In this paper we survey time series models allowing for conditional heteroscedas ticity and autoregression like AR GARCH type models These models reduce to a white noise model when some of the conditional heteroscedasticity parameters take their boundary value at zero and the autoregressive component is in fact not present We reproduce the asymptotic distribution of the pseudo log likelihood ra...

Stock price data at State Gas Company is defined as the time-series comprising varying volatility and heteroscedasticity. One of best models used to solve problem heteroscedasticity GARCH (generalized autoregressive conditional heteroscedasticity) model. Therefore, this study aims build most suitable model for predicting 186 days before 176 after Covid-19 pandemic, well provide recommendations ...

The popularity of Bitcoin increased significantly in 2021. is considered to deliver high returns a relatively short period, indicating that bitcoin has volatility. Data with volatility usually violates the Autoregresstive IntegratedinMovinginAverage (ARIMA)in homoscedasticity assumption. Autoregressive Conditional Heteroscedasticity (ARCH) and General (GARCH) model often used overcome problem h...

Purpose This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories Kong were analyzed from February 1993 2019 test whether clusters are present the market. Real determinants also investigated. Design/methodology/approach Autoregressive conditional heteroscedasticity–Lagrange multiplier is used clustering effects these four kinds ...

Previous studies of the information content of the implied volatilities from the prices of call options have used a cross-sectional regression approach. This paper compares the information content of the implied volatilities from call options on the S&P 100 index to GARCH (Generalized Autoregressive Conditional Heteroscedasticity) and Exponential GARCH models of conditional volatility. By addin...

In the presence of generalized conditional heteroscedasticity (GARCH) in the residuals of a vector error correction model (VECM), maximum likelihood (ML) estimation of the cointegration parameters has been shown to be efficient. On the other hand, full ML estimation of VECMs with GARCH residuals is computationally difficult and may not be feasible for larger models. Moreover, ML estimation of V...

exchange rate and national income of countries trading with each other are among the most important factors affecting each country's trade. considering the political and economic ties between iran and venezuela in recent years, the goal of this paper was to investigate the effect of exchange rate volatility on exports of iran to venezuela. data used in this research include annual data for...

This study evaluates recently reported, conflicting, models for the probability distributions of daily exchange rate price changes. The conflicting conclusions arise from differing data sets, noncomparable evaluation criteria, and failures to directly compare the candidate models. This study evaluates the mixed jump diffusion model, a discrete mixture of normals distribution model, and four alt...

abstract exchange rate volatility is one of the effective and ambiguous factors in agricultural products export. regarding the importance of agricultural trade, to avoid single-product economy, the main aim of this study is to investigate the impact of exchange rate volatility on the pistachio export of iran during 1338-1386. for this purpose, exchange rate volatility index was estimated using ...