Drawdown-based risk indicators for high-frequency financial volumes
Abstract 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-02-01
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| Series: | Financial Innovation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40854-023-00593-0 |
| _version_ | 1797273517867139072 |
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| author | Guglielmo D’Amico Bice Di Basilio Filippo Petroni |
| author_facet | Guglielmo D’Amico Bice Di Basilio Filippo Petroni |
| author_sort | Guglielmo D’Amico |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T14:45:30Z |
| format | Article |
| id | doaj.art-3c18b2597ced44b69dac3ae2bd5baca3 |
| institution | Directory Open Access Journal |
| issn | 2199-4730 |
| language | English |
| last_indexed | 2024-03-07T14:45:30Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj.art-3c18b2597ced44b69dac3ae2bd5baca32024-03-05T20:01:35ZengSpringerOpenFinancial Innovation2199-47302024-02-0110114010.1186/s40854-023-00593-0Drawdown-based risk indicators for high-frequency financial volumesGuglielmo D’Amico0Bice Di Basilio1Filippo Petroni2Department of Economics, Gabriele D’Annunzio University of Chieti-PescaraDepartment of Economics, Gabriele D’Annunzio University of Chieti-PescaraDepartment of Economics, Gabriele D’Annunzio University of Chieti-PescaraAbstract 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.https://doi.org/10.1186/s40854-023-00593-0Drawdown-based measuresHigh-frequency financial volumesSemi-Markov modelRight censoringChi-square independence testGoodness-of-fit test |
| spellingShingle | Guglielmo D’Amico Bice Di Basilio Filippo Petroni Drawdown-based risk indicators for high-frequency financial volumes Financial Innovation Drawdown-based measures High-frequency financial volumes Semi-Markov model Right censoring Chi-square independence test Goodness-of-fit test |
| title | Drawdown-based risk indicators for high-frequency financial volumes |
| title_full | Drawdown-based risk indicators for high-frequency financial volumes |
| title_fullStr | Drawdown-based risk indicators for high-frequency financial volumes |
| title_full_unstemmed | Drawdown-based risk indicators for high-frequency financial volumes |
| title_short | Drawdown-based risk indicators for high-frequency financial volumes |
| title_sort | drawdown based risk indicators for high frequency financial volumes |
| topic | Drawdown-based measures High-frequency financial volumes Semi-Markov model Right censoring Chi-square independence test Goodness-of-fit test |
| url | https://doi.org/10.1186/s40854-023-00593-0 |
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