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...

Barberis, Shleifer and Vishny (1998) and others have developed Bayesian models to explain investors' behavioral biases by using the conservatism heuristics and the representativeness heuristics in making decisions. To extend their work, Lam, Liu, and Wong (2010) have developed a model of weight...

In this paper, we introduce a new pseudo-Bayesian model to incorporate the impact of a financial Crisis and establish some properties of stock returns and investors' behaviors during the financial crisis and during recovery after crisis. Our proposed model can be applied to investigate some...

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 is large. Bai, Liu, and Wong (2006,2009a,b) propose...

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us 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...