Using the Markowitz mean-variance portfolio optimization theory, researchers have shown that the traditional estimated return greatly overestimates the theoretical optimal return, especially when the dimension to sample size ratio p/n is large. Bai, Liu, and Wong (2009) propose a...

This paper proposes the spectral corrected methodology to estimate the Global Minimum Variance Portfolio (GMVP) for the high dimensional data. In this paper, we analysis the limiting properties of the spectral corrected GMVP estimator as the dimension and the number of the sample set increase to...

In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under the conventional Bayesian estimation method for SV...

This paper considers the portfolio problem for high dimensional data when the dimension and size are both large.We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main factor to...

This paper extends the work of Korkie and Turtle (2002) by first proving that the traditional estimate for the optimal return of self-financing portfolios always over-estimates from its theoretic value. To circumvent the problem, we develop a Bootstrap estimate for the optimal return of...

Bai, et al. (2011c) develop the mean-variance-ratio (MVR) statistic to test the performance among assets for small samples. They provide theoretical reasoning to use MVR and prove that our proposed statistic is uniformly most powerful unbiased. In this paper we illustrate the superiority of our...

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to be seriously departed from its theoretic value. We prove that this phenomenon is natural and the estimated optimal return is always larger than its theoretic parameter. Thereafter, we develop...

In this paper, we introduce a new Bayesian approach to explain some market anomalies. We first develop some properties on the expected earnings shock and its volatility and establish some properties of investors' behavior on the stock price and its volatility during a financial crisis and...

Lam, et al. (2010, 2012) and Guo, et al. (2015) have developed a new Bayesian approach to explain some market anomalies. In this paper we conduct a survey to examine whether the theory developed in Lam, et al. (2010, 2012) and Guo, et al. (2015) holds in the empirically by studying the behavior...

Lam, et al. (2010, 2012) and Guo, et al. (2015) have developed a new Bayesian approach to explain some market anomalies. In this paper we conduct a survey to examine whether the theory developed in Lam, et al. (2010, 2012) and Guo, et al. (2015) holds empirically by studying the behavior of...