Bilinear Garch Time Series Models
نویسندگان
چکیده
In this paper the class of BL-GARCH (Bilinear General AutoregRessive Conditional Heteroskedasticity) models is introduced. The proposed model is a modification to the BL-GARCH model proposed by Storti and Vitale (2003). Stationary conditions and autocorrelation structure for special cases of these new models are derived. Maximum likelihood estimation of the model is also considered. Some simulation results are presented to evaluate our algorithm.
منابع مشابه
BL-GARCH model with elliptical distributed innovations
We are interested in the parametric class of Bilinear GARCH (BL-GARCH) models which are capable of simultaneously capturing the well known properties of financial retrun series, volatility clustering and leverage effects. Specifically, as it is often observed that the distribution of many financial time series data has heavy tails, heavier than the Normal distribution, we examine, in this paper...
متن کاملON THE STATIONARY PROBABILITY DENSITY FUNCTION OF BILINEAR TIME SERIES MODELS: A NUMERICAL APPROACH
In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.
متن کاملEvaluation of Time Series Patterns for Wind Speed Volatilities in Anzali Meteorological Station
Abstract. One of the major problems in using wind energy is that wind-generated electricity is more unstable than electricity generated by other sources, and therefore integrating wind energy use with traditional power generation systems can be a challenge. This problem can be effectively reduced by having accurate information about the mean and wind speed volatilities. Therefore, in this paper...
متن کاملComparison of Neural Network Models, Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...
متن کاملWhen Is a Time Series I(0)? Evaluating the Memory Properties of Nonlinear Dynamic Models
The paper proposes that a useful concept of I(0) is deÞned by requiring the time series to satisfy a functional central limit theorem, and considers sufficient conditions for this property to hold in a variety of time series models. First, a new result is given for semiparametric linear processes, whose conditions are shown to be close to necessary. Second, a range of popular nonlinear models i...
متن کامل