In stock markets, trading volumes serve as a crucial variable, acting as a measure for a security’s liquidity level. To evaluate liquidity risk exposure, we examine the process of volume drawdown and measures of crash-recovery within fluctuating time frames. These moving time windows shield our financial indicators from being affected by the massive transaction volume, a characteristic of the opening and closing of stock markets. The empirical study is conducted on the high-frequency financial volumes of Tesla, Netflix, and Apple, spanning from April to September 2022. First, we model the financial volume time series for each stock using a semi-Markov model, known as the weighted-indexed semi-Markov chain (WISMC) model. Second, we calculate both real and synthetic drawdown-based risk indicators for comparison purposes. The findings reveal that our risk measures possess statistically different distributions, contingent on the selected time windows. On a global scale, for all assets, financial risk indicators calculated on data derived from the WISMC model closely align with the real ones in terms of Kullback–Leibler divergence.

中文翻译：

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基于回撤的高频金融量风险指标

在股票市场中，交易量是一个关键变量，是衡量证券流动性水平的指标。为了评估流动性风险敞口，我们研究了波动时间范围内交易量下降的过程和崩溃恢复的措施。这些移动的时间窗口使我们的财务指标免受大量交易量的影响，而交易量是股票市场开盘和收盘的一个特征。该实证研究是对特斯拉、Netflix 和苹果公司 2022 年 4 月至 9 月的高频金融交易量进行的。首先，我们使用半马尔可夫模型（称为加权模型）对每只股票的金融交易量时间序列进行建模。 -索引半马尔可夫链（WISMC）模型。其次，我们计算实际和合成的基于回撤的风险指标以进行比较。研究结果表明，我们的风险衡量指标在统计​​上具有不同的分布，具体取决于所选的时间窗口。在全球范围内，对于所有资产，根据 WISMC 模型数据计算的金融风险指标在 Kullback-Leibler 散度方面与真实情况非常吻合。