Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models

The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more accurate than those using the normal and t-distribution models. Thus, VaR using Realized Volatility models and skewed t-distribution enhances the performance of risk management in Korean financial markets.