Portfolio optimization based on GARCH-EVT-Copula forecasting models

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-$t$ and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-$t$ copulas, are best at reducing the portfolio risk.     

我国黄金期货市场价格波动研究

以上海期货交易所2008年1月9日至2011年6月30日的黄金期货市场量价关系为研究对象,构建了GARCH(异方差)族模型检验我国黄金期货市场的价格波动特征.研究结果表明,当分别引入黄金期货市场的交易量和持仓量时,交易量对价格波动的影响在当期和滞后期均十分显著,而持仓量对价格波动的影响在当期显著,滞后期则不显著;当同时引入黄金期货市场交易量和持仓量时,他们对价格波动的解释作用增加,且当期统计检验显著,滞后期效应则不明显.     

基于CARR模型和GARCH模型的股市波动性预测研究

金融资产波动性建模和预测是金融理论与实践中的一个重要课题,已经有了许多建模与预测方法。本文利用我国股市的高频数据进行实证研究,分别运用CARP,模型和GARCH模型进行波动性预测,进而对两个模型的预测能力进行对比,结果表明：CARP,模型在波动性预测方面比GARCH模型的效果更好。     

汽车行业股价波动性的实证研究——基于GARCH类模型

近年来，我国汽车产业发展迅猛，汽车股价波动日趋复杂。鉴于GARCH模型能够较好地拟合时间序列的尖峰厚尾特征．本文采集了2008年1月1日至2009年11月30日共计1260个道琼斯第一财经中国600汽车和零件行业领先指数的数据，并将其分为熊市牛市两个阶段．运用GARCH类模型进行实证分析，证实了我国汽车行业股价具有尖峰厚尾、显著异于正态分布的特征，说明了两阶段股市中均存在杠杆效应，同时得出了收益随风险的增加而增加的现象在熊市中更加明显。     

STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG‐RUN RESTRICTIONS VIA HETEROSKEDASTICITY

Long‐run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just‐identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity models. Using changes in volatility for checking long‐run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.     

Impacts of derivative markets on spot market volatility and their persistence

In this article, we investigate the impacts of futures and options markets on the volatility of the underlying market. Unlike earlier studies, the focus is on their persistence over time. Tests on the Hang Seng index yield several interesting results that often contrast with previous findings. Empirical results suggest that the quality of new information generated by derivative trading determines the impacts on the spot market volatility. The futures market provides new, material information reducing spot market volatility. The Options market, on the other hand, generates noisy information and distorts price, which is followed by an increase in volatility and a decrease in its sensitivity to price change. While the impact of futures persists, that of options mostly disappears as the market matures. Our conjecture is that the futures market is mainly driven by informed, experienced participants, while the options market attracts new, inexperienced investors.     

基于VaR修正理论的投资组合优化模型

根据风险价值VaR的计算思路,本文提出了基于GARCH理论的风险计量投资组合优化模型;同时在修正的VaR——尾条件期望的基础上对证券组合投资的优化模型做了进一步的改进。     

Multiperiod hedging in the presence of stochastic volatility

In this paper, we characterize the multiperiod minimum-risk hedge strategy within the stochastic volatility (SV) framework and compare it to other hedge strategies on the basis of hedging performance. Using crude oil markets as an example, we demonstrate that the SV model is appropriate in depicting price behaviour. However, ex ante and ex post comparisons indicate that the SV strategy is inferior to conventional hedging strategies. There is also evidence that the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) strategy may be better than the SV strategy, at least in terms of variance reduction.     

Uncertainty and Export Performance: Evidence from 18 Countries

We study a sample of nine developed and nine developing countries to evaluate the questions of how foreign income uncertainty and real exchange rate (RER) uncertainty impact international trade and how those impacts vary according to stage of development. RER uncertainty has a negative and significant impact on export growth for six of the nine less developed countries in our sample, while it has an insignificant effect for a majority of the developed countries. In both groups, foreign income uncertainty has a more pervasively significant (and frequently larger) influence on trade than does RER uncertainty.     

Varying the VaR for unconditional and conditional environments

Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from 12 European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates accounting for volatility clustering are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law. The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.