Incorporating long memory into the modeling of gold prices
Inflation causes many people to move to gold as an option for savings because gold may be used as a hedging tool against currency devaluation and purchasing power erosion. This has contributed to the increased interest in forecasting the prices at the gold market, just like predicting the prices at...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Lviv Polytechnic National University
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119038/ http://psasir.upm.edu.my/id/eprint/119038/1/119038.pdf |
| _version_ | 1848867855788933120 |
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| author | Rashid, S. F. A. Ibrahim, S. N. I. Laham, M. F. |
| author_facet | Rashid, S. F. A. Ibrahim, S. N. I. Laham, M. F. |
| author_sort | Rashid, S. F. A. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Inflation causes many people to move to gold as an option for savings because gold may be used as a hedging tool against currency devaluation and purchasing power erosion. This has contributed to the increased interest in forecasting the prices at the gold market, just like predicting the prices at the stock market, which exhibits uncertain movement, which can be described mathematically with Geometric Brownian Motion (GBM) and Geometric Fractional Brownian Motion (GFBM). This study aims to model Malaysian gold prices using both GBM and GFBM processes and compare the accuracy of these models. Absolute moment and aggregated variance techniques are used to estimate the Hurst exponents to model the prices with GFBM. These models are simulated using the Monte Carlo simulation via the Euler scheme, where the modeled prices will be tested for their accuracy using Mean Average Percentage Error (MAPE). Based on the findings, the MAPE values for both models exhibited significantly low MAPE values, which implies high accuracy in forecasting the gold prices for a long-term period. Nevertheless, the GFBM produces much lower MAPE values than the GBM, thus indicating that the former is more accurate than the latter. |
| first_indexed | 2025-11-15T14:43:08Z |
| format | Article |
| id | upm-119038 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:43:08Z |
| publishDate | 2024 |
| publisher | Lviv Polytechnic National University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1190382025-08-05T00:09:00Z http://psasir.upm.edu.my/id/eprint/119038/ Incorporating long memory into the modeling of gold prices Rashid, S. F. A. Ibrahim, S. N. I. Laham, M. F. Inflation causes many people to move to gold as an option for savings because gold may be used as a hedging tool against currency devaluation and purchasing power erosion. This has contributed to the increased interest in forecasting the prices at the gold market, just like predicting the prices at the stock market, which exhibits uncertain movement, which can be described mathematically with Geometric Brownian Motion (GBM) and Geometric Fractional Brownian Motion (GFBM). This study aims to model Malaysian gold prices using both GBM and GFBM processes and compare the accuracy of these models. Absolute moment and aggregated variance techniques are used to estimate the Hurst exponents to model the prices with GFBM. These models are simulated using the Monte Carlo simulation via the Euler scheme, where the modeled prices will be tested for their accuracy using Mean Average Percentage Error (MAPE). Based on the findings, the MAPE values for both models exhibited significantly low MAPE values, which implies high accuracy in forecasting the gold prices for a long-term period. Nevertheless, the GFBM produces much lower MAPE values than the GBM, thus indicating that the former is more accurate than the latter. Lviv Polytechnic National University 2024 Article PeerReviewed text en cc_by_nc_4 http://psasir.upm.edu.my/id/eprint/119038/1/119038.pdf Rashid, S. F. A. and Ibrahim, S. N. I. and Laham, M. F. (2024) Incorporating long memory into the modeling of gold prices. Mathematical Modeling and Computing, 11 (4). pp. 1128-1134. ISSN 2312-9794; eISSN: 2415-3788 https://science.lpnu.ua/mmc/all-volumes-and-issues/volume-11-number-4-2024/incorporating-long-memory-modeling-gold-prices 10.23939/mmc2024.04.1128 |
| spellingShingle | Rashid, S. F. A. Ibrahim, S. N. I. Laham, M. F. Incorporating long memory into the modeling of gold prices |
| title | Incorporating long memory into the modeling of gold prices |
| title_full | Incorporating long memory into the modeling of gold prices |
| title_fullStr | Incorporating long memory into the modeling of gold prices |
| title_full_unstemmed | Incorporating long memory into the modeling of gold prices |
| title_short | Incorporating long memory into the modeling of gold prices |
| title_sort | incorporating long memory into the modeling of gold prices |
| url | http://psasir.upm.edu.my/id/eprint/119038/ http://psasir.upm.edu.my/id/eprint/119038/ http://psasir.upm.edu.my/id/eprint/119038/ http://psasir.upm.edu.my/id/eprint/119038/1/119038.pdf |