This paper examines the forecasting performance of four GARCH(1,1) models (GARCH, EGARCH, GJR and APARCH) used with three distributions (Normal, Student-t and Skewed Student-t). We explore and compare different possible sources of forecasts improvements: asymmetry in the conditional variance, fat-tailed distributions and skewed distributions. Two major European stock indices (FTSE 100 and DAX 3...

GARCH-type models have been highly developed since Engle [1982] presented ARCH process 30 years ago. Different kinds of GARCH-type models are applicable to different kinds of research purposes. As documented by many literatures that short-memory processes with level shifts will exhibit properties that make standard tools conclude long-memory is present. Therefore, in this paper, we want to fore...

Data finansial yang mengikuti deret waktu memiliki keragaman atau volatilitas setiap waktunya tidak konstan. Keadaan ini disebut sebagai heteroskedastisitas. Metode dapat menyelesaikan masalah tersebut adalah Autoregressive Conditional Heteroscedasticity (ARCH)/Generalized (GARCH). Namun, ARCH/GARCH mengatasi beberapa kasus seperti perbedaan dalam nilai leverage effect. Sehingga dilakukan pemod...

This research investigates the hedging effectiveness of stock index futures markets in Malaysia and Singapore by employing various hedge ratio estimation methods, which comprises of the conventional OLS model, VECM, EGARCH and bivariate GARCH. The empirical results indicate that the Kuala Lumpur Futures Index (KLFI) provides higher hedging effectiveness compared to the Straits Times Index (STI)...

In this study, the volatility of two Asian stock markets, Bursa Malaysia and Singapore Exchange, is estimated. The analysis used data on daily closing prices indices respective markets between July 1, 2019 August 31, 2020. sample split into subsample periods: Pre-COVID-19 pandemic during COVID-19 pandemic. We estimated a standard GARCH, GARCH-M, TGARCH, EGARCH PGARCH model for each subsample. c...

One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatil...

This paper examines the price volatility in the silver spot (cash) market. A host of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are used to analyze and gain a better understanding of the volatility of silver prices. We find the TGARCH (1,1) model indicates that both positive and negative shocks do not have a significant effect on volatility in the silver spot marke...

The main purpose of this paper is to estimate the volatility in global fertilizer prices. The endogenous structural breakpoint unit root test and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model are estimated for six global fertilizer prices and the crude oil price. Weekly data f...

Traditional GARCH models fail to explain at least two of the stylized facts found in financial series: the asymmetry of the distribution of errors and the leverage effect. The leverage effect stems from the fact that losses have a greater influence on future volatilities than do gains. Asymmetry means that the distribution of losses has a heavier tail than the distribution of gains. We test whe...