Description:
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 fuctuating time frames. These moving time windows shield our fnancial indicators from being afected by the massive transaction volume, a characteristic of the opening and closing of stock markets. The empirical study is conducted on the high-frequency fnancial volumes of Tesla, Netfix, and Apple, spanning from April to September 2022. First, we model the fnancial 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 fndings reveal that our risk measures possess statistically diferent distributions, contingent on the selected time windows. On a global scale, for all assets, fnancial risk indicators calculated on data derived from the WISMC model closely align with the real ones in terms of Kullback-Leibler divergence.