Modelling dynamic conditional heteroscedasticity is the daily routine in time series econometrics. We propose a weighted conditional moment estimation to potentially improve the efficiency of the QMLE (quasi maximum likelihood estimation). The weights of conditional moments are selected based on...

This paper focuses on modeling the evolution of volatility deterministically through (G)ARCH models and compares the performance of the different models, using the daily bilateral prices of one USD to 1 Euro from January 04, 1999 until June 23, 2017. It also compares the performance of these...

This article employed the ARCH, GARCH and EGARCH models to model the oil price volatility and macroeconomic variables in South Africa for the period 1990Q1 to 2018Q2. The macroeconomic variables used in the study are GDP, inflation, interest rate and exchange rates. According to ARCH (1) and...

In this paper, two univariate generalised autoregressive conditional heteroskedasticity (GARCH) option pricing models are applied to Bitcoin and the Cryptocurrency Index (CRIX). The first model is symmetric and the other takes asymmetric effects into account. Furthermore, the accuracy of the...

This paper studies the effect of new gold derivatives products, including Gold-D and Gold Online Futures, on the futures price volatility of existing gold futures with two contract sizes, 50 baht-weight and 10 baht-weight, using symmetric and asymmetric GARCH family models, namely: GARCH (1,1),...

We employ the Markov Regime-Switching GARCH (MRS- GARCH) family models under the normal, Student's t-, and GED distributions to measure the uncertainty of the industry index returns (IIR) of Tehran Stock Exchange over the period of 2013-2019. The models distinguish between two different regimes...