Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns
Using GARCH-Based Dynamic Conditional Correlation Model
Bibliographic Data
| ID | 3280898 |
|---|---|
| Authors | Fahim Afzal (0000-0001-6292-8531, University of Engineering and Technology Lahore, corresponding author), Pan Haiying (University of Engineering and Technology Lahore), Farman Afzal (0000-0001-8637-9741, University of Engineering and Technology Lahore), Asif Mahmood (0000-0003-1416-0390, Namal College), Amir Ikram (0000-0002-2585-8834, University of Engineering and Technology Lahore) |
| Year | 2021 |
| Volume | 11 |
| Issue | 1 |
| Publication date | 2021-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/21582440211005758 |
| OpenAlex | W3152307685 |
| Language | EN |
| Citations received | 2 |
| References cited | 34 |
To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009-2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks
Autoregressive conditional heteroskedasticity · Autoregressive model · Bivariate analysis · Econometrics · Economics · Financial economics · Heteroscedasticity · Risk management · Statistics · Stock exchange · Stock market · Stock market index · Value at risk · Complex Systems and Time Series Analysis · Engineering · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Finance
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Generalized autoregressive conditional heteroskedasticity
A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations
Dynamic Conditional Correlation
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |