in this article using autoregressive (ar), autoregressive conditional heteroskedasticity (arch), generalized autoregressive conditional heteroskedasticity (garch) models we assess the weekend effect and also compare the trading patterns of individual and legal investors during 1381-1385 in tehran stock exchange. our findings suggest that weekend effect exists in tehran stock exchanges which are...

inflation has always been an economic problem and different solutions have been proposed to control it. although it is said that “higher output lowers inflation rate” but it is true when other factors are constant. this study searches the answer to the following question: “what is the effect of inflation rate and output in a case that inflation rate and output growth has a volatility trend?” to...

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity ~ARMAGARCH! model+ The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established+ Consistency of the quasimaximum-likelihood estimator ~QMLE! is proved under only the second-order moment conditi...

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

in this paper, we investigate variations of gold coin price and also probe to model the fluctuations and conditional variance of coin market returns. the data consist of daily market prices of gold coin over the 1380 – 1386 period. since volatility clustering is viewed in time series of returns, we employ arch (autoregressive conditional heteroskedasticity) methodology in order to model the var...

In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive cond...

Volatility is a key parameter used in many financial applications, from derivatives valuation to asset management and risk management. Volatility measures the size of the errors made in modeling returns and other financial variables. It was discovered that, for vast classes of models, the average size of volatility is not constant but changes with time and is predictable. Autoregressive conditi...

Financial series such as stock returns follow a different generating process from the relevant economic series. The key different between each other is that financial time series have some key features which cannot be captured by models such as ARMA. ARMA, which is referred as autoregressive moving-average, models consist a good approximation for economic series but not for financial series. In...