A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model

The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. The estimation of the model is difficult, since the unobservable surplus demand is a time-varying stochastic variable in the return equation, and the market liquidity arises both in the mean term and in the variance term of the return equation in the MM model. A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. Since the simulation smoother algorithm makes use of the band diagonal structure and positive definition of Hessian matrix of the logarithmic density, it can quickly draw the market liquidity. In addition, we discuss the MM model with Student-t heavy tail distribution that can be utilized to address the presence of outliers in typical financial time series. Using the presented modeling method to make analysis of daily income of the S&P 500 index through the point forecast and the density forecast, we find clear support for time-varying volatility, volatility feedback effect, market microstructure theory, and Student-t heavy tails in the financial time series. Through this method, one can use the estimated market liquidity and surplus demand which is much smoother than the strong stochastic return process to assist the transaction decision making in the financial market.

Does Information Asymmetry Affect Dividend Policy? Analysis Using Market Microstructure Variables

This study analyzes the relationship between information asymmetry and dividend policy in an emerging market, Korea. We adopt several proxies for information asymmetry, such as the Glosten–Harris and Hasbrouk–Foster–Viswanathan models, drawn from market microstructure literature. This study finds a negative relationship between information asymmetry and dividend yields, which appears to be particularly strong when firms have difficulty raising external capital because they have high systematic risk, financial constraints, or low stock liquidity. This result, based on an analysis using market microstructure variables that provide direct measures of information asymmetry, suggests that the pecking order theory holds for the Korean stock market and that information asymmetry is a strong determinant of dividend policy decisions in an emerging market.

Detecting factors of quadratic variation in the presence of market microstructure noise

A method of detecting latent factors of quadratic variation (QV) of Itô semimartingales from a set of discrete observations is developed when the market microstructure noise is present. We propose a new way to determine the number of latent factors of quadratic co-variations of asset prices based on the SIML (separating information maximum likelihood) method by Kunitomo et al. (Separating information maximum likelihood estimation for high frequency financial data. Springer, Berlin, 2018). In high-frequency financial data, it is important to investigate the effects of possible jumps and market microstructure noise existed in financial markets. We explore the estimated variance–covariance matrix of latent (efficient) prices of the underlying Itô semimartingales and investigate its characteristic roots and vectors of the estimated quadratic variation. We give some simulation results to see the finite sample properties of the proposed method and illustrate an empirical data analysis on the Tokyo stock market.

THE INFLUENCE OF ASYMMETRIC INFORMATION ON FOREIGN CAPITAL INFLOWS IN ASEAN PLUS THREE COUNTRIES

This paper examines the influence of asymmetric information on foreign capital inflows in ASEAN PLUS THREE (ASEAN+3) countries. Linking capital flows to stock market setting, it substantiates other efforts concerning the debatable issues of the effect of asymmetric information on foreign direct investment (FDI) and foreign portfolio investment (FPI). The asymmetric information is captured through the stock market microstructure perspective on the width and depth dimensions using highly frequency cross sectional data from year 2000 to 2015. Roll and Amivest models are employed to quantify the width and depth aspects of the asymmetric information. Employing the panel data technique, the results demonstrate the significant effect of market transparency on foreign capital inflows specifically the FDI as compared to the FPI. An increase in the width and depth analysis based on the Amivest model signifies a high informational transparency, thus shows a lower asymmetric information which consequently leads to the high foreign capital inflows. The results of the study provide information to the policymakers in monitoring capital inflows on the aspect of market transparency and highlight the importance of the stock market microstructure in assessing the asymmetric information for ASEAN+3 countries.