scholarly journals Volatility Estimation in the Dhaka Stock Exchange (DSE) returns by Garch Models

2014 ◽  
Vol 4 (1) ◽  
pp. 41-50
Author(s):  
Md. Shawkatul Islam Aziz ◽  
◽  
Md. Nezum Uddin ◽  
Keyword(s):  
Stock Exchange ◽  
Garch Models ◽  
Volatility Estimation

scholarly journals Volatility Estimation in the Dhaka Stock Exchange (DSE) returns by Garch Models

2015 ◽  
Vol 4 (1) ◽  
pp. 41
Author(s):  
Md. Shawkatul Islam Aziz ◽  
Md. Nezum Uddin
Keyword(s):  
Stock Exchange ◽  
Garch Models ◽  
Volatility Estimation

scholarly journals Dynamic value at risk estimation for BELEX15

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilija Nikolić-Đorić ◽  
Dragan Đorić
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Long Memory ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Value Theory ◽  
Garch Models ◽  
Measurement Models ◽  
Standardized Residuals

This paper uses RiskMetrics, GARCH and IGARCH models to calculate daily VaR for Belgrade Stock Exchange index BELEX15 returns based on the normal and Student t innovation distribution. In the case of GARCH and IGARCH models VaR values are obtained applying Extreme Value Theory on the standardized residuals. The Kupiec's LR statistics was used to test the accuracy of risk measurement models. The main conclusions are: (1) when modelling value-at-risk it is very important to have a good model for volatility of stock returns; (2) both stationary and integrated GARCH models outperform RiskMetrics in estimating VaR; (3) although long memory volatility is present in the BELEX15 index, IGARCH models cannot outperform GARCH type models in VaR evaluations for this index.

scholarly journals Volatility estimation for cryptocurrencies using Markov-switching GARCH models

2019 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Paulo Vitor Jordao Da Gama Silva ◽  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Leonardo Lima Gomes
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Volatility Estimation

Estimating Long-Term Volatility on National Stock Exchange of India

2019 ◽  
pp. 229-237
Keyword(s):  
Stock Exchange ◽  
Stock Index ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Large Sample ◽  
Market Capitalization ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity

The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

How Does COVID-19 Affect the Volatility Spillover Between the Exchange Rate and the Export-oriented Businesses in Iran?

2022 ◽  
pp. 097215092110606
Author(s):  
Zahra Honarmandi ◽  
Samira Zarei
Keyword(s):  
Exchange Rate ◽  
Stock Exchange ◽  
Spillover Effects ◽  
Garch Models ◽  
Volatility Spillover ◽  
Exogenous Shocks ◽  
Risk Period ◽  
Before And After ◽  
The Exchange Rate ◽  
Fractal Market Hypothesis

This study concentrates on examining the volatility spillover effects between the exchange rate (IRR to USD) and the leading export-oriented industries (i.e., petrochemical, basic metals and minerals) in Tehran Stock Exchange before and after the COVID-19 pandemic. Using DCC- and asymmetric DCC-GARCH approaches, the data sample (from 15 December 2018 to 24 April 2021) has been partitioned into two sub-samples: before and after the official announcement of COVID-19 outbreak. The results demonstrate that from the pre- to post-COVID-19 periods, first, the average returns of all industries have sharply fallen; second, the volatility of all variables has been significantly augmented in different horizons; third, for all industries, not only has the fractal market hypothesis approved in both separated periods, but also analysing the values of the fractional difference parameter, together with the outcomes of GARCH models, supports in the higher-risk post-COVID-19 period, wherein the effects of exogenous shocks last longer than their impacts in the alternative lower-risk period. Furthermore, our investigations demonstrate that the asymmetric spillover (based on the ADCC-GARCH models) in both pre- and post-COVID-19 periods are confirmed in all three industries, except for minerals after the novel coronavirus.Ultimately, the results not only corroborate the increase in the volatility spillover effects right after the COVID-19 but also substantiate that the exchange rate contributes most of the spillover effects into the petrochemical and minerals industries, which have been almost twice as much as those of the basic metals.

Volatility of Indian Stock Market: An Emperical Evidence

2008 ◽  
Vol 4 (4) ◽  
pp. 53-61 ◽  
Author(s):  
Aman Srivastava
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Garch Models ◽  
Indian Stock Market ◽  
Class Models

The purpose of this paper is to apply the GARCH-class models to two major stock exchanges of Indian stock markets. The study includes main indices of Bombay Stock Exchange (SENSEX) and that of National stock exchange (NIFTY). GARCH-class models have been applied to analyze the characteristics of the volatility of Indian stock market. The findings suggest that both the Indian stock exchanges have significant ARCH effects and it is appropriate to use ARCH/GARCH models to estimate the process and also demonstrated that there are leverage effects in the markets. That means the investors in those markets are not grown well and they will be heavily influenced by information (good or bad) very easily.

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