Dominik Krężołek

Selected GARCH‑type Models in the Metals Market – Backtesting of Value‑at‑Risk

Risk analysis in the financial market requires the correct evaluation of volatility in terms of both prices and asset returns. Disturbances in quality of information, the economic and political situation and investment speculations cause incredible difficulties in accurate forecasting. From the investor’s point of view, the key issue is to minimise the risk of huge losses. This article presents the results of using some selected GARCH ‑ type models, ARMA ‑ GARCH and ARMA ‑ APARCH, in evaluating volatility of asset returns in the metals market. To assess the level of risk, the Value ‑ at ‑ Risk measure is used. The comparison between real and estimated losses (in terms of VaR) is made using the backtesting procedure.

The Effectiveness of the GlueVaR Risk Measure on the Metals Market – the Application of Omega Performance Measure

Decision ‑ making process is an individual matter for each investor and the strategy they choose, reflects the level of accepted risk. Nevertheless, any investor wants to minimize huge losses while maximizing profits. As far as the measure of risk is concerned, literature is full of examples of tools which help to evaluate the risk. However, the level of the risk usually differs, depending on circumstances. In this paper we present two non ‑ classical risk measures: Omega performance risk measure and GlueVaR risk measure. Both of them require a threshold to be set, which reflects the starting point for the investment to be considered as a loss. The effectiveness of the Omega and GlueVaR risk measures is compared using the example of metals market investments.

The Application of the GlueVaR Measure in Risk Assessment on the Metal Market

The purpose of the study is the application of a new risk measure, called GlueVaR, into investment risk assessment. This measure is closely related to Value-at-Risk (VaR) and Conditional VaR (CVaR). In the literature describing theoretical background of VaR and CVaR certain properties of risk measures are highlighted. The first one is a the good risk measure has to be coherent, and the second one is that both VaR and CVaR belong to the class of distortion risk measures. As far as it is concerned, VaR is not a coherent risk measure because, it does not meet the subadditivity property. This unfulfilled property has a particular application in risk analysis, especially in extreme risk measurement. On the other hand, distortion risk measures are associated with an investor’s risk attitude which is an individual attribute of any decision-maker. The research area chosen for this study is the metal market divided into two natural sub-markets: The precious metals and the non-ferrous metals market. Risk measures as VaR, CVaR and GlueVaR are calculated and the results are associated with the investor’s attitude toward risk.

Non-Classical Measures of Investment Risk on the Market of Precious Non-Ferrous Metals Using the Methodology of Stable Distributions

The aim of this article is to present some non-classical risk measures which are commonly used in financial investments, including investments in assets from the market of precious non-ferrous metals. The time series of log-returns of gold, silver, platinum and palladium prices are considered. To properly asses the investment risk the measures based on Value-at-Risk methodology have been used (the VaR estimation approach based on values from the tail of the distribution). Additionally, the measure comparing expected profits to expected losses from the opposite tails distribution has been shown – the Rachev ratio. It was assumed that the log-returns of presented assets belong to the family of stable distributions. The results confirm the validity of the use of stable distributions to asses the risk on the precious non-ferrous metals market.