EMPIRICAL ANALYSIS OF LONG MEMORY AND ASYMMETRY EFFECTS FOR THE EFFECTIVENESS OF FORECASTING VOLATILITY OF RETURNS ON THE COMMODITY MARKET BASED ON THE EXAMPLE OF GOLD AND SILVER
Risk management is one of the most dynamically developed areas in economic sciences. One of the main driving forces for this development has been the practical challenge resulting from increasing financial risk. Risk management is a process in which key role is played by risk measurement (Jajuga, 2016). Comparison of various forecasting models and selection of the best ones for particular markets is of key importance in many fields of economics and finance. Theoretic aspects concerning commodity markets very often concentrate on relations between changes in commodity prices and on the news impact on rates of return. However, up until now studies concerning conditional volatility of returns on commodity markets and market risk have been less comprehensive than those concerning conditions affecting prices and rates of return. Nevertheless, studies concerning market volatility are becoming increasingly popular due to the growth of market volatility itself and
the significance of commodities as investment assets (Kang, 2013; Thuraisamy, 2013; Vivian, 2012). The growing interest also results from the fact that commodity rates of return have some empirically verifiable features such as non-normal distribution, asymmetry, structural breaks and fat tails (Aloui, 2010; Cheng, 2011).
Jméno a příjmení autora:
Bogdan Włodarczyk, Ireneusz Miciuła
Forecasting, price volatility, commodity market, risk management, long memory effect, GARCH models
DOI (& full text):
This paper presents an empirical analysis of the significance of the long memory and asymmetry effects for forecasting conditional volatility and market risk on the commodity market based on the…více
This paper presents an empirical analysis of the significance of the long memory and asymmetry effects for forecasting conditional volatility and market risk on the commodity market based on the example of gold and silver. The analysis involved testing a wide range of linear and non-linear GARCH-type models. The aim of studying dependencies between rates of return and volatility was to select the optimum model. In-sample and out-of-sample analysis indicated that volatility of returns on gold and silver is better described with non-linear volatility models accommodating long memory and asymmetry effects. In particular, the FIAPARCH model proved to be the best for estimating VaR forecasts for long and short trading positions. Also, this model generated the lowest number of violations of Basel II regulations at the confidence level of 99%. Among the models studied, the FIAPARCH has the most elastic news impact curve, which translates into more possibilities to adjust to data. The results of the analyses suggest that within the period studied, the FIAPARCH model was the best predictive tool compared to the other models. This stems from the model’s ability to satisfactorily capture the effects accompanying price volatility of precious metals, i.e. asymmetry and long memory. The FIAPARCH model produced the lowest number of VaR violations (lowest risk of the model) for all series, which means that it seems to be the most advantageous predictive model with respect to gold and silver from the point of view of financial institutions. Attention was also paid to the prevalence and significance of long memory and asymmetry effects, which should be taken into account when using GARCH-class models.