Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective

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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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    • Figure 1. Return plot of financial assets
    • Table 1. Description of data
    • Table 2. Stationarity test
    • Table 3. Descriptive statistics
    • Table 4. ARCH-LM test result
    • Table 5. Estimation result of GARCH models: Bond yield
    • Table 6. Estimation result of GARCH models: Gold
    • Table 7. Estimation result of GARCH models: Crude oil
    • Table 8. Estimation result of GARCH models: INR/USD
    • Table 9. Estimation result of GARCH models: NIFTY
    • Table 10. Estimation result of GARCH models: SENSEX
    • Table A1. Log likelihood comparison of different asymmetric models under Gaussian and Student-t distribution
    • Table A2. SIC value comparison of different models under Gaussian and Student-t distribution
    • Conceptualization
      Neenu Chalissery, Mosab I. Tabash, Maha Rahrouh
    • Data curation
      Neenu Chalissery, Mosab I. Tabash, Maha Rahrouh
    • Investigation
      Neenu Chalissery
    • Writing – original draft
      Neenu Chalissery
    • Software
      Mosab I. Tabash
    • Supervision
      Mosab I. Tabash, Mohamed Nishad T.
    • Formal Analysis
      Mohamed Nishad T.
    • Methodology
      Mohamed Nishad T., Maha Rahrouh
    • Writing – review & editing
      Mohamed Nishad T., Maha Rahrouh