Long Memory and Time Varying Hedging Opportunities Between Clean Energy, Crude Oil and Technology Sector

In this paper, long memory and time varying hedging opportunities between clean energy, West Texas Intermediate (WTI) crude oil and technology share prices were analyized between 3 May 2005-16 October 2019. The relationships were investigated by DECO-FIGARCH model with daily frequencies. According to findings, it is understood that volatility clusters were determined in crude oil, alternate source energy and technology returns. Due to this useful information shocks reach to all three investment tools and being eliminated at hyperbolic speed, also the volatility spillover lasted for a long time. The most important finding of the research is that long position risks arising in both clean energy and technology sectors can be effectively and efficiently hedged with WTI futures contracts. On the other hand, it was determined that WTI can be added to the portfolio in order to reduce the risks of portfolio to be established with clean energy and technology sector.

Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.

Stock Returns and Long-range Dependence

This article studies the long memory behaviour of stock returns on the Ghana Stock Exchange. The estimates employed are based on the daily closing prices of seven stocks on the Ghana Stock Exchange. The results of the autoregressive fractionally integrated moving average-fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH) model suggest that the stock returns are characterized by a predictable component; this demonstrates a complete departure from the efficient market hypothesis suggesting that relevant market information was only partially reflected in the changes in stock prices. This pattern of time dependence in stock returns may allow for past information to be used to improve the predictability of future returns.

Modeling Long Range Dependence in Wheat Food Price Returns

The present paper focuses on analyzing the volatility dynamics of wheat commodity based on the presence of long memory. The paper utilizes several econometric tests to identify the presence and magnitude of the fractional difference parameter. Fractional GARCH models, namely FIGARCH and FIEGARCH, are employed to examine the long memory property. Twenty years of wheat daily price data were used to study the long-range dependence. The results reveal that fractional integration is found in the daily wheat price return series. Overall, the FIGARCH model seems a better fit, in describing the time-varying volatility of the commodity adequately, compared to the FIEGARCH model. Food price shocks are likely to persist for a long time for wheat, resulting in higher market risk for producers and increased purchasing costs for consumers.

Modeling the risk-return characteristics of the SB1 Mexican private pension fund index

This paper analyzes the returns and variance behavior of the largest specialized private pension investment funds index in Mexico, the SIEFORE Básica 1 (or, SB1). The analysis was carried out with time series techniques to model the returns and volatility of the SB1, using publicly available historical data for SB1. Like many standard financial time series, the SB1 returns show non-normality, volatility clusters and excess kurtosis. The econometric characteristics of the series were initially modeled using three GARCH family models: GARCH (1,1), TGARCH and IGARCH. However, due to the presence of highly persistent volatility, the series modeling was extended using Fractionally Integrated GARCH (FIGARCH) methods. To that end, an extended specification: an ARFIMA (p,d,q) and a FIGARCH model were incorporated. The evidence obtained suggests the presence of long memory effects both in the returns and the volatility of the SB1. Our analysis’ results have important implications for the risk management of the SB1. Keywords: Private Pension Funds, Time Series modelling, GARCH models, Long Term memory series

An Overview of FIGARCH and Related Time Series Models

This paper reviews the theory and applications related to fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the observed persistence in the volatility of a time series. The long memory nature of FIGARCH models allows to be a better candidate than other conditional heteroscedastic models for modeling volatility in exchange rates, option prices, stock market returns and inflation rates. We discuss some of the important properties of FIGARCH models inthis review. We also compare the FIGARCH with the autoregressive fractionally integrated moving average (ARFIMA) model. Problems related to parameter estimation and forecasting using a FIGARCH model are presented. The application of a FIGARCH model to exchange rate data is discussed. We briefly introduce some other models, that are closely related to FIGARCH models. The paper ends with some concluding remarks and future directions of research.