New Economics Papers
on Market Microstructure
Issue of 2013‒12‒29
eleven papers chosen by
Thanos Verousis

  1. Reflecting on the VPN Dispute By Torben G. Andersen; Oleg Bondarenko
  2. Assessing Measures of Order Flow Toxicity via Perfect Trade Classification By Torben G. Andersen; Oleg Bondarenko
  3. A Monte Carlo method for optimal portfolio executions By Nico Achtsis; Dirk Nuyens
  4. The Cost of New Information – ECB Macro Announcement Impacts on Bid-Ask Spreads of European Blue Chips By Tobias R. Rühl; Michael Stein
  5. Asymptotic Inference about Predictive Accuracy Using High Frequency Data By Jia Li; Andrew J. Patton
  6. Coupled mode theory of stock price formation By Jack Sarkissian
  7. Individual Investors Repurchasing Behavior: Preference for Stocks Previously Owned By Cristiana Cerqueira Leal; Manuel J. Rocha Armada; Gilberto Loureiro
  8. Dynamic Copula Models and High Frequency Data By Irving Arturo De Lira Salvatierra; Andrew J. Patton
  9. On central bank interventions in the Mexican peso/dollar foreign exchange market By Santiago García-Verdú; Miguel Zerecero
  10. Does Realized Skewness Predict the Cross-Section of Equity Returns? By Diego Amaya; Peter Christoffersen; Kris Jacobs; Aurelio Vasquez
  11. Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures By Worapree Maneesoonthorn; Catherine S. Forbes; Gael M. Martin

  1. By: Torben G. Andersen (Northwestern University and CREATES); Oleg Bondarenko (University of Illinois at Chicago)
    Abstract: In Andersen and Bondarenko (2014), using tick data for S&P 500 futures, we establish that the VPIN metric of Easley, Lopez de Prado, and O'Hara (ELO), by construction, will be correlated with trading volume and return volatility (innovations). Whether VPIN is more strongly correlated with volume or volatility depends on the exact implementation. Hence, it is crucial for the interpretation of VPIN as a harbinger of market turbulence or as a predictor of short-term volatility to control for current volume and volatility. Doing so, we find no evidence of incremental predictive power of VPIN for future volatility. Likewise, VPIN does not attain unusual extremes prior to the flash crash. Moreover, the properties of VPIN are strongly dependent on the underlying trade classification. In particular, using more standard classification techniques, VPIN behaves in the exact opposite manner of what is portrayed in ELO (2011a, 2012a). At a minimum, ELO should rationalize this systematic reversal as the classification becomes more closely aligned with individual transactions. ELO (2014) dispute our findings. This note reviews the econometric methodology and the market microstructure arguments behind our conclusions and responds to a number of inaccurate assertions. In addition, we summarize fresh empirical evidence that corroborates the hypothesis that VPIN is largely driven, and significantly distorted, by the volume and volatility innovations. Furthermore, we note there is compelling new evidence that transaction-based classification schemes are more accurate than the bulk volume strategies advocated by ELO for constructing VPIN. In fact, using perfect classification leads to diametrically opposite results relative to ELO (2011a, 2012a).
    Keywords: VPIN, PIN, High-Frequency Trading, Order Flow Toxicity, Order Imbalance, Flash Crash, VIX, Volatility Forecasting
    JEL: G01 G14 G17
    Date: 2013–04–08
  2. By: Torben G. Andersen (Northwestern University and CREATES); Oleg Bondarenko (University of Illinois at Chicago)
    Abstract: The VPIN, or Volume-synchronized Probability of INformed trading, metric is introduced by Easley, Lopez de Prado and O'Hara (ELO) as a real-time indicator of order flow toxicity. They find the measure useful in predicting return volatility and conclude it may help signal impending market turmoil. The VPIN metric involves decomposing volume into active buys and sells. We use the best-bid-offer (BBO) files from the CME Group to construct (near) perfect trade classification measures for the E-mini S&P 500 futures contract. We investigate the accuracy of the ELO Bulk Volume Classification (BVC) scheme and find it inferior to a standard tick rule based on individual transactions. Moreover, when VPIN is constructed from accurate classification, it behaves in a diametrically opposite way to BVC-VPIN. We also find the latter to have forecast power for short-term volatility solely because it generates systematic classification errors that are correlated with trading volume and return volatility. When controlling for trading intensity and volatility, the BVC-VPIN measure has no incremental predictive power for future volatility. We conclude that VPIN is not suitable for measuring order flow imbalances.
    Keywords: VPIN, Accuracy of Trade Classification, Order Flow Toxicity, Order Imbalance, Volatility Forecasting
    JEL: G01 G12 G14 G17 C58
    Date: 2013–11–25
  3. By: Nico Achtsis; Dirk Nuyens
    Abstract: Traders are often faced with large block orders in markets with limited liquidity and varying volatility. Executing the entire order at once usually incurs a large trading cost because of this limited liquidity. In order to minimize this cost traders split up large orders over time. Varying volatility however implies that they now take on price risk, as the underlying assets' prices can move against the traders over the execution period. This execution problem therefore requires a careful balancing between trading slow to reduce liquidity cost and trading fast to reduce the volatility cost. R. Almgren solved this problem for a market with one asset and stochastic liquidity and volatility parameters, using a mean-variance framework. This leads to a nonlinear PDE that needs to be solved numerically. We propose a different approach using (quasi-)Monte Carlo which can handle any number of assets. Furthermore, our method can be run in real-time and allows the trader to change the parameters of the underlying stochastic processes on-the-fly.
    Date: 2013–12
  4. By: Tobias R. Rühl; Michael Stein
    Abstract: Bid-ask spreads using intraday data reveal significant sensitivity to European Central Bank (ECB) macro announcements. Effects are strongest for announcements that comprise unexpected information or a change in interest rates, and spreads rise sharply during the minutes surrounding interest rate or other important macroeconomic announcements by the ECB. Both Euro area stocks (of German DAX 30 and French CAC 40) and non-Euro stocks (of FTSE 100) have been used for comparative reasons. All results are robust to changes in specification and when being controlled for normal daytime-dependent frictions or other macroeconomic announcements.
    Keywords: Market microstructure; transaction costs; bid-ask spreads; ECB; announcement effects
    JEL: G14 G18 E52
    Date: 2013–11
  5. By: Jia Li; Andrew J. Patton
    Abstract: This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous-time process. We provide primitive conditions under which a "negligibility" result holds, and thus the asymptotic size of standard predictive accuracy tests, implemented using a high-frequency proxy for the latent variable, is controlled. An extensive simulation study verifies that the asymptotic results apply in a range of empirically relevant applications, and an empirical application to correlation forecasting is presented.
    Keywords: Forecast evaluation, realized variance, volatility, jumps, semimartingale
    JEL: C53 C22 C58 C52 C32
    Date: 2013
  6. By: Jack Sarkissian
    Abstract: We develop a theory of bid and ask price dynamics where the two prices form due to interaction of buy and sell orders. In this model the two prices are represented by eigenvalues of a 2x2 price operator corresponding to "bid" and "ask" eigenstates. Matrix elements of price operator fluctuate in time which results in phase jitter for eigenstates. We show that the theory reflects very important characteristics of bid and ask dynamics and order density in the order book. Calibration examples are provided for stocks at various time scales. Lastly, this model allows to quantify and measure risk associated with spread and its fluctuations.
    Date: 2013–12
  7. By: Cristiana Cerqueira Leal (Universidade do Minho - NIPE); Manuel J. Rocha Armada (Universidade do Minho - NIPE); Gilberto Loureiro (Universidade do Minho - NIPE)
    Abstract: In this paper we study the repurchasing behavior of individual investors and identify several characteristics (stock- and investor-specific) that affect the preference for repurchasing stocks previously owned. Using a unique database of 5,128 individual investors trading from August 1st, 2003 to July 31st, 2007, we find that investors prefer to repurchase stocks that are prior winners and those that dropped in price after being sold, in line with Strahilevitz, Odean and Barber (2011). We also find that the larger the prior gain, or the drop in stock price after the sell, the more likely is the investor to repurchase the same stock. Additionally, we find that (1) local stocks with negative market adjusted performance are more likely to be repurchased, and (2) less active, under-diversified, and poor performance investors are more likely to engage in such behavior. Overall, our results indicate that reference prices, prior stock returns, stock visibility, and investor performance and sophistication are determinants of the repurchasing behavior.
    Keywords: regret; counterfactuals; portfolio choice; individual investors
    JEL: G02 G11 G14
    Date: 2013
  8. By: Irving Arturo De Lira Salvatierra; Andrew J. Patton
    Abstract: This paper proposes a new class of dynamic copula models for daily asset returns that exploits information from high frequency (intra-daily) data. We augment the generalized autoregressive score (GAS) model of Creal, et al. (2012) with high frequency measures such as realized correlation to obtain a "GRAS" model. We find that the inclusion of realized measures significantly improves the in-sample fit of dynamic copula models across a range of U.S. equity returns. Moreover, we find that out-of-sample density forecasts from our GRAS models are superior to those from simpler models. Finally, we consider a simple portfolio choice problem to illustrate the economic gains from exploiting high frequency data for modeling dynamic dependence.
    Keywords: Realized correlation, realized volatility, dependence, forecasting, tail risk
    JEL: C32 C51 C58
    Date: 2013
  9. By: Santiago García-Verdú; Miguel Zerecero
    Abstract: In recent years the Bank of Mexico has made a series of rules-based interventions in the peso/dollar foreign exchange market. We assess the effectiveness of two specific interventions that occurred in periods of great stress for the Mexican economy. The aims of these two interventions were, respectively, to provide liquidity and to promote orderly conditions in the foreign exchange market. For our analysis, we follow the framework implemented by Dominguez (2003) and Dominguez (2006), an event-style microstructure approach. We use the bid-ask spreads as a measure of liquidity and of orderly conditions. In general, our results show no indication of an effect in the opposite direction from the one intended for the first intervention and are fairly conclusive regarding a significant reduction on the bid-ask spread for the second intervention.
    Keywords: foreign exchange rate, central bank interventions, microstructure
    Date: 2013–09
  10. By: Diego Amaya (University of Quebec at Montreal (UQUAM)); Peter Christoffersen (University of Toronto and CREATES); Kris Jacobs (University of Houston); Aurelio Vasquez (Instituto Tecnológico Autónomo de México (ITAM))
    Abstract: We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments? time-series and cross-sectional properties. We investigate if this week?'s realized moments are informative for the cross-section of next week'?s stock returns. We ?find a very strong negative relationship between realized skewness and next week?'s stock returns. A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 24 basis points with a t-statistic of 3.65. Our results on realized skewness are robust across a wide variety of implementations, sample periods, portfolio weightings, and firm characteristics, and are not captured by the Fama-French and Carhart factors. We ?find some evidence that the relationship between realized kurtosis and next week?'s stock returns is positive, but the evidence is not always robust and statistically significant. We do not find a strong relationship between realized volatility and next week?'s stock returns.
    Keywords: Realized volatility, skewness, kurtosis, equity markets, cross-section of stock returns
    JEL: G11 G12 G17
    Date: 2013–02–21
  11. By: Worapree Maneesoonthorn; Catherine S. Forbes; Gael M. Martin
    Abstract: This paper investigates the dynamic behaviour of jumps in financial prices and volatility. The proposed model is based on a standard jump diffusion process for price and volatility augmented by a bivariate Hawkes process for the two jump components. The latter process a joint dynamic structure for the price and volatility jump intensities, with the intensity of a volatility jump also directly affected by a jump in the price. The impact of certain aspects of the model on the higher-order conditional moments for returns is investigated. In particular, the differential effects of the jump intensities and the random process for latent volatility itself, are measured and documented. A state space representation of the model is constructed using both financial returns and non-parametric measures of integrated volatility and price jumps as the observable quantities. Bayesian inference, based on a Markov chain Monte Carlo algorithm, is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities. An extensive empirical investigation using data based on the S&P500 market index over a period ending in early-2013 is conducted. Substantial empirical support for dynamic jump intensities is documented, with predictive accuracy enhanced by the inclusion of this type of specification. In addition, movements in the intensity parameter for volatility jumps are found to track key market events closely over this period.
    Keywords: and phrases: Dynamic price and volatility jumps; Stochastic volatility; Hawkes process; Nonlinear state space model; Bayesian Markov chain Monte Carlo; Global financial cri-
    Date: 2013

This issue is ©2013 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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