The aim of this study is to investigate the volatility movements in natural gas returns, which one financial investment instruments futures markets, before and after Covid-19 pandemic, using GARCH family models. For purpose, daily data from 30.08.2017 10.03.2020 Pandemic, 11.03.2020 21.09.2021 Pandemic were used. return on was expressed as RLNPO RLNPS. RLNPO, TGARCH determined most suitable mod...

This paper proposes a class of nonlinear stochastic volatility models. The proposed class encompasses many parametric stochastic volatility models that have appeared in the literature, including the well known lognormal stochastic volatility model. The new class is based on the Box-Cox transformation and offers an alternative to the one introduced in Andersen (1994). An advantage of our propose...

In this work, we study the famous model of volatility; called conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The has two mixing components, one is a GARCH short and other long memory. main objective search finds best between mixtures models made (long memory, memory) Also, existence its stationary solution discussed. Monte Carlo experime...

Volatility is an important concept for identifying and predicting the risk of financial products. The aim study to determine most appropriate discrete model volatility Bitcoin returns using discrete-time GARCH its extensions compare it with Lévy driven continuous-time model. For this purpose, modeled daily data / United States Dolar exchange rate. By comparing models according information crite...

Financial markets have an important role in the economy of a country including Indonesia. One activities chosen by investors financial market is investing. In world investment, especially stocks, there phenomenon volatility, which situation where stock price value increases and decreases. Volatility this something that very interesting for because its impact on existence global markets. The pur...

Multivariate probability density functions of returns are constructed in order to model the empirical behavior of returns in a financial time series. They describe the well-established deviations from the Gaussian random walk, such as an approximate scaling and heavy tails of the return distributions, long-ranged volatility-volatility correlations (volatility clustering) and return-volatility c...

Modelling and forecasting volatility of a financial time series has been significant area research in recent years, owing to the fact that is regarded as an essential notion many economic applications. Because not directly observable, analysts are especially eager obtain accurate estimation this conditional variance process. As result, number models have developed specifically suited estimate i...

All countries consume crude oil or oil products. Both producers and consumers are highly concerned about crude oil prices. The crude oil prices are being directly affecting by several factors such as economic, political, geopolitical, and technological, oil reserves, available stocks and weather conditions, among others. On other hand the crude oil prices fluctuations influence directly the wor...

the present study aimed at investigating the existence of long memory properties in ten emerging stock markets across the globe. when return series exhibit long memory, it indicates that observed returns are not independent over time. if returns are not independent, past returns can help predict future returns, thereby violating the market efficiency hypothesis. it poses a serious challenge to ...

This study considers three alternative sources of information about volatility potentially useful in predicting daily asset returns: daily returns, intraday returns, and option prices. For each source of information the study begins with several alternative models, and then works from the premise that all of these models are false to construct a single improved predictive distribution for daily...