New Economics Papers
on Market Microstructure
Issue of 2010‒01‒16
six papers chosen by
Thanos Verousis

  1. On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting By Julien Chevallier; Benoît Sévi
  2. Covariance estimation and dynamic asset allocation under microstructure effects via Fourier methodology By Mancino Maria Elvira; Simona Sanfelici
  3. Asset Market Liquidity Risk Management: A Generalized Theoretical Modeling Approach for Trading and Fund Management Portfolios By Al Janabi, Mazin A. M.
  4. Dynamic Pairs Trading Strategy For The Companies Listed In The Istanbul Stock Exchange By Bolgun, Evren; Kurun, Engin; Guven, Serhat
  5. Does Security Transaction Volume-Price Behavior Resemble a Probability Wave? By Leilei Shi
  6. The Effects of Stock Lending on Security Prices: An Experiment By Kaplan, Steven N.; Moskowitz, Tobias J.; Sensoy, Berk A.

  1. By: Julien Chevallier (Imperial College London); Benoît Sévi (University of Angers (GRANEM) and LEMNA)
    Abstract: The recent implementation of the EU Emissions Trading Scheme (EU ETS) in January 2005 created new financial risks for emitting firms. To deal with these risks, options are traded since October 2006. Because the EU ETS is a new market, the relevant underlying model for option pricing is still a controversial issue. This article improves our understanding of this issue by characterizing the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European Climate Exchange (ECX), which is valid during Phase II (2008-2012) of the EU ETS. The realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-distributions hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability. Our conclusions indicate that (i) the standard Brownian motion is not an adequate tool for option pricing in the EU ETS, and (ii) a jump component should be included in the stochastic process to price options, thus providing more efficient tools for risk-management activities.
    Keywords: CO2 Price, Realized Volatility, HAR-RV, GARCH, Futures Trading, Emissions Markets, EU ETS, Intraday data, Forecasting
    JEL: C5 G1 Q4
    Date: 2009–12
  2. By: Mancino Maria Elvira (Dipartimento di Matematica per le Decisioni, University of Firenze); Simona Sanfelici (Dipartimento di Economia, University of Parma)
    Abstract: We analyze the properties of different estimators of multivariate volatilities in the presence of microstructure noise, with particular focus on the Fourier estimator. This estimator is consistent in the case of asynchronous data and robust to microstructure effects; further we prove the positive semi-definiteness of the estimated covariance matrix. The in sample and forecasting properties of Fourier method are analyzed through Monte Carlo simulations. We study the economic benefit of applying the Fourier covariance estimation methodology over other estimators in the presence of market microstructure noise from the perspective of an asset-allocation decision problem. We find that using Fourier methodology yields statistically significant economic gains under strong microstructure effects
    Keywords: nonparametric covariance estimation, non-synchronicity, microstructure, optimal portfolio choice, Fourier analysis
    JEL: G11 C14 C22
    Date: 2009–12
  3. By: Al Janabi, Mazin A. M.
    Abstract: Asset market liquidity risk is a significant and perplexing subject and though the term market liquidity risk is used quite chronically in academic literature it lacks an unambiguous definition, let alone understanding of the proposed risk measures. To this end, this paper presents a review of contemporary thoughts and attempts vis-à-vis asset market/liquidity risk management. Furthermore, this research focuses on the theoretical aspects of asset liquidity risk and presents critically two reciprocal approaches to measuring market liquidity risk for individual trading securities, and discusses the problems that arise in attempting to quantify asset market liquidity risk at a portfolio level. This paper extends research literature related to the assessment of asset market/liquidity risk by providing a generalized theoretical modeling underpinning that handle, from the same perspective, market and liquidity risks jointly and integrate both risks into a portfolio setting without a commensurate increase of statistical postulations. As such, we argue that market and liquidity risk components are correlated in most cases and can be integrated into one single market/liquidity framework that consists of two interrelated sub-components. The first component is attributed to the impact of adverse price movements, while the second component focuses on the risk of variation in transactions costs due to bid-ask spreads and it attempts to measure the likelihood that it will cost more than expected to liquidate the asset position. We thereafter propose a concrete theoretical foundation and a new modeling framework that attempts to tackle the issue of market/liquidity risk at a portfolio level by combining two asset market/liquidity risk models. The first model is a re-engineered and robust liquidity horizon multiplier that can aid in producing realistic asset market liquidity losses during the unwinding period. The essence of the model is based on the concept of Liquidity-Adjusted Value-at-Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions. Conversely, the second model is related to the transactions cost of liquidation due to bid-ask spreads and includes an improved technique that tackles the issue of bid-ask spread volatility. As such, the model comprises a new approach to contemplating the impact of time-varying volatility of the bid-ask spread and its upshot on the overall asset market/liquidity risk.
    Keywords: Economic Capital; Emerging Markets; Financial Engineering; Financial Risk Management; Financial Markets; Liquidity Risk; Portfolio Management; Liquidity Adjusted Value at Risk
    JEL: G32
    Date: 2009–05–20
  4. By: Bolgun, Evren; Kurun, Engin; Guven, Serhat
    Abstract: In this research we performed pairs trading strategy based on a comparative mean reversion of asset prices with daily data over the period 2002 through 2008 in Istanbul Stock Exchange. We did not categorize stock pairs by sectors and therefore it is possible to observe mean reversion characteristics of different stocks that are selected from ISE-30 index. The initial formation period is 125 days (approx. 6 months) while we measure the performance results daily. Then both formation process and trading strategies have been structured as dynamic (rolling windows) market trading model through 2008. The results indicate that pairs produced average returns of % 3.36 daily comparing with the naïve buy and hold strategy. However ISE30 daily average return performance % 0.038 between 2002-2008 period. Our trading constraints and trading commissions take away the excess return on pairs mostly. Furthermore, the performance analysis reveals that the pairs trading strategy yields excess returns with less volatility than the market portfolio.
    Keywords: mean reversion; pairs trading; distance method; market neutral portfolio; Istanbul Stock Exchange; trading strategies
    JEL: G1 G11
    Date: 2009–10
  5. By: Leilei Shi (Department of Systems Science, School of Management, Beijing Normal University)
    Abstract: Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. To explain the volume-price behavior, we, in terms of physics, propose a transaction energy hypothesis, derive a time-independent transaction volume-price probability wave equation, and get two sets of analytical volume distribution eigenfunctions over a trading price range. By empiric test, we show the existence of coherence in stock market and demonstrate the model validation at this early stage. The volume-price behaves like a probability wave.
    Date: 2010–01
  6. By: Kaplan, Steven N. (University of Chicago); Moskowitz, Tobias J. (University of Chicago); Sensoy, Berk A. (Ohio State University)
    Abstract: Working with a sizeable (greater than $15 billion in assets) anonymous money manager, we exogenously shift the supply of lendable shares for certain stocks by randomly making available for lending 2/3 of the stocks in the manager's portfolio and withholding 1/3 of the stocks from the loan market. The lending program commenced in early September 2008 and the loans were recalled in mid-September 2008, with over $700 million of securities lent out at the peak of the study. During the lending (recall) period, returns to stocks randomly made available for lending were not lower (not greater) than returns to stocks randomly withheld from lending. Stocks randomly made available for lending experienced no differences in volatility, bid-ask spreads, or skewness than stocks randomly withheld from lending during either the lending or recall period. We find some evidence that loan supply increases volatilities and spreads for stocks with high short interest and expected loan spreads.
    Date: 2009–07

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