Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

随机变量之间相依性的有关研究

随着copulas理论知识的完善，copulas用于相依性的研究也越来越多，从随机变量单调变换的角度出发，给出了在随机变量单调变换的情况下，它们之间的相依性以及相关性所发生的变化，进一步挖掘了copulas在相依性研究中的运用．     

基于连接函数的相关性度量

列出了几种不同含义的相关系数以及用连接函数计算这些相关系数的方法,并就使用相关系数时所存在的问题及评价的标准做了一些注记。     

基于Kendall’s τ的连接函数分布估计算法求解云计算中SaaS部署问题(英文)

分布估计算法的性能高度依赖于如何估计和采样概率分布,连接函数(Copula)是构建概率分布模型的强有力的工具.论文给出了一个基于Kendall’s τ的正态连接函数分布估计算法,算法通过估计Kendall’s τ和利用Kendall’s τ与相关矩阵的关系首先估计正态连接函数中的相关矩阵,由此估计出联合分布.然后,Cholesky分解算法被用于该矩阵生成新的个体.由于正态连接函数的简单性,使得该算法具有简单明了的优点.算法被应用于一些测试函数和云计算中的SaaS部署问题,实验结果表明了算法的有效性.     

Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis

We transform financial return series into its frequency and time domain via wavelet decomposition to separate short‐run noise from long‐run trends and assess the relevance of each frequency to value‐at‐risk (VaR) forecast. Furthermore, we analyze financial assets in calm and turmoil market times and show that daily 95% VaR forecasts are mainly driven by the volatility that is captured by the first scales comprising the short‐run information, whereas more timescales are needed to adequately forecast 99% VaR. As a result, individual timescales linked via copulas outperform classical parametric VaR approaches that incorporate all information available. Copyright © 2015 John Wiley & Sons, Ltd.