Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models
Purpose: This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets,...
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| Format: | Article |
| Language: | fas |
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Ayandegan Institute of Higher Education, Tonekabon,
2024-12-01
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| Series: | تصمیم گیری و تحقیق در عملیات |
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| Online Access: | https://www.journal-dmor.ir/article_212360_fcf1ada6c8ed4dbe45863b0d9b964c20.pdf |
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| author | Shiva Hallaji Mahdi Madanchi Zaj Fereydon Ohadi Hamidreza Vakilifard |
| author_facet | Shiva Hallaji Mahdi Madanchi Zaj Fereydon Ohadi Hamidreza Vakilifard |
| author_sort | Shiva Hallaji |
| collection | DOAJ |
| description | Purpose: This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.Methodology: Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.Findings: The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.Originality/Value: This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market. |
| format | Article |
| id | doaj-art-58afd27a5bc34f55ac8584800da750c2 |
| institution | Kabale University |
| issn | 2538-5097 2676-6159 |
| language | fas |
| publishDate | 2024-12-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | تصمیم گیری و تحقیق در عملیات |
| spelling | doaj-art-58afd27a5bc34f55ac8584800da750c22025-01-30T15:04:06ZfasAyandegan Institute of Higher Education, Tonekabon,تصمیم گیری و تحقیق در عملیات2538-50972676-61592024-12-019381683210.22105/dmor.2024.489195.1889212360Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression modelsShiva Hallaji0Mahdi Madanchi Zaj1Fereydon Ohadi2Hamidreza Vakilifard3Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.Department of Financial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.Department of Industrial Engineering, Faculty of Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran, Iran.Purpose: This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.Methodology: Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.Findings: The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.Originality/Value: This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market.https://www.journal-dmor.ir/article_212360_fcf1ada6c8ed4dbe45863b0d9b964c20.pdfforecastingcvarheterogeneous regression |
| spellingShingle | Shiva Hallaji Mahdi Madanchi Zaj Fereydon Ohadi Hamidreza Vakilifard Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models تصمیم گیری و تحقیق در عملیات forecasting cvar heterogeneous regression |
| title | Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models |
| title_full | Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models |
| title_fullStr | Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models |
| title_full_unstemmed | Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models |
| title_short | Forecasting of CVaR based on intraday trading in Tehran ETFs: The approach of heterogeneous autoregression models |
| title_sort | forecasting of cvar based on intraday trading in tehran etfs the approach of heterogeneous autoregression models |
| topic | forecasting cvar heterogeneous regression |
| url | https://www.journal-dmor.ir/article_212360_fcf1ada6c8ed4dbe45863b0d9b964c20.pdf |
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