Drawdown-based risk indicators for high-frequency financial volumes  

作　　者：Guglielmo D’Amico Bice Di Basilio Filippo Petroni 

机构地区：[1]Department of Economics,Gabriele D’Annunzio University of Chieti-Pescara,Pescara,Italy

出　　处：《Financial Innovation》2024年第1期2538-2577,共40页金融创新(英文)

摘　　要：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.

关 键 词：Drawdown-based measures High-frequency financial volumes Semi-Markov model Right censoring Chi-square independence test Goodness-of-ft test Kullback-Leibler divergence 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]