Asset Modeling with Non-Gaussian Innovation and Applications to Asset Allocation

• Main Authors: Chen, Hsuan Yu, 陳炫羽
• Other Authors: Huang, Hong Chih
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/46293940112911143077

碩士 === 國立政治大學 === 風險管理與保險研究所 === 101 === Since the stock markets always have the characteristics of heavy-tailness, skewness and kurtosis and there exists tail dependence among the international stock markets, we can’t use the Gaussian distribution as our model. Recently, the generalized hyperbolic (GH) distribution has been suggested to fit the single stock returns. This article will use the multivariate affine JD (MAJD), multivariate affine variance gamma (MAVG) and multivariate affine normal inverse Gaussian (MANIG) distributions to construct the risky asset returns, and apply them to asset allocation. After constructing the risky asset returns, we provide two different forms of portfolio and obtain the mean, variance, skewness, kurtosis of portfolio. We can try to select the optimal weights of portfolio by using the mean, variance, skewness, kurtosis of portfolios as our objective functions. To make our asset allocation more dynamic and efficient, we re-estimate all parameters for our models, select the optimal weights of portfolio, and re-assess the optimal asset allocation at each decision date. Empirically, when the performances of stock markets are good, we suggest that our asset allocation uses the skewness as the objective function. When the performances of stock markets are not good, we suggest that our asset allocation uses the variance as the objective function.