Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation
Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely...
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MDPI AG
2023-08-01
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| Online Access: | https://www.mdpi.com/2306-5729/8/8/133 |
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| author | Farid Bagheri Diego Reforgiato Recupero Espen Sirnes |
| author_facet | Farid Bagheri Diego Reforgiato Recupero Espen Sirnes |
| author_sort | Farid Bagheri |
| collection | DOAJ |
| description | Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (<i>d</i>) according to different metrics (i) to their respective variants that included stock return forecast information of <i>d</i> and stock return data of the days before <i>d</i> and (ii) to a GARCH model that included return prediction information of <i>d</i> and stock return data of the days before <i>d</i>. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces. |
| first_indexed | 2024-03-11T00:00:05Z |
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| last_indexed | 2024-03-11T00:00:05Z |
| publishDate | 2023-08-01 |
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| spelling | doaj.art-422510dbcf434d248eaeb039929412cc2023-11-19T00:47:04ZengMDPI AGData2306-57292023-08-018813310.3390/data8080133Leveraging Return Prediction Approaches for Improved Value-at-Risk EstimationFarid Bagheri0Diego Reforgiato Recupero1Espen Sirnes2Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalySchool of Business and Economics, UiT The Arctic University of Norway, Breivangvegen 23, 9010 Tromsø, NorwayValue at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (<i>d</i>) according to different metrics (i) to their respective variants that included stock return forecast information of <i>d</i> and stock return data of the days before <i>d</i> and (ii) to a GARCH model that included return prediction information of <i>d</i> and stock return data of the days before <i>d</i>. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces.https://www.mdpi.com/2306-5729/8/8/133VaR estimationmachine learningreturn predictionwalking forward optimization |
| spellingShingle | Farid Bagheri Diego Reforgiato Recupero Espen Sirnes Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation Data VaR estimation machine learning return prediction walking forward optimization |
| title | Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation |
| title_full | Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation |
| title_fullStr | Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation |
| title_full_unstemmed | Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation |
| title_short | Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation |
| title_sort | leveraging return prediction approaches for improved value at risk estimation |
| topic | VaR estimation machine learning return prediction walking forward optimization |
| url | https://www.mdpi.com/2306-5729/8/8/133 |
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