Market Resiliency Research Articles

Strengthening the Treasury Market

For more than 200 years, the market for U.S. Treasury securities has propelled U.S. economic success. More recently, it has been described as the world’s most important financial market because Treasury securities promote liquidity, price discovery, and economic stability across the globe. Despite its vital role at home and abroad, the Treasury market lacks certain structural protections that are common in other advanced financial markets. Bringing the Treasury market into the modern age is necessary because it is not immune to the risks that confront other financial markets. This was underscored in early 2020, when the coronavirus (COVID-19) pandemic roiled the Treasury market, producing significant trading disruptions and liquidity constraints. 
 To help reduce the risk of future Treasury market instability, this Article recommends modest enhancements that have proven successful in similar markets. Robust public reporting of Treasury securities trades, improved oversight of trading venues, safeguards that promote operational resiliency, and expanded central clearing will promote Treasury market strength and resiliency well into the future.

The man in the middle—liquidity provision under central clearing in the credit default swap market: A regression discontinuity approach

AbstractIn this study, I analyze the effect of central clearing on credit default swap (CDS) market breadth, depth, and resiliency using a regression discontinuity design. I find evidence for a decrease in absolute bid–ask spreads and bid–ask spread resiliency and an increase in gross trading volume with the beginning of central clearing. However, we observe positive effects of central clearing on CDS market liquidity only for CDS contracts of high fundamental and liquidity risk. Further results indicate that lower trading frictions, that is, counterparty risk and regulatory capital charges, may explain the positive effects of central clearing on CDS market liquidity.

Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.

Resiliency: Cross-Venue Dynamics with Hawkes Processes

Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.

Multidimensional Effects of Central Clearing on CDS Market Liquidity and Their Economic Channels – A Regression Discontinuity Approach

In this study, I analyze the effect of central clearing on market liquidity in the CDS market. This study extends existing literature by using semi-parametric and non-parametric regression discontinuity designs (RDD) in order to isolate the effect of central clearing on measures of market tightness, market depth and market resiliency. I find evidence for a decrease in absolute bid-ask spreads with the beginning of central clearing and an increase in gross trading volume. Bid-ask spread resiliency decreases with the beginning of central clearing. These effect differ across contracts depending on their fundamental risk and liquidity risk. I show that counterparty risk and inventory costs may partly explain the effects of central clearing on CDS market liquidity. Especially the lower relevance of counterparty risk and lower regulatory capital charges seem to affect positively dealer competition and risk-taking capacity in the CDS market.

Liquidity and Information in Limit Order Markets

How does informed trading affect liquidity in limit order markets, where traders can choose between market orders (demanding liquidity) and limit orders (providing liquidity)? In a dynamic model, informed trading overall helps liquidity: A higher share of informed traders i) improves liquidity as proxied by the bid–ask spread and market resiliency, and ii) has no effect on the price impact of orders. The model generates other testable implications, and suggests new measures of informed trading.

Estimation of intraday stock market resiliency: Short-Time Fourier Transform approach

In this study, a new methodology for intraday stock resiliency measurement based on the Short-Time Fourier Transform (STFT) for high-frequency data is introduced and utilized. The STFT is a Discrete Fourier Transform (DFT) variant that measures local signal changes over time. Resiliency is one of stock market liquidity dimensions and, as an estimate of price dynamism, is not constant over a given time period. The findings of empirical experiments for real-data from the Warsaw Stock Exchange confirm that the new proxy enables us to unveil and investigate intraday patterns of resiliency. We wish to emphasize both theoretical and practical aspects of market resiliency measurement.

Depth, tightness and resiliency as market liquidity dimensions: evidence from the Polish stock market

The aim of this paper is an empirical analysis of market liquidity dimensions on the Warsaw Stock Exchange (WSE). We investigate market depth, tightness, and resiliency for 53 companies divided into three size groups. The high-frequency data covers the period January 2005 - June 2015. The additional goal is a robustness analysis of the results with respect to the whole sample period and three adjacent subsamples: pre-crisis, crisis, and post-crisis periods. Order ratio (OR) is employed as a proxy of market depth. Market tightness is approximated by using relative spread (RS). Market resiliency is estimated by utilising realised spread (RealS). The empirical results indicate that OR values do not depend on a firm size, while RS estimations are slightly higher for small companies. RealS proxy values are positive for almost all stocks. Moreover, the results turn out to be robust to the choice of the sample for all groups of assets.

Market Microstructure Invariance: A Dynamic Equilibrium Model

We derive invariance relationships in a dynamic, infinite-horizon, equilibrium model of adverse selection with risk-neutral informed traders, noise traders, market makers, and with endogenous information production. Scaling laws for bet size and transaction costs require the assumption that the effort required to generate one bet does not vary across securities and time. Scaling laws for pricing accuracy and market resiliency require the additional assumption that private information has the same signal-to-noise ratio across markets. Prices follow a martingale with endogenously derived stochastic volatility. Returns volatility, pricing accuracy, liquidity, and market resiliency are connected by a specific proportionality relationship. The model solution depends on two state variables: stock price and hard-to-observe pricing accuracy. Invariance makes predictions operational by expressing them in terms of log-linear functions of easily observable variables such as price, volume, and volatility.

Transitory prices, resiliency, and the cross-section of stock returns

This paper suggests a new measure of stock market resiliency and demonstrates that resiliency is a dimension of liquidity that generates cross-sectional variations in stock returns. Resiliency is defined as quickness of the transitory price recovery from a liquidity shock. Using the Beveridge-Nelson decomposition and the spectral analysis in the frequency domain, we measure resiliency as the speed of mean reversion of transitory price components. Our main finding is that a zero-investment portfolio long in low-resiliency stocks and short in high-resiliency stocks earns significant abnormal returns. We also find that our resiliency measure is complementary to existing liquidity measures.

Measuring stock market resiliency with Discrete Fourier Transform for high frequency data

In this paper, we investigate market resiliency as one of the stock market liquidity dimensions. A new methodology for stock resiliency measurement based on Discrete Fourier Transform (DFT) for high-frequency intraday data is introduced. The known disadvantage of DFT is signal leakage. Therefore, the modified formula for resiliency proxy that decreases the signal leakage impact by filtering is utilized. Three alternative window functions are employed: (1) the Hamming window, (2) the Kaiser window, and (3) the SR785 Flat-Top window. Furthermore, there is provided statistical analysis of the results, including its significance and some additional properties. The findings of empirical experiments for real-data from the Warsaw Stock Exchange reveal that the results rather turn out to be robust to the choice of the window filter. Thus, the DFT approach might be considered an auspicious resiliency proxy with an intuitive base.

Market resiliency conundrum: is it a predicator of economic growth?

Resiliency provides fundamental insights on the speed at which the marginal price impact increases as transaction volume increases in the stock market yet very few empirical research has been dedicated to its study. Consequently, this study was directed towards determining whether market resiliency is a predicator of economic growth. Secondly, the study also sought to examine whether real interest rate and risk premium moderate the relationship between stock market resiliency and the economic growth in Kenya. To solve the conundrum on the relationship between market resiliency and economic resiliency growth, a sagacious moderating regression analysis (MRA) was used. The liquidity and variance ratios were used as measures of resiliency while real interest rate and risk premium were taken as moderating variables. The CUSUM plots were used to determine the stability of the model. The results of this study shows that market resiliency is a predicator of economic growth and both real interest rates and risk premium moderates the relationship between stock market resilience and the economic growth in Kenya.

Regional spillovers across transitioning emerging and frontier equity markets: A multi-time scale wavelet analysis

The episodic wave of crises experienced across the global financial markets over the past two decades has raised questions surrounding the vulnerability of transitioning emerging and frontier equity markets to exogenous shocks. These markets, by design, have lacked the institutional or financial architecture supporting their capital base compared to more established markets. We make the initial attempt to examine four such stock markets (Saudi Arabia, UAE, South Africa and Israel). We perform multi-timescale analysis using wavelet-based time and frequency decompositions in order to investigate (i) whether the shocks transmitted were pure contagion or fundamental-based and (ii) also whether the dynamic evolution of stock market integration was mainly short-term or long-term. We find that prior to the 2008/09 US subprime crisis, the shocks generated pure contagion in contrast to the subprime crisis that reveals evidence supportive of fundamental-based contagion. Further, when exploring the dynamics of market integration, we find that integration strengthens over time as opposed to any immediate short-term outcome. This supports policies engendered to promote stock market resiliency and stability.

Market Microstructure Invariance: A Dynamic Equilibrium Model

We derive invariance relationships for a dynamic infinite-horizon model of market microstructure with risk-neutral informed trading, noise trading, market making, and endogenous production of information. Equilibrium prices follow a martingale with endogenously derived stochastic volatility. The invariance relationships for bet sizes and transaction costs are obtained under the assumption that the effort required to generate one discrete bet does not vary across securities and time. The invariance relationships for pricing accuracy and market resiliency require the additional assumption that private information has the same signal-to-noise ratio across markets. Since bets are based on the arrival of discrete chunks of information, the structural model describes how the invariance relationships reflect differences in the granularity of information flows across markets. The model links proportionality coefficients in invariance relationships to fundamental parameters.

Liquidity, resiliency and market quality around predictable trades: Theory and evidence

We extend the theory of strategic trading around a predictable liquidation by considering the role of market resiliency. Our model predicts that even a monopolist strategic trader improves market quality and increases liquidator proceeds if trades’ temporary price impacts are quickly reversed, and that competition among strategic traders strictly improves market quality. We provide related empirical evidence by studying prices, liquidity, and individual account trading activity around the large and predictable “roll” trades undertaken by a large exchange-traded fund (ETF). The evidence indicates narrower bid-ask spreads, greater order book depth, and improved resiliency on roll dates. We find that a larger number of individual trading accounts provide liquidity on roll dates, and do not find evidence of the systematic use of predatory strategies. On balance, the theory and evidence imply that traders supply liquidity to rather than exploit predictable trades in resilient markets.

High-frequency quoting, trading, and the efficiency of prices

We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross section of securities in the US. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk and a sharp decline in the cost of trading.

Quantifying the Effects of New Derivative Introduction on Exchange Volatility, Efficiency and Liquidity

Quantifying the Effects of New Derivative Introduction on Exchange Volatility, Efficiency and Liquidity

The Deal/Book Split Analysis: A New Method to Disentangle the Contribution to Market and Limit Orders in Any Price Change

In this paper we propose a method to breakdown any price change at any scale time into a book-driven component and a deal driven component.Following recent empirical researches on orderbook dynamics, we build an estimate of the most probable next traded price (i.e. price'' conditionally to the state of the orderbook). Thus we can identify the expected price changes due to trades (i.e. Deal part), or to orderbook changes without the support of any transaction (i.e. Book part).Qualitatively one may associate the Book'' component to market markers adjustments or to liquidity providers in general. The Deal'' component can be attributed to directional traders who consume liquidity. We also provide a quantitative analysis of auto correlation and pairwise correlation of each component, whose results comfort our first intuitions. Lastly we propose a new quantitative measure of market resiliency which can be estimated by the negative correlation of Book and Deal components.

High Frequency Quoting, Trading, and the Efficiency of Prices

We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross-section of securities in the U.S. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk, and a sharp decline in the cost of trading.

Predatory or Sunshine Trading? Evidence from Crude Oil ETF Rolls

We extend the theory of strategic trading around a predictable liquidation by considering the role of market resiliency. Our model predicts that even a monopolist strategic trader improves market quality and increases liquidator proceeds if trades’ temporary price impacts are quickly reversed. We provide related empirical evidence by studying prices, liquidity, and individual account trading activity around the large and predictable “roll” trades undertaken by the largest ETF tracking crude oil futures prices. The evidence indicates narrower bid-ask spreads, greater order book depth and improved resiliency on roll dates. We find that a larger number of individual trading accounts provide liquidity on roll dates, and do not find evidence of the systematic use of predatory strategies. On balance, the theory and evidence supports that strategic traders choose to provide liquidity to predictable trades in resilient markets. However, the large volume of trading associated with the roll transactions leads to substantial trade execution costs that average three percent per year.