Minseok Shin
Pohang University of Science and Technology · 経済学
研究室紹介
Professor Minseok Shin's research lab specializes in statistical modeling and econometrics for high-frequency financial data, with a focus on volatility estimation, heavy-tailed distributions, and dynamic factor models. The lab develops robust statistical methods to handle the heavy-tailed and heterogeneous nature of financial returns, particularly in large-dimensional volatility matrices. Key research directions include modeling overnight and intraday volatility dynamics using diffusion processes, and proposing advanced estimation techniques such as penalized optimization and truncation schemes for high-dimensional, non-Gaussian financial data. The lab's work bridges theoretical statistics with practical financial applications, aiming to improve risk management and market microstructure analysis.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
11Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot account for the heavy-tail phenomenon of stock-returns. Recently, a robust estimator was developed to handle heavy-tailed distributions with some bounded fourth-moment assumption. However, we often observe th
Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this article, to account for whole-day market dynamics, we propos
This paper introduces a novel process for both factor and idiosyncratic volatility matrices whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR (FIVAR) model. The FIVAR model accounts for the dynamics of the factor and idiosyncratic volatilities and includes many parameters. In addition, many empirical studies have shown that high-frequency stock returns and volatilities often exhibit heavy tails. To handle these two problems simultaneousl