Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets
Forecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is buil...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/7/789 |
| _version_ | 1797561965802946560 |
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| author | Katarzyna Bień-Barkowska |
| author_facet | Katarzyna Bień-Barkowska |
| author_sort | Katarzyna Bień-Barkowska |
| collection | DOAJ |
| description | Forecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is built under a discrete-time framework. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices. The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial (NegBin) distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter. In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk (VaR) forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns. |
| first_indexed | 2024-03-10T18:22:02Z |
| format | Article |
| id | doaj.art-a00d5fe4d65a489db92d7bde59fc5d1f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:22:02Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a00d5fe4d65a489db92d7bde59fc5d1f2023-11-20T07:17:40ZengMDPI AGEntropy1099-43002020-07-0122778910.3390/e22070789Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial MarketsKatarzyna Bień-Barkowska0Institute of Econometrics, Warsaw School of Economics, Madalińskiego 6/8, 02-513 Warsaw, PolandForecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is built under a discrete-time framework. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices. The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial (NegBin) distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter. In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk (VaR) forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns.https://www.mdpi.com/1099-4300/22/7/789forecasting market riskvalue at riskextreme returnspeaks over thresholdself-exciting point processdiscrete-time models |
| spellingShingle | Katarzyna Bień-Barkowska Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets Entropy forecasting market risk value at risk extreme returns peaks over threshold self-exciting point process discrete-time models |
| title | Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets |
| title_full | Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets |
| title_fullStr | Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets |
| title_full_unstemmed | Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets |
| title_short | Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets |
| title_sort | looking at extremes without going to extremes a new self exciting probability model for extreme losses in financial markets |
| topic | forecasting market risk value at risk extreme returns peaks over threshold self-exciting point process discrete-time models |
| url | https://www.mdpi.com/1099-4300/22/7/789 |
| work_keys_str_mv | AT katarzynabienbarkowska lookingatextremeswithoutgoingtoextremesanewselfexcitingprobabilitymodelforextremelossesinfinancialmarkets |