The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Index by ARMA and GARCH models and then take a step further into the analysis from discrete modeling to continuous modeling. Through applying unit root and stationary tests on the log return of the index, we found that log return of ISE100 data is stationary. Best candidate model chosen was found to be AR(1)~GARCH(1,1) by AIC and BIC criteria. Then using the parameters from the discrete model, COGARCH(1,1) was applied as a continuous model
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Ind...
Non-linearity is the general characteristic of financial series. Thus, common non-linear models such...
Non-linearity is the general characteristic of financial series. Thus, common non-linear models such...
The discrete-time GARCH methodology which hits had such a profound influence on the modelling of het...
ARMA, stock returns, ISE 100. Autoregressive conditional heteroscedasticity (ARCH) and Generalized A...
Abstract. In this paper we decompose the realized volatility of the GARCH-RV model into continuous s...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
Abstract. In this paper we decompose the realized volatility of the GARCH-RV model into continuous s...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
The objective of this paper is to model the volatility of Istanbul Stock Exchange market, ISE100 Ind...
Non-linearity is the general characteristic of financial series. Thus, common non-linear models such...
Non-linearity is the general characteristic of financial series. Thus, common non-linear models such...
The discrete-time GARCH methodology which hits had such a profound influence on the modelling of het...
ARMA, stock returns, ISE 100. Autoregressive conditional heteroscedasticity (ARCH) and Generalized A...
Abstract. In this paper we decompose the realized volatility of the GARCH-RV model into continuous s...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic vola...
Abstract. In this paper we decompose the realized volatility of the GARCH-RV model into continuous s...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...
We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on eq...