Forecasting the Global Gold Price Movement with Marginal Distribution Modeling Approach: An Application of the Copula GARCH Gaussian and t

Subject Areas : Financial engineering

Mohammad reza  Haddadi ¹ (Department of Mathematics, Ayatollah Boroujerdi University, Boroujerd, Iran.)
Younes  Nademi ² (Department of Economics, Ayatollah Boroujerdi University, Boroujerd, Iran.)
Hamed  Farhadi ³ (Department of Mathematics, Ayatollah Boroujerdi University, Boroujerd, Iran.)

Received: 2019-11-03 Accepted : 2020-01-11 Published : 2020-03-20

Keywords: Forecast, Time series, COPULA, gold price, GARCH-Copula Model,

Abstract :

Given the importance of gold prices in financial markets and the economic effects of price fluctuations, the trend of gold price changes in the national and global economy has attracted the attention of many researchers and economic analysts. Therefore, the main purpose of this study is to predict the trend of the global gold price movement. The purpose of this study was to introduce a combined model of the GARCH-Classic and the GARCH-Copula models and to compare them with the Garch family models in order to predict the global gold price trend in the period 01/04/2002 to 26/06/2018. The forecast horizons are 1, 5, 10, and 22 days. The prediction accuracy of these models has been evaluated and compared using RMSE error criterion. Results showed that in short-run prediction horizons, the normal Capula model with GARCH-t distribution and in long run prediction horizon, Capula- t model with the distribution of GARCH-t performs better than competing models. The hybrid model presented in this study has a high potential for predicting the trend of global gold price movement, so using this model for different sector investors, economic analysts, as well as country macro planners, can have valuable results.

References:

اسکندری، بهزاد (1396). کاربرد فیگارچ در پیش‌بینی نوسانات طلا و ارز و اثرات آن‌ها بر شاخص بورس اوراق بهادار تهران. پایان‌نامه کارشناسی ارشد. دانشده علوم پایه. دانشگاه آیت الله بروجردی (ره). بروجرد.

امیر‌حسینی، زهرا؛ داور‌پناه، عاطفه (1395). طراحی الگویی جهت پیش‌بینی قیمت طلا، با استفاده از الگوریتم پرواز پرندگان و الگوریتم ژنتیک و الگوریتم ترکیبی. مجله مهندسی مالی و مدیریت اوراق بهادار، شماره 26، ص 59-83.

بکی حسکوئی، مرتضی؛ خواجه‌وند، فاطمه (1393). پیش‌بینی نوسانات بازار‌های آتی نفت با استفاده از مدل‌های گارچ و مدل‌های تغیر رژیم مارکوف گارچ . فصلنامه دانش مالی تحلیل اوراق بهادار، شماره 23، ص 85-108.

زراء‌نژاد، منصور؛ رئوفی، علی؛ کیانی، پویان (1391). ارزیابی و مقایسه عملکرد مدل خودرگرسیون میانگین متحرک انباشته و شبکه عصبی فازی برای پیش‌بینی روزانه قیمت طلا. اولین کنفرانس بین المللی اقتصادسنجی، روش‌ها و کاربرد‌ها. دانشگاه آزاد واحد اسلامی سنندج، سنندج.

سر فراز، لیلا؛ افسر، امیر (1384). بررسی عوامل بر قیمت طلا و ارائه مدل و پیش‌بینی برمبنای شبکه‌های عصبی فازی. فصلنامه پژوهشی اقتصادی، شماره 16، ص 149-165.

مشیری، سعید (1380). پیش‌بینی تورم ایران با استفاده از مدل‌های ساختاری، سری‌های زمانی و شبکه‌های عصبی. مجله تحقیقات اقتصادی, شماره 58، ص 147-184

نصیری، فرشاد؛ حسنلو، خدیجه؛ ابراهیمی، سید بابک (1393). برآورد ارزش در معرض خطر پرتقوی سرمایه‌گذاری با استفاده از مدل‌های کاپولا-گارچ مطالعه موردی: بازا ارز، طلا و سهام. کنفرانس بین المللی مدیریت و مهندسی صنایع. تهران.

Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250-260.

 

Bentes, S. (2015). Forecasting Volatility in Gold returns underThe GARCH, IGARCH, and FIGARCH. New evidence, ELSVIER, Physica, Vol (438), PP 355- 364.

Bollerslev, T. (1986). Generalized Autoreyressive Conditional Heteroskedasticity. Journ of Economics, Vol (31), PP 307- 32.

Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula Methods in Finance. West Sussex.

Dury, M., & Xiao, B. ( 2018). Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models.

Embrechts, P., & Lindckog, F., McNeil, A. (2001). Modelling Dependence witch Copula and Applications to Risk Management. Handbook of Heavy Taild Distributions in Finance, PP, 329-384.

Engle, F. (1982). Autoregressive Conditional Heteroscedasticity Witch Estimates if the Variance if United Kingdom Inflation. Econometrica, PP, 987-1007.

Kumar, S. (2019). Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models. The Journal of Prediction Markets, 12(3), 63-78.

Krishna, K. M., Reddy, N. K., & Sharma, M. R. (2019). Forecasting of Daily Prices of Gold in India using ARIMA and FFNN Models. International Journal of Engineering and Advanced Technology (IJEAT)ISSN2249 –8958,Volume-8 Issue-3.

Sklar, A. (1959). Fonctions de repartition a n dimensions et leurs marges. Publications de Institut de Statistique de I Univercite de Paris , (8), PP, 299-331.

Sopipan, N. (2017). Trading Gold Future with ARIMA_ GARCH models. Thai Journal of Mathematics, Special Issue, Ahnual Meeting in Matematics.

Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250-260.

Amirhosseini, Zahra; Davrpanah, Atefeh (2015). Designing a model to predict the price of gold, using the bird flight algorithm, genetic algorithm, and hybrid algorithm. Journal of Financial Engineering and Securities Management, No. 26, pp. 59-83.

Becky Haskoi, Morteza; Khajakhund, Fatemeh (2014). Forecasting the fluctuations of future oil markets using Garch models and Markov Garch regime change models. Financial Knowledge Quarterly of Securities Analysis, No. 23, pp. 85-108.

Bentes, S. (2015). Forecasting Volatility in Gold returns underThe GARCH, IGARCH, and FIGARCH. New evidence, ELSVIER, Physica, Vol (438), PP 355- 364.

Bollerslev, T. (1986). Generalized Autoreyressive Conditional Heteroskedasticity. Journ of Economics, Vol (31), PP 307- 32.

Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula Methods in Finance. West Sussex.

Dury, M., & Xiao, B. ( 2018). Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models.

Embrechts, P., & Lindckog, F., McNeil, A. (2001). Modelling Dependence witch Copula and Applications to Risk Management. Handbook of Heavy Taild Distributions in Finance, PP, 329-384.

Engle, F. (1982). Autoregressive Conditional Heteroscedasticity Witch Estimates if the Variance if United Kingdom Inflation. Econometrica, PP, 987-1007.

Eskandari, Behzad (2016). Application of Figarch in predicting gold and currency fluctuations and their effects on Tehran Stock Exchange index. Master's thesis. Faculty of Basic Sciences. Ayatollah Boroujerdi University (RA). Borujerd

Krishna, K. M., Reddy, N. K., & Sharma, M. R. (2019). Forecasting of Daily Prices of Gold in India using ARIMA and FFNN Models. International Journal of Engineering and Advanced Technology (IJEAT)ISSN2249 –8958,Volume-8 Issue-3.

Kumar, S. (2019). Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models. The Journal of Prediction Markets, 12(3), 63-78.

Moshiri, Saeed (1380). Forecasting Iran's inflation using structural models, time series and neural networks. Journal of Economic Research, No. 58, pp. 147-184

Nasiri, Farshad; Hassanlou, Khadija; Ebrahimi, Seyed Babak (2013). Estimating the value at risk of prospective investments using Copula-Garch models, a case study: Baza, gold and stocks. International Conference on Management and Industrial Engineering. Tehran.

Sar Faraz, Leila; Afsar, Amir (1384). Examining the factors on the price of gold and presenting a model and prediction based on fuzzy neural networks. Economic Research Quarterly, No. 16, pp. 149-165.

Sklar, A. (1959). Fonctions de repartition a n dimensions et leurs marges. Publications de Institut de Statistique de I Univercite de Paris , (8), PP, 299-331.

Sopipan, N. (2017). Trading Gold Future with ARIMA_ GARCH models. Thai Journal of Mathematics, Special Issue, Ahnual Meeting in Matematics.

Zaranjad, Mansour; Reofi, Ali; Kiani, Pouyan (2011). Evaluation and comparison of performance of cumulative moving average autoregressive model and fuzzy neural network for daily forecasting of gold price. The first international conference on econometrics, methods and applications. Islamic Azad University of Sanandaj, Sanandaj.