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Full-Text Articles in Social and Behavioral Sciences
Reactions Of Stock Market To Monetary Policy Shocks During The Global Financial Crisis: The Nigerian Case, Aliyu Shehu U.R.
Reactions Of Stock Market To Monetary Policy Shocks During The Global Financial Crisis: The Nigerian Case, Aliyu Shehu U.R.
CBN Journal of Applied Statistics (JAS)
This paper seeks to assess the reactions of Nigeria’s stock market to monetary policy innovations during the period of global financial crisis on the basis of monthly data over the period January, 2007 to August, 2011. In particular, stock market return was regressed against major monetary policy instruments; money stock (M1, and M2) and monetary policy rate (MPR). The theoretical basis for the paper stems from the works of new classical macroeconomics and rational expectation hypothesis (REH). Lucas (1972) postulated that only the unanticipated monetary shock influences real economic activity. Using the GARCH by developed Engle and Bollerslev (1986) and …
Modeling Time Series With Conditional Heteroscedastic Structure, Ratnayake Mudiyanselage Isuru Panduka Ratnayake
Modeling Time Series With Conditional Heteroscedastic Structure, Ratnayake Mudiyanselage Isuru Panduka Ratnayake
Doctoral Dissertations
"Models with a conditional heteroscedastic variance structure play a vital role in many applications, including modeling financial volatility. In this dissertation several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroscedastic model, are further generalized to provide more effective modeling of price range data well as count data. First, the Conditional Autoregressive Range (CARR) model is generalized by introducing a composite range-based multiplicative component formulation named the Composite CARR model. This formulation enables a more effective modeling of the long and short-term volatility components present in price range data. It treats the long-term volatility as a stochastic component that in …
Garch Modeling Of Value At Risk And Expected Shortfall Using Bayesian Model Averaging, Ismail Kheir
Garch Modeling Of Value At Risk And Expected Shortfall Using Bayesian Model Averaging, Ismail Kheir
Theses and Dissertations
This thesis conducts Value at Risk (VaR) and Expected Shortfall (ES) estimation using GARCH modeling and Bayesian Model Averaging (BMA). BMA considers multiple models weighted by some information criterion. Through BMA, this thesis finds that VaR and ES estimates can be improved through enhanced modeling of the data generation process.
Misspecification Of Variants Of Autoregressive Garch Models And Effect On In-Sample Forecasting, Olusanya E. Olubusoye, Olaoluwa S. Yaya, Oluwadare O. Ojo
Misspecification Of Variants Of Autoregressive Garch Models And Effect On In-Sample Forecasting, Olusanya E. Olubusoye, Olaoluwa S. Yaya, Oluwadare O. Ojo
Journal of Modern Applied Statistical Methods
Generally, in empirical financial studies, the determination of the true conditional variance in GARCH modelling is largely subjective. In this paper, we investigate the consequences of choosing a wrong conditional variance specification. The methodology involves specifying a true conditional variance and then simulating data to conform to the true specification. The estimation is then carried out using the true specification and other plausible specification that are appealing to the researcher, using model and forecast evaluation criteria for assessing performance. The results show that GARCH model could serve as better alternative to other asymmetric volatility models.
Estimates And Forecasts Of Garch Model Under Misspecified Probability Distributions: A Monte Carlo Simulation Approach, Olaoluwa S. Yaya, Olusanya E. Olubusoye, Oluwadare O. Ojo
Estimates And Forecasts Of Garch Model Under Misspecified Probability Distributions: A Monte Carlo Simulation Approach, Olaoluwa S. Yaya, Olusanya E. Olubusoye, Oluwadare O. Ojo
Journal of Modern Applied Statistical Methods
The effect of misspecification of correct sampling probability distribution of Generalized Autoregressive Conditionally Heteroscedastic (GARCH) processes is considered. The three assumed distributions are the normal, Student t, and generalized error distributions. The GARCH process is sampled using one of the distributions and the model is estimated based on the three distributions in each sample. Parameter estimates and forecast performance are used to judge the estimated model for performance. The AR-GARCH-GED performed better on the three assumed distributions; even, when Student t distribution is assumed, AR-GARCH-Student t does not perform as the best model.
Exchange–Rates Volatility In Nigeria: Application Of Garch Models With Exogenous Break, Bala A. Dahiru, Joseph O. Asemota
Exchange–Rates Volatility In Nigeria: Application Of Garch Models With Exogenous Break, Bala A. Dahiru, Joseph O. Asemota
CBN Journal of Applied Statistics (JAS)
This paper examines exchange–rate volatility with GARCH models using monthly exchange–rate return series from 1985:1 to 2011:7 for Naira/US dollar return and from 2004:1 to 2011:7 for Naira/British Pounds and Naira/Euro returns. The study compare estimates of variants of GARCH models with break in respect of the US dollar rates with exogenously determined break points. Our results reveal presence of volatility in the three currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. Evaluating the models through standard information criteria, volatility persistence and the log likelihood …
The Effect Of Garch (1,1) On The Granger Causality Test In Stable Var Models, Panagiotis Mantalos, Ghazi Shukur, Pär Sjölander
The Effect Of Garch (1,1) On The Granger Causality Test In Stable Var Models, Panagiotis Mantalos, Ghazi Shukur, Pär Sjölander
Journal of Modern Applied Statistical Methods
Using Monte Carlo methods, the properties of Granger causality test in stable VAR models are studied under the presence of different magnitudes of GARCH effects in the error terms. Analysis reveals that substantial GARCH effects influence the size properties of the Granger causality test, especially in small samples. The power functions of the test are usually slightly lower when GARCH effects are imposed among the residuals compared with the case of white noise residuals.
A Class Of Nonlinear Stochastic Volatility Models, Jun Yu, Zhenlin Yang
A Class Of Nonlinear Stochastic Volatility Models, Jun Yu, Zhenlin Yang
Research Collection School Of Economics
This paper proposes a class of nonlinear stochastic volatility models based on the Box-Cox transformation which offers an alternative to the one introduced in Andersen (1994). The proposed class encompasses many parametric stochastic volatility models that have appeared in the literature, including the well known lognormal stochastic volatility model, and has an advantage in the ease with which different specifications on stochastic volatility can be tested. In addition, the functional form of transformation which induces marginal normality of volatility is obtained as a byproduct of this general way of modeling stochastic volatility. The efficient method of moments approach is used …
Forecasting Volatility In European Stock Markets With Non-Linear Garch Models, Giancarlo Forte, Matteo Manera
Forecasting Volatility In European Stock Markets With Non-Linear Garch Models, Giancarlo Forte, Matteo Manera
Matteo Manera
This paper investigates the forecasting performance of three popular variants of the nonlinear GARCH models, namely VS-GARCH, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of classical criteria, as well as forecast combination techniques with constant and non-constant weights. With respect to the standard GARCH specification, the non-linear models generally lead to better forecasts in terms of both smaller forecast errors and lower biases. In-sample forecast combination regressions are better than those from …