Modeling of Gold coin futures with stochastic differential equations
Subject Areas :
Financial engineering
Rahele Baqeri
1
,
mohammadreza setayesh
2
,
Reza Radfar
3
1 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran,Iran
2 - Department of Accounting, Science and Research Branch, Islamic Azad University, Tehran,Iran
3 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran,Iran
Received: 2020-06-29
Accepted : 2020-07-13
Published : 2020-12-21
Keywords:
Brownian Motion,
Stochastic differential equations,
Predicting the Price of Future Gold Coin Contracts,
Random Process,
Abstract :
The capital market is one of the financial markets that in a dynamic economy can pave the way for long-term economic growth.Futures contracts that derive their values from an underlying asset, are included these financial instruments.To enter the futures market, the investor needs to anticipate future trends to cover his risk. For this purpose, the appropriate random differential equation has been selected to model the prediction of future coin contracts in the present study.Thus, after providing the necessary explanations about the necessity of using random models and as a result of new principles called random accounts, to introduce the most important stochastic differential equation in financial sciences including geometric Brownian, geometric Brownian with jump term, Heston and the explained model are discussed. Then, the appropriate model is selected, with a practical approach and based on the ability of each model to predict the price of futures contracts by assembling the Monte Carlo.The results of the fitness criteria regarding the predictive power indicate the superiority of the model explained in these contracts.
References:
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Hafezi, R., & Akhavan, A. (2018). Forecasting gold price changes: Application of an equipped artificial neural network. AUT Journal of Modeling and Simulation, 50(1), 71-82.
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Cortazar, G., Millard, C., Ortega, H., & Schwartz, E. S. (2019). Commodity price forecasts, futures prices, and pricing models. Management Science, 65(9), 4141-4155.
Coyle, C., Gogolin, F., & Kearney, F. (2019). Modelling gold futures: should the level of speculation inform our choice of variables The European Journal of Finance, 25(10), 966-977.
Dastranj, E., Fard, H. S., Abdolbaghi, A., & Hejazi, S. R. (2020). Power option pricing under the unstable conditions (Evidence of power option pricing under fractional Heston model in the Iran gold market). Physica A: Statistical Mechanics and its Applications, 537, 122690.
Fernandez-Perez, A., Fuertes, A. M., González-Fernández, M., & Miffre, J. (2019). Fear of Hazards in Commodity Futures Markets. Available at SSRN 3411117.
Garboden, P. M. (2020). Sources and Types of Big Data for Macroeconomic Forecasting. In Macroeconomic Forecasting in the Era of Big Data (pp. 3-23). Springer, Cham.
Horváth, L., Liu, Z., Rice, G., & Wang, S. (2019). A functional time series analysis of forward curves derived from commodity futures. International Journal of Forecasting.
Hua, Q., & Jiang, T. (2015). The prediction for London gold price: improved empirical mode decomposition. Applied Economics Letters, 22(17), 1404-1408.
Khan, S., & Bhardwaj, S. (2019). Time Series Forecasting of Gold Prices. In Emerging Trends in Expert Applications and Security (pp. 63-71). Springer, Singapore.
Lu, W., Geng, C., & Yu, D. (2019). A New Method for Futures Price Trends Forecasting Based on BPNN and Structuring Data. IEICE Transactions on Information and Systems, 102(9), 1882-1886.
Maréchal, L. (2019). A comprehensive look at commodity volatility forecasting.
Nguyen, D. K., & Walther, T. (2019). Modeling and forecasting commodity market volatility with long‐term economic and financial variables. Journal of Forecasting.
Oral, E., & Unal, G. (2019). Modeling and forecasting time series of precious metals: a new approach to multifractal data. Financial Innovation, 5(1), 3.
Rathnayaka, R. K. T., & Seneviratna, D. M. K. N. (2019). Taylor series approximation and unbiased GM (1, 1) based hybrid statistical approach for forecasting daily gold price demands. Grey Systems: Theory and Application, 9(1), 5-18.
Salisu, A. A., Ogbonna, A. E., & Adewuyi, A. (2020). Google trends and the predictability of precious metals. Resources Policy, 65, 101542.
Wang, Y., Cao, X., Sui, X., & Zhao, W. (2019). How do black swan events go global Evidence from US reserves effects on TOCOM gold futures prices. Finance Research Letters, 31.
Wei, Y., Liang, C., Li, Y., Zhang, X., & Wei, G. (2019). Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models. Finance Research Letters.
Weng, F., Chen, Y., Wang, Z., Hou, M., Luo, J., & Tian, Z.(2020) Gold price forecasting research based on an improved online extreme learning machine algorithm. Journal of Ambient Intelligence and Humanized Computing, 1-11.
Yamaka, W., & Maneejuk, P. (2020). Analyzing the Causality and Dependence between Gold Shocks and Asian Emerging Stock Markets: A Smooth Transition Copula Approach. Mathematics, 8(1), 120.
Yan, L., Irwin, S. H., & Sanders, D. R. (2019). Is the Supply Curve for Commodity Futures Contracts Upward Sloping?. Available at SSRN 3360787.
Zainal, N. A., & Mustaffa, Z. (2016, December). Developing a gold price predictive analysis using Grey Wolf Optimizer. In 2016 IEEE student conference on research and development (SCOReD) (pp. 1-6). IEEE.
Xiao, C., Xia, W., & Jiang, J. (2020). Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Computing and Applications, 1-10.
Lin, L., Jiang, Y., Xiao, H., & Zhou, Z. (2020). Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model. Physica A: Statistical Mechanics and its Applications, 123532.
Hafezi, R., & Akhavan, A. (2018). Forecasting gold price changes: Application of an equipped artificial neural network. AUT Journal of Modeling and Simulation, 50(1), 71-82.
Batten, J. A., Ciner, C., Kosedag, A., & Lucey, B. M. (2017). Is the price of gold to gold mining stocks asymmetric?. Economic Modelling, 60, 402-407.
Reboredo, J. C., & Ugolini, A. (2017). Quantile causality between gold commodity and gold stock prices. Resources Policy, 53, 56-63.
Liu, D., & Li, Z. (2017). Gold price forecasting and related influence factors analysis based on random forest. In Proceedings of the Tenth International Conference on Management Science and Engineering Management (pp. 711-723). Springer, Singapore.
Sihananto, A. N., & Bachtiar, F. A. (2017, November). Gold price movement forecasting using hybrid ES-FIS. In 2017 International Conference on Sustainable Information Engineering and Technology (SIET) (pp. 321-326). IEEE.
Fang, L., Chen, B., Yu, H., & Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach. Journal of Futures Markets, 38(3), 413-422.
