Treasury Futures Research Articles

Modeling volatility in dynamic term structure models

We propose no-arbitrage term structure models with volatility factors that follow GARCH processes. The models’ tractability is similar to canonical affine term structure models, but they fit yield volatility much better, especially for long-maturity yields. This improvement does not come at the expense of a deterioration in yield fit. Because of the improved volatility fit, the model performs substantially better in pricing Treasury futures options. We conclude that the specification of the volatility factors is critical. Modeling volatility as a function of (lagged) squared innovations to factors improves on models where volatility is a linear function of the factors.

Why Do Mutual Funds Invest in Treasury Futures?

Asset managers’ net long positions in Treasury futures have reached their historical highs in recent months, driven in part by mutual funds’ demand for short- and medium-term Treasury futures. Analyzing mutual fund portfolio holdings reports on SEC Form N-PORT, we find that the increase in mutual funds’ futures holdings since 2020 can be attributed to both increased demand for Treasury exposures during a higher interest rate environment and mutual funds’ preference for sourcing these exposures through futures rather than securities.

Quantifying Treasury Cash-Futures Basis Trades

The Treasury cash-futures basis trade exploits the difference in prices between a Treasury security and a related Treasury futures contract – the so-called cash-futures basis – by purchasing the asset that is relatively undervalued and selling the other in a bet that the prices will converge. Basis traders support Treasury market functioning by keeping the prices of Treasury futures near their fair value relative to Treasury securities and by serving as an important source of demand for Treasury securities, including during the 2017-2019 period of quantitative tightening when basis traders absorbed much of the increased Treasury supply.

Risk prediction of interest rate futures based on machine learning scenarios

The interest rate futures market is a significant part of the financial market. It has a crucial impact on forecast the interest rate risk in global financial markets, which due to the complexity of financial markets and the volatility of interest rate futures. Based on machine learning scenarios to analyse and compare different algorithms, this paper analyses and forecast 2-year Treasury futures for the period 2022.6-2023.6 through regressions and other methods. Meanwhile, it is applied to construct charts and graphs to better compare and analyse models that are more suitable for forecasting future risk in interest rate futures. National policies, the volatility of the general market environment and its smoothness are utilized as the main factors to forecast its risk fluctuations. The main algorithms this paper use are: random forest regression, ARIMA model, BP Neural Network regression model, ARCH model (model validity test), GARCH model. In conclusion, though the predicted results of the random forest and ARIMA models exhibit a close to 0 and have strong stability, the predicted results of the GARCH are relatively better, none of them achieve the desired prediction performance.

The crucial role of the five-year Treasury in the US yield curve

This paper analyzes the connectedness and spillover transmission mechanism in the U.S. Treasury yields with different maturities than previous studies which focused on the spread between short- and long-term rates. We find that 5-year Treasury yields play the role of the transmitter to other yields, conveying their liquidity and center location in yield curve advantages. Most importantly, the information transmitter role of 5-year Treasury yields remains unchanged under unconventional monetary policies even though these policies decrease the total connectedness of Treasury yields. The transmitter role of the 5-year Treasury has also manifested in the Treasury market flash event and in Treasury futures and ETFs.

Recent Developments in Hedge Funds’ Treasury Futures and Repo Positions: is the Basis Trade “Back"?

In short, the answer is "probably", at least to some degree. This note summarizes recent developments in hedge funds' Treasury futures and repo positions derived from the Commodities Futures and Trading Commission's (CFTC's) Traders in Financial Futures data and the Office of Financial Research's ("OFR") Cleared Repo Collection.

Inter-Variety Equilibrium of Chinese Treasury Futures

Treasury futures, important tools in interest risk management, need to maintain price equilibrium between different varieties. In this paper, we conduct research on ten-, five-, and two-year Treasury futures in China’s futures market. The auto-regression model is used to fit and predict the spot yield, the CTD (cheapest to deliver) price is used in valuing Treasury futures, and the transaction cost and market friction are considered in building the arbitrage-free spread interval. By comparing the amount of deviation and the equilibrium reversion speed, we analyse the inter-variety price equilibrium between Treasury futures. We find that there are many arbitrage opportunities among the three varieties, and the market is not fully efficient. Through further analysis of the pairwise spread relationship of the futures, we conclude that longer operation of the Treasury futures market will lead to higher market efficiency, shorter duration of arbitrage opportunities, and a faster return to equilibrium. The existing literature mainly focuses on the equilibrium relationship between two Treasury futures in statistical terms, but this paper examines the equilibrium relationship between all existing varieties of Treasury futures in China’s market based on pricing, which expands the subject and methods of research on inter-variety equilibrium in Treasury futures.

Unravelling the JPMorgan spoofing case using particle physics visualization methods

AbstractOn 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time‐based snapshots. This approach allows to examine multiple indicators related to market manipulation and complement existing research methods, thereby enhancing the identification and understanding of, as well as the motivation for, market manipulation. In the JPMorgan cases, we offer an alternative motivation for spoofing than moving the price.

The misleading notion of notionals—Why market value might be a more meaningful measure in the Treasury futures market

The misleading notion of notionals—Why market value might be a more meaningful measure in the Treasury futures market

When leverage ratio meets derivatives: Running out of options?*

AbstractThis paper examines the impact of Basel III leverage ratio on the competitive landscape of US derivatives markets. Because the leverage ratio focuses on notional amounts and does not fully recognize offsetting positions and risk‐mitigating collateral, it is more likely the binding constraint for derivatives. The leverage ratio also put heterogeneous constraints on different types of institutions and activities. Using daily positions of clearing members and their customers on S&P 500 E‐mini futures options, we test the following four hypotheses when the public disclosure of the leverage ratio became mandatory in January 2015: (1) banks lose market share to nonbanks; (2) US banks lose market share to European banks; (3) banks' clearing activities shift away from customer accounts to house accounts; (4) low‐delta options are affected most by the leverage ratio. All hypotheses are confirmed in the data. Short‐dated US Treasury futures options, which receive zero exposure in the leverage ratio calculation, do not exhibit such behavior. Our evidence suggests that the leverage ratio requirement pushes derivatives activities toward less constrained institutions and market segments.

Economic policy uncertainty and volatility of treasury futures

This paper investigates the relation between Treasury futures market volatility and economic policy uncertainty using GARCH-MIDAS. We formulated models with the realized volatility of Treasury futures, the level and volatility of economic policy uncertainty. We find that the realized volatility of Treasury futures and economic policy uncertainty play a significant role in the dynamics of long-run volatility in Treasury futures markets in China and United States.

Predicting Stock and Bond Market Returns with Emotions: Evidence from Futures Markets

We explore the ability of market emotions (fear, gloom, joy, optimism) to predict S&P 500 Index and 10-year Treasury notes futures returns by utilizing VAR and TGARCH models. In our VAR models, we find that one of four emotions (fear) has predictive power for stock index futures returns. We also find Treasury futures market returns are influenced by joy and optimism measures of emotions. Further, we employ a TGARCH model with anemotional sentiment measure (fear) and find that fear has a major effect on the market returns and conditional volatility of futures markets.

Bond Implied Risks Around Macroeconomic Announcements

Using a large panel of Treasury futures and options, I construct model-free measures of bond uncertainty and tail risks across different tenors from 2000 to 2020. I find that bond tail risk 1) negatively correlates with stock tail risk in general, but the correlation turns positive prior to and around three financial crises; 2) It increased dramatically before the 2008 Financial Crisis and in March 2020 foreshadowing the extreme challenges in the Treasury bond markets; 3) and has significantly decreased in recent years under zero-lower-bound and forward guidance. I then study the behavior of bond tail and uncertainty risk measures around FOMC announcements and document three novel findings: First, bond uncertainty increases three to two days prior to the announcements and reverts back upon release, due to an increase in call option prices rather than puts. Second, yields of 5, 10, and 30 year Treasuries decline by 1bps on the day before the announcement. Third, uncertainty risk cannot help explain the pre-FOMC announcement drift.

What Do Quoted Spreads Tell Us About Machine Trading at Times of Market Stress? Evidence from Treasury and FX Markets during the COVID-19-Related Market Turmoil in March 2020

We highlight four main results from our analysis. First, we find that quoted spreads did not merely rise to much higher than usual levels on average, but were also oscillating over a wider range; while at some points within the day spreads were substantially wider than on a typical day, at other times spreads were as narrow as on a typical day. This result suggests that market participants, likely including both dealers and PTFs, became less willing to replenish the order book fast enough to keep quoted bid-ask spreads consistently tight in these markets, which may have amplified the initial shock to liquidity, particularly for the 30-year bond. Second, using alternative volume-weighted quoted spread measures we put forward in this note, we also find evidence that some market participants employing machine trading strategies were nonetheless able to mitigate the increase in trading costs—by executing trades when bid-ask spreads were relatively tight. Third, we further document that liquidity conditions in Treasury futures markets deteriorated less than those in Treasury cash markets. Fourth, we find that quoted spreads in other liquid futures markets, such as those for major foreign exchange (FX) rates, increased notably more than most of the Treasury futures market (the exception being the 30-year Ultra Treasury bond futures contract). Our findings thus point to the systemic nature of the market strains in March, extending beyond differences in market structure and participation. Our findings also underscore the potential benefits from the use of sophisticated algorithmic execution strategies that can adapt rapidly to changing liquidity conditions.

Asset pricing: A tale of night and day

The capital asset pricing model (CAPM) performs poorly overall, as market risk (beta) is weakly related to 24-h returns. This is because stock prices behave very differently with respect to their sensitivity to beta when markets are open for trading versus when they are closed. Stock returns are positively related to beta overnight, whereas returns are negatively related to beta during the trading day. These day-night relations hold for beta-sorted portfolios and individual stocks in the US and internationally as well as for industry and book-to-market portfolios and cash flow and discount rate beta-sorted portfolios. In addition to the change in slope of returns with respect to beta, the implied risk-free rate differs significantly between night and day. Consistent with this, returns on US Treasury futures differ significantly between night and day.

Macroeconomic news and treasury futures return volatility: Do treasury auctions matter?

Various macroeconomic announcements are known to influence asset price volatility. In addition to non-farm payrolls, we highlight the importance of Treasury auctions – a news event that has grown in importance due to ongoing Federal deficits. The occurrence of an auction, which increases supply in the underlying cash market, pushes futures prices lower and volatility higher. Conversely, a higher bid-to-cover ratio, indicates greater demand for Treasury securities, increases Treasury futures prices and lowers volatility. The response is consistent with market participants using futures to manage inventory risk. The results are consistent across a set of volatility estimates, and in an alternate conditional volatility framework.

Taper Tantrums: Quantitative Easing, Its Aftermath, and Emerging Market Capital Flows

Abstract This paper examines the spillover effects of U.S. unconventional monetary policy (UMP) on emerging market capital flows and asset prices. Affine term structure model estimates show that U.S. monetary policy shocks, identified with high-frequency Treasury futures data, represent revisions to expected short-term yields and term premia, especially during the UMP period. The policy shocks exhibit sizable effects on U.S. holdings of emerging market assets. These effects disproportionately manifest through valuation changes versus physical flows, are more pronounced for equity relative to bond markets, and are asymmetric between the quantitative easing and tapering periods, with flows more important during the unwinding.

Large Order Size Liquidity in Treasury Markets

We measure market liquidity for large-sized orders in the ten-year treasury futures market estimating mean-variance frontiers for their execution cost during the period of 2012 to 2017. We identify large orders from regulatory transaction data and introduce a methodological innovation to infer the urgency of a large order from the pattern of its execution. We find that the mean-variance frontier becomes significantly worse as order size increases, but that the frontier has improved over the time period studied. We also find that the costs of executing large orders on behalf of customers are significantly greater than the costs of executing orders for house accounts.

Precedence rules in matching algorithms

Markets with time priority rules incentivize efforts to increase trading speeds. In particular, revenues resulting from liquidity provision due to discrete pricing can often accrue to the fastest traders, those who can hold favorable queue positions. We examine the impact of precedence rules for a single market that switched to a first-in-first-out (FIFO) matching algorithm from one primarily based on pro-rata. Due to a technology issue, in May 2015 the two year Treasury futures contract unexpectedly switched from partially pro-rata to FIFO for several days. We study the effects of this switch through a difference-in-difference comparison with a related futures contract that had no change in priority rules. We find that compared to FIFO, orders placed later in time are significantly more profitable under pro-rata. However, the lower profitability of earlier orders in the queue under pro-rata matching causes prices to be less efficient under pro-rata rules. Secondary results find that order sizes and cancellations increase under pro-rata. While analyzed using the Treasury futures market, our findings are applicable to commodity futures as well, where both FIFO and pro-rata are widely used.

Does maturity matter? The case of treasury futures volume

AbstractWe find that Treasury futures volume contains information about future economic and financial market conditions. Short‐ and long‐term volumes are economically different: A relatively higher volume in short‐term (long‐term) Treasury futures is counter‐cyclical (procyclical), preceding worse (better) economic and financial conditions. Further, we construct a single factor from futures volumes of different maturities that forecasts the performances of Treasury securities and the corporate debt and equity markets, as well as macroeconomic conditions. Our results are consistent with the notion that futures volumes from different market segments reflect differences in beliefs and contain different information about future financial and economic activity.