategic trading model of private information by Kyle (1985) and differences of opinion model of public information by Harris and Raviv (1993) are discussed. I propose that the differences of opinion models are more suitable in studying Treasury futures trading, since virtually all diligent analysts are exposed to the same set of public data (such as Federal Open Market Committee announcements, Fed watchers' studied guesses, or quarterly GDP growth rates and monthly nonfarm payrolls for general economic outlook), but they nonetheless infer their interest rate expectations with different interpretations of data. Unlike the cases in stock or corporate bond markets where differential accesses to private information are essential, differences in interpretation are more significant in Treasury futures trading and private information is less distinctive for individual contracts. The main predictions of the Harris and Raviv model are compared with VAR results from Liao, W. (2008b); it is especially notable that the positively autocorrelated volume pattern is confirmed by the surprisingly strong VAR evidence. I then conclude with an episode (09/2006) of market squeeze warnings to financial institutions essentially primary dealers) in the Treasury market. Implications on Treasury futures trading in terms of regulating the market are discussed.

Speculators versus Hedgers: Evidence from Treasury Futures VAR Analysis of the Volume-Volatility Relations

This paper examines empirical relations of volume and volatility of Treasury futures contracts traded at the CBOT (now CME Group). It is roughly concluded that speculators destabilize the Treasury futures market, causing a more turbulent trading pattern; nonetheless, evidence suggests at best a weak relation between hedging activities and decreased price volatilities. This study applies the VAR technique to Treasury futures trading with an explicit focus on individual contracts. GARCH volatility specifications are tested and GARCH(1,1) is conveniently arrived at for future cross-market comparisons. A promise of differentiating an economic reasoning with GARCH volatility (versus historical and intra-day) is noted. Automated analytics in the same vein can be developed for trading or regulatory monitoring purposes.

Trading Activity in the Treasury Futures Market and Its Role in Futures Price Fluctuations

Chapter 1: Trading Treasury Futures: A Vector Autoregressive (VAR) Analysis on the Volume-Volatility RelationDoes trading by hedgers or speculators destabilize the Treasury futures market? And if it is the case, how do they destabilize the market? This is an empirical study on the volume-volatility relation which is central to the market microstructure literature. Vector autoregressive (VAR) analysis is conducted on the 2-, 5-, 10-, and 30-year Treasury futures contracts traded at the Chicago Board of Trade. With some mixed results, it can be roughly concluded that speculators destabilize the Treasury futures market, causing a more turbulent trading pattern as evident in the increased volatility. However, the same can not be said of hedgers; available evidence at best suggests a weak relation between hedging and a decreased price volatility (indicating a market being stabilized). To my knowledge, this study is the first in applying the VAR technique to the context of Treasury futures trading, and the first in comparing the three different volatility measures (intra-day, historical, and GARCH) simultaneously. In addition, GARCH volatility specifications are comprehensively tested and the commonly-used GARCH(1,1) is conveniently arrived at. It is also the first in examining the volume and open interest by constructing two different trading activity series (aggregate and active contract amounts) in the same study, and the results are compared. On the volume-volatility relation, it is among the few that have a specific focus on each individual contract instead of an all-as-one approach. The long period of data (from year 1991 to 2006) is applied to the VAR framework.Chapter 2: Volume and Volatility: The Relation, Two Models, and Regulating Market Squeezes of Treasury Futures TradingI review in Chapter 2 the classes of models in theorizing the volume-volatility relation. The strategic trading models of private information by Kyle (1985) and differences of opinion model of public information by Harris and Raviv (1993) are discussed. I propose that the differences of opinion model are more suitable for trading behavior in the Treasury futures market, since virtually all diligent analysts are exposed to the same set of public data (such as Federal Open Market Committee announcements, Fed watchers' studied guesses, or quarterly GDP growth rates and monthly nonfarm payrolls for general economic outlook), but they nonetheless infer their interest rate expectations with different interpretations of data. Unlike the cases in stock or corporate bond markets where differential accesses to private information are essential, differences in interpretation are more significant in Treasury futures trading and private information is less distinctive for individual contracts. The main predictions of the Harris and Raviv model are compared with my VAR results from Chapter 1; it is especially notable that the positively autocorrelated volume pattern is confirmed by the surprisingly strong VAR evidence. I then conclude with the recent episode (09/2006) of market squeeze warnings to Treasury primary dealers. Implications on Treasury futures trading in terms of regulating the market are discussed.

Dynamic Term Structure Modeling: The Preface

Dynamic Term Structure Modeling, the second book in the trilogy on the Fixed Income Valuation Course, shows how to value interest rate derivatives and credit derivatives using a variety of affine, quadratic, HJM, and LIBOR market models. Using a new taxonomy, this book classifies all term structure models as either fundamental models, or preference-free single-plus, double-plus, and triple-plus models. Filled with in-depth insights and expert advice, this book shows how to price basic interest rate and credit derivative products, such as Treasury and Eurodollar futures, bond options, interest rate options (e.g., caps, floors, and swaptions), forward rate agreements, interest rate swaps, credit default swaps, credit spread options, and others. With intuitive explanations and fully developed examples, this book provides new transforms for building efficient trees under state-dependent volatility models, stochastic volatility models, and jump-diffusion models, for pricing American options, and describes fast computational methods such as the Fourier inversion method (including the FFT) and the cumulant expansion method, for valuing interest rate derivatives and credit derivatives, under a variety of affine, quadratic, and LIBOR market models. This book is also accompanied by an informative CD-ROM, which contains various Excel®/VBA® spreadsheets. This software allows for valuation of interest rate derivatives by building interest rate trees for low-dimensional affine models, as well as computing solutions using quasi-analytical formulas for higher-dimensional affine, quadratic, and LIBOR market models. Though most of the programs require coding in advanced scientific languages, such as C and C++, the final output is presented in user-friendly Excel/VBA spreadsheets.

Price Discovery in the Treasury Futures Market

We investigate the mechanism by which price discovery takes place within the futures market for U.S Treasury securities. Specifically, given the strong theoretical linkage between the U.S. Treasury cash and futures markets, we compare how orderflow contributes to price discovery as well as analyze how and when information flows from one market to the other. We also consider how a number of environmental variables (trader type, financing rates and liquidity) impact the information flows between these two markets. Our findings provide new evidence on the extent to which price discovery happens away from a primary market.

Price Discovery in the U.S. Treasury Market

This paper is the first to characterize the tatonnement of high-frequency returns from U.S. Treasury spot and futures markets. In particular, we highlight the previously neglected role of the futures markets in price discovery. The lower-bound estimate of bivariate information shares for 30-year Treasury futures typically exceeds 50% from 1998 on. Standard liquidity measures, including the proportion of trades and relative bid-ask spreads, explain daily information shares. These conclusions still hold when one controls for days of macroeconomic announcements. Finally, a 5-dimensional cointegrated system explains a high percentage of Treasury returns. In that system, the 30-year futures contract and the 5-year spot market dominate price discovery.

Affine-Quadratic Term Structure Models - Toward the Understanding of Jumps in Interest Rate

In this paper, we propose a unifying class of affine-quadratic term structure models (AQTSMs) in the general jump-diffusion framework. Extending existing term structure models, the AQTSMs incorporate random jumps of stochastic intensity in the short rate process. Using information from the Treasury futures market, we propose a GMM approach for the estimation of the risk-neutral process. A distinguishing feature of the approach is that the time series estimates of stochastic volatility and jump intensity are obtained, together with model parameter estimates. Our empirical results suggest that stochastic jump intensity significantly improves the model fit to the term structure dynamics. We identify a stochastic jump intensity process that is negatively correlated with interest rate changes. Overall, negative jumps tend to have a larger size than positive ones. Our empirical results also suggest that, at monthly frequency, while stochastic volatility has certain predictive power of inflation, jumps are neither triggered by nor predictive of changes in macroeconomic variables. At daily frequency, however, we document interesting patterns for jumps associated with informational shocks in the financial market.