New Economics Papers
on Market Microstructure
Issue of 2010‒05‒15
six papers chosen by
Thanos Verousis

  1. Spot Variance Path Estimation and its Application to High Frequency Jump Testing By Charles S. Bos; Pawel Janus; Siem Jan Koopman
  2. Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data By Cathy Ning, Dinghai Xu, Tony Wirjanto
  3. A Multi Agent Model for the Limit Order Book Dynamics By Marco Bartolozzi
  4. Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model: A Realized Volatility Approach By Dinghai Xu, Yuying Li
  5. A Threshold Stochastic Volatility Model with Realized Volatility By Dinghai Xu
  6. Do Jumps Matter? Forecasting Multivariate Realized Volatility Allowing for Common Jumps By Yin Liao; Heather Anderson; Farshid Vahid

  1. By: Charles S. Bos (VU University Amsterdam); Pawel Janus (VU University Amsterdam); Siem Jan Koopman (VU University Amsterdam)
    Abstract: This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used to extend an existing high frequency jump test statistic, to detect arrival times of jumps and to obtain distributional characteristics of detected jumps. The effectiveness of our approach is explored through Monte Carlo simulations. It is shown that sparse sampling for mitigating the impact of microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high frequency price observations that are contaminated with microstructure noise without the need for sparse sampling, say at fifteen minute intervals. An empirical illustration is presented for the intraday EUR/USD exchange rates. Our main finding is that fewer jumps are detected when sampling intervals increase.
    Keywords: high frequency; intraday periodicity; jump testing; leverage effect; microstructure noise; pre-averaged bipower variation; spot variance
    JEL: C12 C13 C22 G10 G14
    Date: 2009–12–04
  2. By: Cathy Ning, Dinghai Xu, Tony Wirjanto (Department of Economics, University of Waterloo)
    Abstract: Volatility clustering is a well-known stylized feature of financial asset returns. In this paper, we investigate the asymmetric pattern of volatility clustering on both the stock and foreign exchange rate markets. To this end, we employ copula-based semi-parametric univariate time-series models that accommodate the clusters of both large and small volatilities in the analysis. Using daily realized volatilities of the individual company stocks, stock indices and foreign exchange rates constructed from high frequency data, we find that volatility clustering is strongly asymmetric in the sense that clusters of large volatilities tend to be much stronger than those of small volatilities. In addition, the asymmetric pattern of volatility clusters continues to be visible even when the clusters are allowed to be changing over time, and the volatility clusters themselves remain persistent even after forty days.
    JEL: C51 G32
    Date: 2010–01
  3. By: Marco Bartolozzi
    Abstract: In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The model follows a "zero intelligence" approach where the actions of the traders are related to a stochastic variable, the market sentiment, which we define as a mixture of public and private information. The model, despite the parsimonious approach, is able to reproduce several empirical features of the high-frequency dynamics of the market microstructure not only related to the price movements but also to the deposition of the orders in the book.
    Date: 2010–05
  4. By: Dinghai Xu, Yuying Li (Department of Economics, University of Waterloo)
    Abstract: Increasing attention has been focused on the analysis of the realized volatility, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data (from 1-min to 60-min). Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration.Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency. The volatility persistence becomes weaker at the lower sampling frequency. We also find that the consistent-scaling and "optimal"-weighted realized volatility method proposed by Hansen and Lunde (2005) provide relatively better performances compared to other methods considered. Length: 26 pages
    JEL: C01 C51
    Date: 2010–05
  5. By: Dinghai Xu (Department of Economics, University of Waterloo)
    Abstract: Rapid development in the computer technology has made the financial transaction data visible at an ultimate limit level. The realized volatility, as a proxy for the "true" volatility, can be constructed using the high frequency data. This paper extends a threshold stochastic volatility specification proposed in So, Li and Lam (2002) by incorporating the high frequency volatility measures. Due to the availability of the volatility time series, the parameters estimation can be easily implemented via the standard maximum likelihood estimation (MLE) rather than using the simulated Bayesian methods. In the Monte Carlo section, several mis-specification and sensitivity experiments are conducted. The proposed methodology shows good performance according to the Monte Carlo results. In the empirical study, three stock indices are examined under the threshold stochastic volatility structure. Empirical results show that in different regimes, the returns and volatilities exhibit asymmetric behavior. In addition, this paper allows the threshold in the model to be flexible and uses a sequential optimization based on MLE to search for the "optimal" threshold value. We find that the model with a flexible threshold is always preferred to the model with a fixed threshold according to the log-likelihood measure. Interestingly, the "optimal" threshold is found to be stable across different sampling realized volatility measures.
    JEL: C01 C51
    Date: 2010–05
  6. By: Yin Liao; Heather Anderson; Farshid Vahid
    Abstract: Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models.
    JEL: C13 C32 C52 C53 G32
    Date: 2010–05

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