Modelling and Forecasting Volatility in the Gold Market

We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast...

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Main Authors: Trück, Stefan, Liang, Kevin
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2012
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/25027/1/IJBF%209%201%202012%2048%2080.pdf
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author Trück, Stefan
Liang, Kevin
author_facet Trück, Stefan
Liang, Kevin
author_sort Trück, Stefan
collection UUM
description We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast daily volatility and VaR. Both in-sample and out-of-sample forecasts are evaluated using appropriate evaluation measures. For in-sample forecasting, the class of TARCH models provide the best results. For out-of-sample forecasting, the results were not that clear-cut and the order and specification of the models were found to be an important factor in determining model’s performance. VaR for traders with long and short positions were evaluated by comparing failure rates and a simple AR as well as a TARCH model perform best for the considered back-testing period. Overall, most models outperform a benchmark random walk model, while none of the considered models perform significantly better than the rest with respect to all adopted criteria.
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spelling uum-250272018-10-25T00:38:22Z https://repo.uum.edu.my/id/eprint/25027/ Modelling and Forecasting Volatility in the Gold Market Trück, Stefan Liang, Kevin HG Finance We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast daily volatility and VaR. Both in-sample and out-of-sample forecasts are evaluated using appropriate evaluation measures. For in-sample forecasting, the class of TARCH models provide the best results. For out-of-sample forecasting, the results were not that clear-cut and the order and specification of the models were found to be an important factor in determining model’s performance. VaR for traders with long and short positions were evaluated by comparing failure rates and a simple AR as well as a TARCH model perform best for the considered back-testing period. Overall, most models outperform a benchmark random walk model, while none of the considered models perform significantly better than the rest with respect to all adopted criteria. Universiti Utara Malaysia Press 2012 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/25027/1/IJBF%209%201%202012%2048%2080.pdf Trück, Stefan and Liang, Kevin (2012) Modelling and Forecasting Volatility in the Gold Market. International Journal of Banking and Finance (IIJBF), 9 (1). pp. 48-80. ISSN 1675-7227 http://ijbf.uum.edu.my/index.php/previous-issues/145-the-international-journal-of-banking-and-finance-ijbf-vol-9-no-1-march-2012
spellingShingle HG Finance
Trück, Stefan
Liang, Kevin
Modelling and Forecasting Volatility in the Gold Market
title Modelling and Forecasting Volatility in the Gold Market
title_full Modelling and Forecasting Volatility in the Gold Market
title_fullStr Modelling and Forecasting Volatility in the Gold Market
title_full_unstemmed Modelling and Forecasting Volatility in the Gold Market
title_short Modelling and Forecasting Volatility in the Gold Market
title_sort modelling and forecasting volatility in the gold market
topic HG Finance
url https://repo.uum.edu.my/id/eprint/25027/1/IJBF%209%201%202012%2048%2080.pdf
work_keys_str_mv AT truckstefan modellingandforecastingvolatilityinthegoldmarket
AT liangkevin modellingandforecastingvolatilityinthegoldmarket