IJMSS10Nov5784.docx

Engle and Patton (2000) point out that a volatility model must have the forecasting ability, this is the central requirement. They explore the stylized factors of volatility and observe the ability of GARCH type models to capture those features. In this paper, we aim to evaluate the ability of GARCH type models to capture the stylized factors of Dhaka Stock Exchange (DSE) returns volatility. We consider the sample period from 27 th November 2001 to 31 st July 2013 for DSE general index and estimate GARCH type models. We made a comparative of different GARCH type models for capturing the stylized factors of the stock index return's volatility.

This study conducted the empirical investigation for the volatility of Broad index of Dhaka stock Exchange (DESX) in Bangladesh. Asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was used for DESX index. According to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), GJR-GARCH (1,1) is found to be the most applicable model to capture the asymmetric volatility. Their performances were also compared under statistical error measurement tools, e.g., root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), theil inequality coefficient and bias proportion analyses.

Econometric Modeling: Capital Markets - Forecasting eJournal, 2021

Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy unde...

ABAC Journal, 2012

This paper compares and estimates standard and asymmetric GARCH models with daily returns data of the DSI index (All Share Price Index) of the Dhaka Stock Exchange from 28 March 2005 to 30 November 201 0. The maximum likelihood estimation (MLE) technique is used to estimate the parameters of the chosen models. Results indicate that GARCH has lower log-likelihood than the asymmetric GJR-GARCH model, which implies that GJRGARCH model is a better performing model to estimate and to forecast volatility. Results from other hypothesis tests indicate that volatility process has unit root i.e. volatility process is nonstationary, expected returns do not always depend on volatility, and the conditional variance (volatility) of future asset price is a symmetric function of changes in price at Dhaka stock market.

This study aimed at understanding the volatility of Dhaka Stock Exchange (DSE). The daily and monthly average DSE General Index (DGEN), from the period January 1, 2002 to July 31, 2013 has been used. The study has been made by using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to estimate the presence of volatility. Though volatility is a common phenomenon in the capital market, the study recommends careful monitoring of volatility by the concerned authority if necessary. It is also recommended that activities of corporate insiders should be properly checked and information should become available for all of the interested investors and to ensure adequate supply of stock through active participation of the government and giant national and multinational companies and so forth.

2016

The study examined and modeled stock market volatility of financial return series for three listed equities on the Ghana Stock Exchange (GSE). A historical data from 25th June 2007 to 31st October 2014 was considered for the analysis. The series for each of the three equities were tested for stationarity using the KPSS test. Series found to be non-stationary were transformed to be stationary. The study fitted a GARCH (p, q) model for volatility. GARCH (1, 1), GARCH (1, 2), GARCH (2, 1) and the GARCH (2, 2) models were fitted to residual series of some three equities. Results revealed the presence of volatilities in all three equities and also showed that volatility though present was not persistent in the three equities. For each of the companies under study, the GARCH (1, 1) model was found to outperform the other three models based on the comparison of the AICc for each model. The study recommended the use and comparison of other variants of the GARCH model in estimation of volati...

2020

The National Stock Exchange and Bombay Stock Exchange are the two major stock exchanges in India. The Bombay Stock Exchange is the first stock exchange of Asia and 10th largest stock exchange in the world in the terms of market capitalisation. Stock markets significantly contributes in the economic development of India. The stock markets have volatile character which results into the uncertainty of the returns, volatility is caused by the variability in speculative market prices and the instability of business performance. Volatility plays a significant role in financial decisions of the investors, managers, policy makers and the researchers as it can assess the risk exposures in their investments and the uncertainty in stocks returns. The risk averse investor avoid investment in highly volatile market. The stock return forecasting leads to volatility forecasting. This paper has made an attempt to analyse the volatility with reference to Bombay Stock Exchange. The daily data of S&P ...

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) 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.

Open Journal of Statistics, 2017

The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.

Este árbol en la acera de mi calle en medio del cemento crece solitario sin bosque sin pájaros sin insectos sin arroyo; pero verdea siempre en silencio sumiso entre sol o noche bajo el aire grueso de la urbe y es su vida estar allí transformando polución en frescura con tronco ramas hojas flores como un filtro de luz y un apuesto vigía. Óscar Gerardo Ramos 2 (1928 -) 1 . Patiño, Víctor Manuel. La flora en la poesía. Antología. Cali. 1976. 2 . Doctor en Filosofía y Letras. Magíster en Administración Industrial, Doctor Honoris Causa en Literatura y Gran Cruz, Universidad del Valle. www.valledelcauca.gov.co/publicaciones.php?id=2383.