مطالب مرتبط با کلیدواژه

Heteroskedasticity


۱.

Modeling Stock Market Volatility Using Univariate GARCH Models: Evidence from Bangladesh(مقاله علمی وزارت علوم)

کلیدواژه‌ها: GARCH Heteroskedasticity Volatility Clustering Asymmetric Volatility

حوزه های تخصصی:
  1. حوزه‌های تخصصی اقتصاد اقتصاد مالی بازارهای مالی پیش بینی های مالی
  2. حوزه‌های تخصصی اقتصاد روش های ریاضی و کمی مدل های تک معادله ای مدل های سری زمانی،رگرسیون های چندک پویا
تعداد بازدید : ۸۶۹ تعداد دانلود : ۵۹۵
This paper investigates the nature of volatility characteristics of stock returns in the Bangladesh stock markets employing daily all share price index return data of Dhaka Stock Exchange (DSE) and Chittagong Stock Exchange (CSE) from 02 January 1993 to 27 January 2013 and 01 January 2004 to 20 August 2015 respectively. Furthermore, the study explores the adequate volatility model for the stock markets in Bangladesh. Results of the estimated MA(1)-GARCH(1,1) model for DSE and GARCH(1,1) model for CSE reveal that the stock markets of Bangladesh capture volatility clustering, while volatility is moderately persistent in DSE and highly persistent in CSE. Estimated MA(1)-EGARCH(1,1) model shows that effect of bad news on stock market volatility is greater than effect induced by good news in DSE, while EGARCH(1,1) model displays that volatility spill over mechanism is not asymmetric in CSE. Therefore, it is concluded that return series of DSE show evidence of three common events, namely volatility clustering, leptokurtosis and the leverage effect, while return series of CSE contains leptokurtosis, volatility clustering and long memory. Finally, this study explores that MA(1)-GARCH(1,1) is the best model for modeling volatility of Dhaka stock market returns, while GARCH models are inadequate for volatility modeling of CSE returns.
۲.

Modeling the Dynamic Correlations among Cryptocurrencies: New Evidence from Multivariate Factor Stochastic Volatility Model(مقاله علمی وزارت علوم)

کلیدواژه‌ها: Factor Stochastic Volatility Cryptocurrencies Bayesian approach Heteroskedasticity Dynamic Correlation

حوزه های تخصصی:
تعداد بازدید : ۱۳ تعداد دانلود : ۱۱
This paper intends to model the volatilities of returns of 20 different cryptocurrencies using daily data from 08/03/2018 to 09/20/2022. The multivariate factor stochastic volatility model (MFSV) within the framework of the nonlinear space-state approach is used. In this method, the cryptocurrency return volatility is decomposed into volatility rooted in latent factors and idiosyncratic volatility, and the time-varying pairwise correlation and dynamic covariance matrix are estimated in four sub-periods. The MFSV model’s results revealed that each sub-period contains a distinct number of latent factors, 2, 5, 4 and 2, which generally have a favorable impact on all cryptocurrency volatilities. The time-varying positive correlations between the return volatility of all cryptocurrencies are confirmed. Indeed, the strongest pairwise correlations belong to Ethereum, Litcoin, EOS, and VET in each sub-period, respectively. The DOGE, DOGE, Filecoin, and XRP, on the other hand, showedthe weakest correlations . As the pairwise correlations of cryptocurrency volatilities get strenger, especially during descending periods, it seems that the benefits of diversifying a crypto portfolio are getting less and less over time.