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

Krężołek, Dominik

doi:10.12775/dem.2012.006

Cited by 6 publications

(5 citation statements)

References 3 publications

(1 reference statement)

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“…The use of the GlueVaR risk measure in risk assessment is presented using the example of the precious metals market. The financial and economic crises observed in the first decade of the 21st century forced investors to search for other possibilities to invest capital which would generate positive returns [Krężołek 2012]. Among commodities the most popular, from the investment point of view, are electricity, fuel, agricultural products, precious stones and metals, etc.…”

Section: Empirical Results On the Precious Metals Marketmentioning

confidence: 99%

The Use of Value-at-Risk Methodology in the Assessment of Investor’s Risk Attitudes on the Precious Metals Market

Krężołek¹

2017

Ekonometria

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The decision making process is directly associated with risk, no matter what area of interest it is. In the case of financial investments we can define a special type of risk called investment risk. Taking into account the financial time series of assets' characteristics (prices, returns), small deviations of future values comparing to their expected level are not a major threat to the investor's portfolio. In contrast, if the changes in prices/returns are significant, unpredictable and result from unexpected and adverse events, one should pay attention to the proper risk measurement. The topic of this article refers to the new family of risk measures related to investments in assets on the precious metals market. These risk measures are called the GlueVaR risk measures. The name itself suggests that the GlueVaR is related to the commonly used measure of risk-VaR. As will be presented in the article, the family of the GlueVaR risk measures may be expressed as a linear combination of VaR and conditional VaR for fixed tolerance levels. Moreover, the new risk measure allows for assessing risk more personally, taking into account the investor's attitudes towards risk. If portfolio investments are of interest, the GlueVaR risk measures meet the assumption of subadditivity. This property of risk measure is required, as it is strongly related to the diversification problem.

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Section: Empirical Results On the Precious Metals Marketmentioning

confidence: 99%

The Use of Value-at-Risk Methodology in the Assessment of Investor’s Risk Attitudes on the Precious Metals Market

Krężołek¹

2017

Ekonometria

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“…ARFIMA-fractionally integrated GARCH (FIGARCH), ARFIMA-hyperbolic GARCH (HYGARCH), and ARFIMA-fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) models under normal inverse Gaussian, the variance gamma and the Pearson type-IV innovations, respectively, in addition to VaR estimation, were found to be suitable models for modelling the extreme risk of metal prices. Krezłek (2012) used what he referred to as stable distributions to the quantity investment hazard rate of selected non-ferrous metals. The results recommended the stable distributions as risk assessment tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models

Basira,

Dhliwayo,

Chinhamu

et al. 2024

Risks

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Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim of this research was to develop a modelling framework that could be used to accurately estimate and forecast commodity price returns by combining long memory models with heavy-tailed distributions. This study employed dual hybrid long-memory generalised autoregressive conditionally heteroscedasticity (GARCH) models with heavy-tailed innovations, namely, the Student-t distribution (StD), skewed-Student-t distribution (SStD), and the generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean squared error (MSE) values, the best performing LM-GARCH-type model for lithium is the ARFIMA (1, o, 1)-FIAPARCH (1, ξ, 1) with normal innovations. For tobacco, the best model is ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1) with SStD innovations. The robust performing model for gold is the ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1)-GED model. The best performing forecasting model for crude oil and cotton returns are the FIAPARCH 1,ξ, 1−SStD model and HYGARCH 1,ξ, 1−StD model, respectively. The results obtained from this study would be beneficial to those concerned with financial market modelling techniques, such as derivative pricing, risk management, asset allocation, and valuation.

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“…Due to financial and economic crises observed in the first decade of the 21 st century, investors have been forced to search other possibilities to invest capital, which would generate positive returns (Krężołek 2012 Figures 1-2 show significant disturbances in price levels, which affect the volatility in log-returns. If log-returns are considered, we can find some specific characteristics of time series, which are very typical for financial assets: clustering of variance, high volatility, long memory effect, etc.…”

Section: Empirical Analysis On the Non-ferrous Metals Marketmentioning

confidence: 99%

The Gluevar Risk Measure and Investor’s Attitudes to Risk–An Application to the Non-Ferrous Metals Market

Krężołek

2016

Statistics in Transition New Series

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Investing in the economic world, characterized by a high level of uncertainty and volatility, entails a higher level of risk related to investment. One of the most commonly used risk measure is Value-at-Risk. However, despite the ease of calculation and interpretation, this measure suffers from a significant drawbackit is not subadditive. This property is the key issue in terms of portfolio diversification. Another risk measure, which meets this assumption, has been proposed -Conditional Value-at-Risk, defined as a conditional loss beyond Value-at-Risk. However, the choice of a risk measure is an individual decision of an investor and it is directly related to his attitudes to risk. In this paper the new risk measure is proposed -the GlueVaR risk measure, which can be defined as a linear combination of VaR and GlueVaR. It allows for calculating the level of investment loss depending on investment's attitudes to risk. Moreover, GlueVaR meets the subadditivity property, therefore it may be used in portfolio risk assessment. The application of the GlueVaR risk measure is presented for the non-ferrous metals market.

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

Cited by 6 publications

References 3 publications

The Use of Value-at-Risk Methodology in the Assessment of Investor’s Risk Attitudes on the Precious Metals Market

The Use of Value-at-Risk Methodology in the Assessment of Investor’s Risk Attitudes on the Precious Metals Market

Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models

The Gluevar Risk Measure and Investor’s Attitudes to Risk–An Application to the Non-Ferrous Metals Market

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