标准 Va R方法的假设条件与实际数据之间存在较大的差距 ,而差距是必严重影响 Va R方法的实际应用效果 .为此 ,本文从实际数据的基本特征出发 ,讨论了 Va R方法在尖峰、胖尾分布中的计算公式 ,并使用该计算公式对我国证券市场的实际数...标准 Va R方法的假设条件与实际数据之间存在较大的差距 ,而差距是必严重影响 Va R方法的实际应用效果 .为此 ,本文从实际数据的基本特征出发 ,讨论了 Va R方法在尖峰、胖尾分布中的计算公式 ,并使用该计算公式对我国证券市场的实际数据进行了实证分析 .分析结果表明 ,推广的 Va R计算方法对证券市场风险预警有更可靠的揭示作用 .展开更多
The motivation of this paper is to show how to use the information from given distributions and to fit distributions in order to confirm models. Our examples are especially for disciplines slightly away from mathemati...The motivation of this paper is to show how to use the information from given distributions and to fit distributions in order to confirm models. Our examples are especially for disciplines slightly away from mathematics. One minor result is that standard deviation and mean are at most a more or less good approximation to determine the best Gaussian fit. In our first example we scrutinize the distribution of the intelligence quotient (IQ). Because it is an almost perfect Gaussian distribution and correlated to the parents’ IQ, we conclude with mathematical arguments that IQ is inherited only which is assumed by mainstream psychologists. Our second example is income distributions. The number of rich people is much higher than any Gaussian distribution would allow. We present a new distribution consisting of a Gaussian plus a modified exponential distribution. It fits the fat tail perfectly. It is also suitable to explain the old problem of fat tails in stock returns.展开更多
文摘标准 Va R方法的假设条件与实际数据之间存在较大的差距 ,而差距是必严重影响 Va R方法的实际应用效果 .为此 ,本文从实际数据的基本特征出发 ,讨论了 Va R方法在尖峰、胖尾分布中的计算公式 ,并使用该计算公式对我国证券市场的实际数据进行了实证分析 .分析结果表明 ,推广的 Va R计算方法对证券市场风险预警有更可靠的揭示作用 .
文摘The motivation of this paper is to show how to use the information from given distributions and to fit distributions in order to confirm models. Our examples are especially for disciplines slightly away from mathematics. One minor result is that standard deviation and mean are at most a more or less good approximation to determine the best Gaussian fit. In our first example we scrutinize the distribution of the intelligence quotient (IQ). Because it is an almost perfect Gaussian distribution and correlated to the parents’ IQ, we conclude with mathematical arguments that IQ is inherited only which is assumed by mainstream psychologists. Our second example is income distributions. The number of rich people is much higher than any Gaussian distribution would allow. We present a new distribution consisting of a Gaussian plus a modified exponential distribution. It fits the fat tail perfectly. It is also suitable to explain the old problem of fat tails in stock returns.
