Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective
In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasti...
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LLC "CPC "Business Perspectives"
2022-12-01
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| Series: | Investment Management & Financial Innovations |
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| Online Access: | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/17424/IMFI_2022_04_Chalissery.pdf |
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| author | Neenu Chalissery Mosab I. Tabash Mohamed Nishad T. Maha Rahrouh |
| author_facet | Neenu Chalissery Mosab I. Tabash Mohamed Nishad T. Maha Rahrouh |
| author_sort | Neenu Chalissery |
| collection | DOAJ |
| description | In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market. |
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| issn | 1810-4967 1812-9358 |
| language | English |
| last_indexed | 2025-02-17T23:24:54Z |
| publishDate | 2022-12-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Investment Management & Financial Innovations |
| spelling | doaj.art-f75caa20fc3f4c6189330ee51915f6402024-12-02T05:48:12ZengLLC "CPC "Business Perspectives"Investment Management & Financial Innovations1810-49671812-93582022-12-0119424425910.21511/imfi.19(4).2022.2017424Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspectiveNeenu Chalissery0https://orcid.org/0000-0002-2583-9641Mosab I. Tabash1https://orcid.org/0000-0003-3688-7224Mohamed Nishad T.2https://orcid.org/0000-0003-1390-3500Maha Rahrouh3https://orcid.org/0000-0002-8304-2585Ph.D., Department of Commerce and Management Studies, Farook College (Autonomous) KozhikodePh.D., Associate Professor, Department of Business Administration, College of Business, Al Ain UniversityAssociate Professor, Department of Commerce and Management Studies, Farook College (Autonomous) KozhikodePh.D., Assistant Professor, Department of Business Administration, College of Business, Al Ain UniversityIn recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/17424/IMFI_2022_04_Chalissery.pdfasymmetric volatilityfinancial assetsmodel comparisonunivariate Generalized Autoregressive Conditional Heteroscedasticity models |
| spellingShingle | Neenu Chalissery Mosab I. Tabash Mohamed Nishad T. Maha Rahrouh Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective Investment Management & Financial Innovations asymmetric volatility financial assets model comparison univariate Generalized Autoregressive Conditional Heteroscedasticity models |
| title | Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective |
| title_full | Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective |
| title_fullStr | Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective |
| title_full_unstemmed | Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective |
| title_short | Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective |
| title_sort | modeling asymmetric volatility of financial assets using univariate garch models an indian perspective |
| topic | asymmetric volatility financial assets model comparison univariate Generalized Autoregressive Conditional Heteroscedasticity models |
| url | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/17424/IMFI_2022_04_Chalissery.pdf |
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