Market Microstructure Effects Research Articles

Put–call parity in a crypto option market — Evidence from Binance

This paper investigates a special and already well-documented topic within market efficiency, the put–call parity relationship. The analysis is carried out to the cryptocurrency options market, where market microstructure effects are treated. A new concept of the forward and backward PCP breaking for European options is introduced. This is examined in detail for Ethereum options listed on Binance using a one-second resolution data set. The results show that significant put–call parity violations remain even after controlling for bid–ask spread, trading costs and funding costs.

The SEC’s Short-Sale Experiment: Evidence on Causal Channels and Reassessment of Indirect Effects

During 2005–2007, the Securities and Exchange Commission conducted a randomized trial in which it removed short-sale restrictions from one third of the Russell 3000 firms. Early studies found modest market microstructure effects of removing the restrictions but no effect on short interest, stock returns, volatility, or price efficiency. More recently, many studies have attributed a wide range of indirect outcomes to this experiment, mostly without assessing the causal channels for those outcomes. We examine the three most often cited causal channels for these indirect effects: short interest, returns, and managerial fear. We find no evidence to support these channels. We then reexamine the principal findings in four recent studies using a sample that closely matches the actual experiment and a common research design and find minimal support for the reported indirect effects. Our findings highlight the importance of confirming a causal channel or an economic mechanism and show that sample selection and specification choices can produce statistical significance even without an underlying economic mechanism. This paper was accepted by David Sraer, finance. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2023.4918 .

Relationship between housing, oil, gold and stock markets: evidence from UK and Norway

Purpose The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach. Design/methodology/approach This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2. Findings The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices. Research limitations/implications This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution. Practical implications These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices. Social implications These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators. Originality/value Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

Dissecting the idiosyncratic volatility anomaly

Abstract The idiosyncratic volatility (IVOL) anomaly, documented in Ang, et al. (2006), has garnered a great deal of attention in the literature. Yet questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise — such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.

ETFs’ high overnight returns: The early liquidity provider gets the worm

I examine the extent to which exchange-traded funds’ (ETFs) unusually high overnight returns are distorted by market microstructure effects; specifically, positive order imbalances and overnight increases in bid-ask spreads. Introducing a model that isolates these effects, I show that they artificially increase ETFs’ overnight returns by an average of over 6% annually. I find that the ETF market is prone to these distortions because its rapid growth is accompanied by order imbalances exceeding 10%. I provide detailed intraday statistics on order imbalances and spreads, and an example of an overnight investment strategy selecting ETFs susceptible to overnight biases.

Governance Effects of Corporate Bond Market Microstructure

I find that bond issuing entities reduce acquisition activity by approximately 22% following an exogenous increase in bond market exit threat. The treatment effect is less pronounced when an issuer is a reference entity in the CDS market and when bank monitoring is higher. The effect is more pronounced when the investment opportunities are higher, for firms with a greater short-selling threat, and for speculative-grade issuers. These findings suggest that bond market exit threat disciplines corporate risk-taking. Treatment firms do not change capital expenditures and R&D expenditures, implying that bond market disciplining is less effective when corporate risk-taking is less visible. However, treatment firms reduce shareholder payout and increase precautionary cash holdings. Collectively, the findings imply that the secondary corporate bond market influences issuing firms’ real decisions.

Flow Toxicity and Bank Equity Holdings

This paper investigates the market microstructure effects on client firms of equity holdings by relationship banks, i.e., lenders and/or underwriters, prior to the 2008 financial crisis. It intends to shed light on the need for “the Volcker Rule.” We find that banks’ equity holdings of corporate clients reduce flow toxicity and effective spreads surrounding earnings announcements. Such client firms also experience higher 3-day abnormal stock returns and better subsequent one-month stock performance. In contrast, firms with higher holdings by other independent institutional investors do not have robust lower flow toxicity and experience significantly lower abnormal returns during the 3-day and one month windows. The findings suggest that, on average, banks perform valuable market making services in the equity market and are compensated by modest profits.

Determinants of Implied Volatility Smiles - An Empirical Analysis Using Intraday DAX Equity Options

By extending and reviewing determinants of the implied volatility in the context of high frequency (HF) trade-by-trade DAX equity options from the EUREX a mean-reversion autocorrelation process is revealed, besides confirming low frequency results such as moneyness, time, liquidity, volume and underlying moment dependencies. Furthermore, we show, that the mean-reversion process is present, even if we control for fluctuating trades between bid and ask prices. It is induced by algorithmic market making and market microstructure effects. We address the HF research gap in market microstructure literature expressed by O’Hara (2015), who argues that markets and trading is radically different today, which consequently altered the basic constructs of market microstructure, and we give additional explanation for the flickering quote hypothesis of Hasbrouck and Saar (2009).

Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading

Estimating the covariance between assets using high frequency data is challenging due to market microstructure effects and asynchronous trading. In this paper we develop a multivariate realised quasi maximum likelihood (QML) approach, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error. Under stochastic volatility the resulting realised QML estimator is positive definite, uses all available data, is consistent and asymptotically mixed normal. The quasi-likelihood is computed using a Kalman filter and optimised using a relatively simple EM algorithm. We also propose an alternative estimator using a factor model, which scales well with the number of assets. We derive the theoretical properties of these estimators and prove that they achieve the efficient rate of convergence. Our estimators are also analysed using Monte Carlo methods and applied to equity data with varying levels of liquidity.

Model-based Measurement of Actual Volatility in High-frequency Data

In this paper we aim to measure actual volatility within a model-based framework using high-frequency data. In the empirical finance literature it is known that tick-by-tick prices are subject to market micro-structure such as bid-ask bounces and trade information. Such market micro-structure effects become more and more apparent as prices or returns are sampled at smaller and smaller time intervals. High-frequency returns are used for the computation of realised volatility. Recent theoretical results have shown that realised volatility is a consistent estimator of actual volatility but when it is subject to micro-structure noise, the estimator diverges. Parametric and nonparametric methods can be adopted to account for the micro-structure bias. Here we measure actual volatility using a model that takes account of micro-structure noise together with intra-daily volatility patterns and stochastic volatility. The coefficients of this model are estimated by maximum likelihood methods that are based on importance sampling techniques. It is shown that such Monte Carlo techniques can be employed successfully for our purposes in a feasible way. As far as we know, this is a first serious attempt to model the basic components of the mean and variance of high-frequency prices simultaneously. An illustration is given for three months of tick-by-tick transaction prices of the IBM stock traded at the New York Stock Exchange.

Risk-Return Optimisations Can Lead to Favourite-Longshot Bias in Prediction Markets Operated by Bookmakers

In this paper we propose a new explanation for Favourite-Longshot Bias (FLB). FLB refers to a well known phenomenon, pervasive in many sport betting markets. As measured by historically realised gains, bets on favourites appear to be more favourable than longshot bets. We show that FLB can be caused by intentional adjustments of odds, resulting from the process of risk-return optimisations by bookmakers’, aimed at minimising risk and maximising profits. Research literature offered so far a number of plausible explanations for this effect. They can be divided into two groups. The first group, applicable to parimutuel betting markets, is focused on risk preferences and behaviour of punters. The second group, applicable to markets dominated by bookmakers, considers the market micro-structure effects like power of bookmakers and limited arbitrage. In this paper we propose an alternative explanation that is applicable to betting markets operated by bookmakers. Our model is based on two basic assumptions: (1) Bookmaker’s essential task is to maximise gains and to minimise risks; and (2) Punters’ demands for particular bets are increasing functions of their Von Neuman - Morgenstern expected utilities. Using these assumptions we formulate, discuss and solve the risk-return optimisation problem. The analytical solution to this problem is a set of optimal odds, with embedded adjustments that are shown to lead to FLB. The predictions of our model agree qualitatively well with empirical observations.

Option Market Characteristics and Price Monotonicity Violations

This study reexamines whether option price monotonicity properties hold in a liquid market with little market friction and considers the validity of the monotonicity properties in light of option market characteristics. We confirm that option prices do not monotonically correlate with underlying spot prices and that call and put prices often increase or decrease together, indicating that the monotonicity properties are not consistent with the observed option price dynamics. The violations of monotonicity properties are associated with not only market microstructure effects, trade sizes, and option leverage but also other market characteristics such as trade direction, individual investor demand, foreign investor trading, and market volatility. Violation occurrences tend to be clustered, and their relationship with option market characteristics has not been affected by the recent market reform. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 37:473–498, 2017

Considering all microstructure effects: The extension of a trade indicator model

By considering various market microstructure effects, this letter proposes a comprehensive trade indicator model incorporating trade duration, order sizes, bid–ask spreads, and market depth into a unified framework. Examining the intraday price behavior of the KOSPI200 futures market, we find that (i) fast trading indicates informed trading, (ii) stealth trading does not prevail, (iii) order-processing costs reach economies of scale, and (iv) liquidity significantly affects investors’ order submission decisions in the highly liquid market.

A noisy principal component analysis for forward rate curves

Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non-trivial observational errors arise in any statistical analysis. In this work, we point out that the classical PCA analysis is not suitable for estimating factors of forward rate curves due to the presence of measurement errors induced by market microstructure effects and numerical interpolation. Our analysis indicates that the PCA based on the long-run covariance matrix is capable to extract the true covariance structure of the forward rate curves in the presence of observational errors. Moreover, it provides a significant reduction in the pricing errors due to noisy data typically found in forward rate curves.

Short-term determinants of the idiosyncratic sovereign risk premium: A regime-dependent analysis for European credit default swaps

This study investigates the dynamics of the sovereign CDS term premium, i.e. difference between 10Y and 5Y CDS spreads. It can be regarded a forward-looking measure of idiosyncratic sovereign default risk as perceived by financial markets. For some European countries this premium featured distinct nonstationary and heteroskedastic pattern during the last years. Using a Markov-switching unobserved component model, we decompose the daily CDS term premium of five European countries into two unobserved components of statistically different nature and link them in a vector autoregression to various daily observed financial market variables. We find that such decomposition is vital for understanding the short-term dynamics of this premium. The strongest impacts can be attributed to CDS market liquidity, local stock returns, and overall risk aversion. By contrast, the impact of shocks from the sovereign bond market is rather muted. Therefore, the CDS market microstructure effect and investor sentiment play the main roles in sovereign risk evaluation in real time. Moreover, we also find that the CDS term premium response to shocks is regime-dependent and can be ten times stronger during periods of high volatility.

Overnight News and Daily Equity Trading Risk Limits

This paper proposes a new bivariate modeling approach for setting daily equity-trading risk limits using high-frequency data. We construct one-day-ahead Value-at-Risk (VaR) forecasts by taking into account the different dynamics of the overnight and daytime return processes and their covariance. The covariance is motivated by market microstructure effects such as price staleness and news spillover. Among the competitors we include a simpler bivariate model where the overnight return is redefined by moving the open price further into the trading day, and a univariate model based on the close-to-close return and an overnight-adjusted realized volatility. We illustrate the different approaches using data on the S&P 500 and Russell 2000 indices. The evidence in favour of modeling the covariance is more convincing for the latter index due to the lower trading volumes and, relatedly, the less efficient price discovery at market open for small-cap stocks.

Ex-dividend day abnormal returns for special dividends

Past research shows that the difference between dividend amount and ex-dividend day price drop reflects the transaction costs and the differential in the tax rates on dividends and capital gains. Moreover, it is also documented that the higher the dividend yield, the lower is the ex-dividend day return. This paper focuses on large special dividends and tests the two competing hypothesis, tax hypothesis and short term trading hypothesis. Our focus on large special dividends is motivated by the following three considerations. First, special dividends have experienced a surge in recent years. Second, special dividends are important for dividend capture by institutions, corporations and arbitragers. Third, using a sample of large special dividends allows us to reduce the market microstructure effects and focus more directly on the two competing hypotheses. Based on a sample of large special dividends, we find that price drop on ex-dividend day is significantly less than the dividend amount. Furthermore, we show that ex-dividend day returns are positive and hence, are not fully arbitraged away. Our tests indicate that tax hypothesis explains some portion of ex-dividend day abnormal returns even for large special dividends, whereas the support for the short-term trading hypothesis is weak.

Determinantes de la tasa de cambio en Colombia: un enfoque de microestructura de mercados

This work seeks to provide some additional elements in understanding the dynamics of the exchange rate in Colombia. First, it uses information from a survey directed to the exchange rate market players. It highlights the difference exhibited by market participants about the determinants of the exchange rate in the short, medium and long term. In the first case, the determinants respond primarily to market microstructure effects while in the medium and long term, the exchange rate is mainly drivenby its economic fundamentals. Agents also highlighted the increasing importance of international factors with respect to the premises in determining the dynamics of this variable. This study focuses specifically on the determinants of short-term of the exchange rate where the microstructure elements take greater relevance. To this end, a theoretical model developed by Kyle (1985) is presented, which highlights the importance of microstructure factors in determining the exchange rate in a scenario of asymmetric information and strategic agents. Based on the identification of these microstructure determinants, an empirical model was estimated using information downloaded directly from intraday trading screens. This model is used to corroborate some of the survey results. In particular, it validates the increasing importance of international factors with respect to local factors, as well as the impact of the intervention of the Central Bank on the dynamics of the exchange rate in Colombia.

Electricity Spot and Derivatives Pricing When Markets are Interconnected

Increasing interconnectivity between electricity wholesale markets requires an efficient allocation scheme in order to provide access to scarce cross-border transmission capacities. In both the US and Europe, existing schemes have primarily induced economically inefficient interconnector use given that flows have to be nominated prior to spot market clearing. By contrast, the market coupling mechanisms recently rolled out in parts of Europe avoid these inefficiencies by implicitly allocating cross-border transmission capacity upon spot market clearance. In this paper, we show that these institutional aspects of market design clearly manifest in the empirical dynamics of both electricity spot and derivatives prices, and hence, do have important implications for pricing and hedging in these markets. Since traditional reduced-form models fail to reproduce such effects of market microstructure, we employ a fundamental multi-market model for electricity pricing in order to analyze how the key stylized facts of electricity prices are impacted by the different allocation schemes.

Obtaining and Predicting the Bounds of Realized Correlations

The problem of estimation of realized correlation, which is analogous to realized covariance, is compounded by effects of market microstructure, noise, and asynchronous trading. Various methods have been proposed to decrease the biases, but they require assumptions to be made that may be unrealistic. This paper argues that the inherent data problems make precise point identification of realized correlation difficult, but identification bounds in the spirit of Manski (1995) can be derived. These identification bounds allow for a more robust approach to inference, especially when the realized correlation is used for estimating other risk measures. We forecast the identification bounds using the HAR model of Corsi (2003) using data during the year of onset of the credit crisis, and find that the bounds provide good predictive coverage of the realized correlation for both one step and ten step forecasts, even in volatile periods.