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Volume 18 -
ijmt 2023, 18 - : 52-57 |
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Pourkermani K. Modelling and Forecasting Yield Volatility of Baltic Exchange Dry Index. ijmt 2023; 18 :52-57
URL: http://ijmt.ir/article-1-792-en.html
URL: http://ijmt.ir/article-1-792-en.html
Khorramshahr University of Marine Sciences and Technology
Abstract: (763 Views)
Purpose – Baltic Dry Index (BDI) is shipping freight-cost index which is reported daily by Baltic Exchange. The index is a benchmark for the prices of ship chartering contracts which is a proxy for the maritime economy, BDI is heavily used by financial traders to predict the world economy, the volatility forecast has an important implication for all the investors and hence in this paper the daily forecast performance of different models is evaluated.
Research methodology – The daily forecast performance of conditional and unconditional volatility of 12 long memory GARCH-type models based on the root-mean-square error (RMSE) is evaluated. Because all return series were skewed and fat-tailed, each conditional volatility model was estimated under a skewed Student distribution.
Findings – According to the idea that the accuracy of Value-at-Risk (VaR) estimates was sensitive to the adequacy of the volatility model used, the result showed that the 250-day moving average models, exponential smoothing, and (component GARCH) CGARCH function better than other models based on RMSE standard. The results of hybrid models such as Dibold-Mariano statistics showed that there was no significant difference between the predictive power of 250 days moving average (MA250) and CGARCH.
Practical implications – BDI was widely regarded as a benchmark for the world economy by traders and hedge fund managers.
Originality/Value – we examine the science of volatility prediction in BDI which has not been performed before.
Research methodology – The daily forecast performance of conditional and unconditional volatility of 12 long memory GARCH-type models based on the root-mean-square error (RMSE) is evaluated. Because all return series were skewed and fat-tailed, each conditional volatility model was estimated under a skewed Student distribution.
Findings – According to the idea that the accuracy of Value-at-Risk (VaR) estimates was sensitive to the adequacy of the volatility model used, the result showed that the 250-day moving average models, exponential smoothing, and (component GARCH) CGARCH function better than other models based on RMSE standard. The results of hybrid models such as Dibold-Mariano statistics showed that there was no significant difference between the predictive power of 250 days moving average (MA250) and CGARCH.
Practical implications – BDI was widely regarded as a benchmark for the world economy by traders and hedge fund managers.
Originality/Value – we examine the science of volatility prediction in BDI which has not been performed before.
Type of Study: Research Paper |
Subject:
Maritime Transport and Port Management
Received: 2022/02/9 | Accepted: 2023/11/5
Received: 2022/02/9 | Accepted: 2023/11/5
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