4 GARCH Models 7 4.1 Basic GARCH Specifications . . . . . . . . . . . . . . . . . . . 8 4.2 Diagnostic Checking . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Regressors in the Variance Equation . . . . . . . . . . . . . . . 12 4.4 The GARCH–M Model . . . . . . . . . . . . . . . . . . . . . . 12 4.5 The Threshold GARCH (TARCH) Model . . . . . . . . . . . . 12 4.6 The Exponential GARCH (EG...

modeling and forecasting the volatility of tehran exchange dividend price index (tedpix) the present research, analyses the forecasting performance of a variety of conditional and non-conditional models of tedpix volatility at the daily frequencies performance criterion namely the root mean square error (rmse). under rmse, results show ma250 and cgarch models had better performance between non ...

Purpose The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns volatility. Design/methodology/approach competing models are autoregressive moving average (ARMA) model fractional integrated (ARFIMA) for returns. For volatility, exponential generalized conditional heteroscedasticity (EGARCH) with GARCH (FIGARCH) component...

With the rising importance of China’s role in the world economy, the Chinese economic fluctuation has become a more and more significant factor that influences the world economy. Therefore, it is an interesting issue for all circles as well as academicians that whether the real economic interconnection leads to volatility spillover between China’s and international stock markets. In this paper,...

The asymmetric volatility, temporary volatility, and permanent volatility of financial asset returns have attracted much interest in recent years. However, a consensus has not yet been reached on the causes of them for both the stocks and markets. This paper researched asymmetric volatility and short-run and long-run volatility through global financial crisis for eight Asian markets. EGARCH and...

In this article, we analyze the time series of minute price returns on Bitcoin market through statistical models generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values and their volatilities—GARCH family models, with a so-called Mixture Distribution Hypothesis, which states dynamics are governed by information flow about mark...