On Distribution of the Stock Market Risk with a Maximum Drawdown of a Wiener Process
Keywords:
Statistical physics distributions, multivariate distribution, Maximum drawdown distribution of a Wiener process, Basic statistical properties, Stock market riskAbstract
In this article, we investigate the Wiener stock market risk's maximum drawdown distribution. The danger of stochastic volatility in stock prices can be reduced by taking into account the most reliable and accurate decisions using this distribution. We extract various significant dependability aspects of this distribution, including the hazard and inverted hazard rate functions, in addition to presenting the closed-form pricing formula, which demonstrated the precise maximum drawdown distribution of the price path from the perspective of mathematical analysis. Additionally, a Wiener stock market risk's estimated value is calculated. This predicted value can be used to forecast future risk. Furthermore, we present a multivariate distribution of a maximum drawdown for an m-dimensional Wiener process and its key reliability aspects when this risk depends on the n separate and distinct primary stock market risks.
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