Risk estimation of CSI 300 index spot and futures in China from a new perspective
Risk estimation of CSI 300 index spot and futures in China from a new perspective
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DOI：
10.1016/j.econmod.2015.05.011
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发表时间：
2015-09
影响因子：
4.7
通讯作者：
Yuan Suo;Donghua Wang;Sai-Ping Li
中科院分区：
文献类型：
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作者：
Yuan Suo;Donghua Wang;Sai-Ping Li
本文研究了中国股票指数和股票指数期货市场高频收益率大于或小于给定正阈值的回归区间的统计行为及其在风险估计中的应用。通过研究复发区间的概率密度函数，我们发现正负发生阈值的对称分布，可以用拉伸指数函数拟合。概率密度函数进一步与平均间隔成比例，作为不同阈值的统一函数形式。进一步研究了条件概率密度函数和标度平均条件递归区间对前一个递归区间的依赖性，证明了递归区间中存在短记忆.去趋势波动分析的结果显示出长期相关性，其中去趋势波动函数衰减为指数函数，指数在0.5和1之间。基于递归区间分析的结果，我们构造了一个风险函数，并定义了损失概率，以评估金融市场中的风险。令人惊讶的是，在股票指数和期货市场的损失概率图中发现了交叉，这揭示了基于复杂金融市场的递归区间分析的风险价值（VaR）高估（低估）问题。这项研究将使人们能够改善风险估计，并有助于管理金融市场的风险。
We investigate the statistical behavior and application in risk estimation of recurrence intervals between high-frequency returns that are either larger than a given positive threshold or smaller than a negative threshold for the stock index and stock index futures markets in China. By studying the probability density function of recurrence intervals, we find symmetric profiles for both the positive and negative occurrence thresholds, which can be fitted with stretched exponential functions. The probability density function further scales with the mean interval as the unified functional form for different thresholds. We further study the dependence of the conditional probability density function and the scaled mean condition recurrence interval on the previous recurrence interval, and demonstrate the existence of short memory in recurrence intervals. The result from detrended fluctuation analysis exhibits long-term correlations, where the detrended fluctuation function decays as an exponential function, with an exponent between 0.5 and 1. Based on the results of the analysis of recurrence intervals, we construct a hazard function and define a loss probability in order to evaluate risk in financial markets. To our surprise, a crossover is found in the loss probability plot of the stock index and its futures market, which sheds light on the issue of value at risk (VaR) overestimation (underestimation) based on recurrence interval analysis of complex financial markets. The study would enable one to improve risk estimation and is useful for management of risks in financial markets.