Rational GARCH model: An empirical test for stock returns

2017 ◽  
Vol 473 ◽  
pp. 451-460 ◽  
Author(s):  
Tetsuya Takaishi
Keyword(s):  
Stock Returns ◽  
Empirical Test ◽  
Garch Model

scholarly journals Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

2021 ◽  
Vol 14 (7) ◽  
pp. 314
Author(s):  
Najam Iqbal ◽  
Muhammad Saqib Manzoor ◽  
Muhammad Ishaq Bhatti
Keyword(s):  
Stock Returns ◽  
Garch Model ◽  
Bad News ◽  
Good News ◽  
Fiscal Policies ◽  
News Shocks ◽  
Australian Health ◽  
Egarch Model ◽  
Negative Effect ◽  
The Garch Model

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

Prospect Theory and Stock Returns: An Empirical Test

2016 ◽  
Vol 29 (11) ◽  
pp. 3068-3107 ◽  
Author(s):  
Nicholas Barberis ◽  
Abhiroop Mukherjee ◽  
Baolian Wang
Keyword(s):  
Stock Returns ◽  
Prospect Theory ◽  
Empirical Test

Are Shocks Asymmetric to Volatility of Chinese Stock Markets?

2004 ◽  
Vol 07 (03) ◽  
pp. 379-395 ◽  
Author(s):  
Wei-Chiao Huang ◽  
Yuanlei Zhu
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Garch Model ◽  
Leverage Effect ◽  
Arch Models ◽  
Before And After ◽  
Chinese Markets ◽  
Chinese Stock Markets ◽  
Better Than ◽  
A Share

This paper uses ARCH models to examine if there is a leverage effect and also to test if A- and B-share holdings have different risks in Chinese stock markets before and after B-share markets open to domestic investors in February 2001. The empirical results suggest that leverage effect was not present and shocks have symmetric impact on the volatility of Chinese B-share stock returns in both periods and A-share returns in Period I. Thus GARCH model would be a better model to fit the Chinese B-share stock returns than EGARCH or GJR-GARCH model. But EGARCH or GJR-GARCH model fits recent (Period II) A-share markets data better than GARCH model. Another finding of this paper is that holding A- or B-share bears different risk in returns in the two Chinese markets. Furthermore, news or shocks have a larger impact on volatility of B-share returns in Period I than in Period II.

scholarly journals Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model

2017 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
Eun-Joo Lee ◽  
Noah Klumpe ◽  
Jonathan Vlk ◽  
Seung-Hwan Lee
Keyword(s):  
Stock Returns ◽  
Time Series Data ◽  
Financial Time Series ◽  
Garch Model ◽  
Series Data ◽  
Future Price ◽  
Investment Strategies ◽  
Volatility Clustering ◽  
Technology Stocks ◽  
Stock Portfolio

Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the construction of a multivariate distribution. Using the copula-based GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.

scholarly journals The Relationship between Stock Returns and Trading Hours: Evidence from Amman Stock Exchange

2018 ◽  
Vol 7 (3.21) ◽  
pp. 89
Author(s):  
Buthiena Kharabsheh ◽  
Mahera Hani Megdadi ◽  
Waheeb Abu-ulbeh
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Amman Stock Exchange ◽  
The Relationship ◽  
Autoregressive Conditional Heteroscedasticity

This study investigates the relationship between stock returns and trading hours for 22 shares listed on Amman Stock Exchange (ASE). We analyze the hourly trading data for the period Dec.2005 to Dec.2006. The two trading hours in ASE were split into four periods; first half of the first hour (10:00-10:30), second half of the first hour (10:30-11:00), first half of the second hour (11:00-11:30), and second half of the second hour (11:30-12:00). Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, our results reveal that the hourly trading time significantly affects stock returns.  

scholarly journals Research on Stock Returns Forecast of the Four Major Banks Based on ARMA and GARCH Model

2020 ◽  
Vol 1616 ◽  
pp. 012075
Author(s):  
Yuanwei Hu ◽  
Zheng Tao ◽  
Dongao Xing ◽  
Ziyi Pan ◽  
Junyi Zhao ◽  
...  
Keyword(s):  
Stock Returns ◽  
Garch Model
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