Tag Archives: GARCH model

Examining the Effect of Financial Liberalization on the Performance of Tehran Stock Market (Published)

In this article, the effect of financial liberalization on the performance of information in Tehran Stock Exchange for the period 1997 to 2014 is examined. In order to determine the effect of financial liberalization on the performance of information, the method of treatment effects by Maddala (1983) was established. An optimization algorithm called a Kalman filter is used in estimation model, because it is impossible using conventional estimation techniques. To estimate the model, generalized least squares method (OLS) was used. The experimental strategy used in this study considers the main feature of emerging markets i.e. fragility of the financial system and different strategies used in previous studies. Here, only the direct effect between financial liberalization and performance evaluation of the information is examined, but there is an indirect effect of the financial crisis to examine the effect of liberalization. Research findings show that firstly although the information efficiency is not constant over time, but the path is not increased.  Second, financial liberalization impact on recent currency crisis will not affect the performance information.  Third, the financial crisis impact on information efficiency are variable. As a result, the impact of liberalization on information efficiency is approved.

Keywords: Financial Crises, Financial Liberalization, GARCH model, Information Efficiency, Kalman Filter Model

MODELLING THE VOLATILITY OF EXCHANGE RATES IN RWANDESE MARKETS (Published)

This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approach to modelling volatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errors was fitted to the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility. Asymptotic consistency and asymptotic normality of estimated parameters were given. Akaike Information criterion was used for appropriate GARCH model selection while Jarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. It was shown that the estimated model fits Rwanda exchange rate returns data well.

Keywords: Exchange Rate, GARCH model, Model, Quasi Maximum Likelihood, Volatility