Research Article
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Year 2024, Volume: 4 Issue: 2, 165 - 192, 30.06.2024
https://doi.org/10.53391/mmnsa.1416148

Abstract

References

  • [1] Din, A., Sabbar, Y. and Wu, P. A novel stochastic Hepatitis B virus epidemic model with second-order multiplicative α-stable noise and real data. Acta Mathematica Scientia, 44, 752-788, (2024).
  • [2] Zhang, G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175, (2003).
  • [3] Nisar, K.S. and Sabbar, Y. Long-run analysis of a perturbed HIV/AIDS model with antiretroviral therapy and heavy-tailed increments performed by tempered stable Lévy jumps. Alexandria Engineering Journal, 78, 498-516, (2023).
  • [4] Sabbar, Y., Khan, A., Din, A. and Tilioua, M. New method to investigate the impact of independent quadratic α-stable Poisson jumps on the dynamics of a disease under vaccination strategy. Fractal and Fractional, 7(3), 226, (2023).
  • [5] Sabbar, Y. Asymptotic extinction and persistence of a perturbed epidemic model with different intervention measures and standard Lévy jumps. Bulletin of Biomathematics, 1(1), 58-77, (2023).
  • [6] Ru, Y. and Ren, H.J. Application of ARMA model in forecasting aluminum price. in: Applied Mechanics and Materials, Vol. 155, Trans Tech Publ, pp. 66-71, (2012).
  • [7] Rossen, A. What are metal prices like? Co-movement, price cycles and long-run trends. Resources Policy, 45, 255–276, (2015).
  • [8] Haque, M.A., Topal, E. and Lilford, E. Iron ore prices and the value of the Australian dollar. Mining Technology, 124(2), 107-120, (2015).
  • [9] Cortez, C.T., Saydam, S., Coulton, J. and Sammut, C. Alternative techniques for forecasting mineral commodity prices. International Journal of Mining Science and Technology, 28(2), 309-322, (2018).
  • [10] Lee, J., List, J.A. and Strazicich, M.C. Non-renewable resource prices: Deterministic or stochastic trends?. Journal of Environmental Economics and Management, 51(3), 354–370, (2006).
  • [11] Uhlenbeck, G.E. and Ornstein, L.S. On the theory of the Brownian motion. Physical Review, 36(5), 823, (1930).
  • [12] Vasicek, O. An equilibrium characterization of the term structure. Journal of Financial Economics, 5(2), 177-188, (1977).
  • [13] Wylomanska, A. The dependence structure for symmetric α-stable CARMA (p, q) processes. In Proceedings, Workshop on Cyclostationary Systems and Their Applications (CSTA), pp. 189-206, Springer, (2014, February).
  • [14] Brockwell, P.J. Continuous-time ARMA processes. Handbook of Statistics, 19, 249-276, (2001).
  • [15] Tully, E. and Lucey, B.M. A power GARCH examination of the gold market. Research in International Business and Finance, 21(2), 316-325, (2007).
  • [16] Wyłoma´nska, A. Measures of dependence for Ornstein–Uhlenbeck process with tempered stable distribution. Acta Physica Polonica B, 42(10), 2049-2062, (2011).
  • [17] Obuchowski, J. and WYŁOMANSKA, A. The Ornstein–Uhlenbeck process with non-Gaussian structure. Acta Physica Polonica B, 44(5), 1123-1136, (2013).
  • [18] Nolan, J.P. Computational aspects of stable distributions. Wiley Interdisciplinary Reviews: Computational Statistics, 14(4), e1569, (2021).
  • [19] Zolotarev V.M., On representation of stable laws by integrals. Selected Translation in Mathematical Statistics and Probability, 6, 84-88, (1966).
  • [20] McCulloch, J.H. Simple consistent estimators of stable distribution parameters. Communications in Statistics-Simulation and Computation, 15(4), 1109–1136, (1986).
  • [21] Ho, T.S.Y., Lee, S.B. Term structure movements and pricing interest rate contingent claims. The Journal of Finance, 41(5), 1011-1029, (1986).
  • [22] Hull, J. and White, A. Pricing interest-rate-derivative securities. The Review of Financial Studies, 3(4), 573-592, (1990).
  • [23] Nolan, J.P. Univariate Stable Distributions: Models for Heavy Tailed Data. Springer: Switzerland, (2020).
  • [24] Zhang, S. and Zhang, X. A least squares estimator for discretely observed Ornstein-Uhlenbeck processes driven by symmetric α-stable motions. Annals of the Institute of Statistical Mathematics, 65, 89-103, (2013).
  • [25] Hu, Y. and Long, H. Parameter estimation for Ornstein–Uhlenbeck processes driven by α-stable Lévy motions. Communications on Stochastic Analysis, 1(2), 175-192, (2007).
  • [26] Cui, H. Estimation in partial linear EV models with replicated observations. Science in China Series A: Mathematics, 47, 144, (2004).
  • [27] Fan, J., Jiang, J., Zhang, C. and Zhou, Z. Time-dependent diffusion models for term structure dynamics. Statistica Sinica, 13(4), 965-992, (2003).
  • [28] Lévy P. Théorie des erreurs. La loi de Gauss et les lois exceptionnelles. Bulletin de la Société Mathématique de France, 52, 49-85, (1924).
  • [29] McCulloch, J.H. 13 Financial applications of stable distributions. Handbook of Statistics, 14, 393-425, (1996).
  • [30] Mittnik, S., Rachev, S.T., Doganoglu, T. and Chenyao, D. Maximum likelihood estimation of stable Paretian models. Mathematical and Computer Modelling, 29(10-12), 275–293, (1999).
  • [31] Nolan, J.P. Modeling financial data with stable distributions. In Handbook of Heavy Tailed Distributions in Finance (pp. 105–130). North-Holland, Holland: Elsevier, (2003).
  • [32] Rachev, S.T. and Mittnik, S. Stable Paretian models in finance. New York: Wiley: (2000).
  • [33] Samorodnitsky, G., Taqqu, M.S. and Linde, R.W. Stable non-gaussian random processes: stochastic models with infinite variance. New York; London: Chapman & Hall, (1994).
  • [34] Zolotarev, A. One-Dimensional Stable Distributions. USA: American Mathematical Society, Providence, (1986).
  • [35] Fama, E.F. and Roll, R. Parameter estimates for symmetric stable distributions. Journal of the American Statistical Association, 66, 331–338, (1971).
  • [36] Mandelbrot, B.B. The variation of certain speculative prices. In: Fractals and Scaling in Finance. New York: Springer, (1997).
  • [37] Bachelier, L. Théorie de la spéculation. Annales scientifiques de l’École Normale Supérieure, Serie 3, Vol 17, 21-86, (1900).
  • [38] Chambers, J.M., Mallows, C.L. and Stuck, B.W. A method for simulating stable random variables. Journal of the American Statistical Association, 71, 340–344, (1976).
  • [39] Koutrouvelis, I.A. An iterative procedure for the estimation of the parameters of stable laws. Communications in Statistics-Simulation and Computation, 10, 17-28, (1981).
  • [40] Koutrouvelis, I.A. Regression-type estimation of the parameters of stable laws. Journal of the American Statistical Association, 75, 918–928, (1980).
  • [41] Press, S.J. Estimation in univariate and multivariate stable distributions. Journal of the American Statistical Association, 67, 842–846, (1972).
  • [42] Weron, R. Performance of the estimators of stable law parameters. Hugo Steinhaus Center, Wroclaw University of Technology, HSC Research Reports, HSC/95/01, (1995).
  • [43] Nolan, J.P. Numerical calculation of stable densities and distribution functions. Communications in Statistics. Stochastic Models, 13(4), 759–774, (1997).
  • [44] Weron, A. and Weron, R. Inzynieria finansowa. HSC Books: Wydawnictwo NaukowoTechniczne, Warszawa, (1998).
  • [45] Revuz, D. and Yor, M. Continuous Martingales and Brownian Motion (Vol. 293). Springer: Berlin, pp. 14-39, (1991).
  • [46] Merton, R.C. Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141-183, (1973).
  • [47] Brennan, M.J. and Schwartz, E.S. An equilibrium model of bond pricing and a test of market efficiency. Journal of Financial and Quantitative Analysis, 17(3), 301-329, (1982).
  • [48] Dothan, L.U. On the term structure of interest rates. Journal of Financial Economics, 6(1), 59-69, (1978).
  • [49] Cox, J.C., Ingersoll Jr., J.E. and Ross, S.A. A theory of the term structure of interest rates. Econometrica, 53(2), 385-407, (1985).
  • [50] Black, F. and Karasinski, P. Bond and option pricing when short rates are lognormal. Financial Analysts Journal, 47(4), 52-59, (1991).
  • [51] Hastie, T., Friedman, J. and Tibshirani, R. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, (2009).
  • [52] Marsden, J. and Weinstein, A. Calculus II, Springer: New York, (1985).
  • [53] Saleh, A.M.E., Arashi, M. and Tabatabaey, S.M.M. Statistical Inference for Models with Multivariate T-Distributed Errors. John Wiley & Sons: USA, (2014).
  • [54] Cont, R. Encyclopedia of Quantitative Finance. (Vol. 4). John Wiley & Sons: USA, (2010).
  • [55] W Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829-836, (1979).
  • [56] Jaditz, T. and Riddick, L.A. Time-series near-neighbor regression. Studies in Nonlinear Dynamics & Econometrics, 4(1), 35-44, (2000).
  • [57] Elliott, G., Rothenberg, T.J. and Stock, J.H. Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813-836, (1996).
  • [58] Pozzi, F., Di Matteo, T. and Aste, T. Exponential smoothing weighted correlations. The European Physical Journal B, 85(175), 1-21, (2012).
  • [59] Breusch, T.S. and Pagan, A.R. The Lagrange multiplier test and its applications to model specifications in econometrics. The Review of Economic Studies, 47(1), 239-253, (1980).
  • [60] Nolan, J.P. Maximum likelihood estimation of stable parameters. In Levy processes: Theory and applications. (pp. 379–400). Boston: Birkhauser, (2001).
  • [61] Tan, P. and Drossos, C. Invariance properties of maximum likelihood estimators. Mathematics Magazine, 48, 37-41, (1975).
  • [62] Shanno, D.F. Conditioning of quasi-Newton methods for function minimization. Mathematics of Computation, 24(111), 647-656, (1970).
  • [63] Fox, L. and Mayers, D.F. Numerical Solution of Ordinary Differential Equations. Chapman and Hall: London, (1987).
  • [64] Stephens, M.A. EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730-737, (1974).
  • [65] Brandimarte, P. Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics. John Wiley & Sons: USA, (2014).
  • [66] Sikora, G., Michalak, A., Bielak, Ł., Mi´sta, P. and Wyłoma´nska, A. Stochastic modeling of currency exchange rates with novel validation techniques. Physica A: Statistical Mechanics and its Applications, 523, 1202-1215, (2019).

An approach to stochastic differential equations for long-term forecasting in the presence of $\alpha$-stable noise: an application to gold prices

Year 2024, Volume: 4 Issue: 2, 165 - 192, 30.06.2024
https://doi.org/10.53391/mmnsa.1416148

Abstract

This article introduces a novel approach to forecasting gold prices over an extended period by leveraging a sophisticated stochastic process. Departing from traditional models, our proposed framework accommodates the non-Gaussian and non-homogeneous nature of gold market dynamics. Rooted in the $\alpha$-stable distribution, our model captures time-dependent characteristics and exhibits flexibility in handling the distinctive features observed in real gold prices. Building upon prior research, we present a comprehensive methodology for estimating time-dependent parameters and validate its efficacy through simulations. The results affirm the universality of our stochastic model, showcasing its applicability for accurate and robust long-term predictions in gold prices.

References

  • [1] Din, A., Sabbar, Y. and Wu, P. A novel stochastic Hepatitis B virus epidemic model with second-order multiplicative α-stable noise and real data. Acta Mathematica Scientia, 44, 752-788, (2024).
  • [2] Zhang, G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175, (2003).
  • [3] Nisar, K.S. and Sabbar, Y. Long-run analysis of a perturbed HIV/AIDS model with antiretroviral therapy and heavy-tailed increments performed by tempered stable Lévy jumps. Alexandria Engineering Journal, 78, 498-516, (2023).
  • [4] Sabbar, Y., Khan, A., Din, A. and Tilioua, M. New method to investigate the impact of independent quadratic α-stable Poisson jumps on the dynamics of a disease under vaccination strategy. Fractal and Fractional, 7(3), 226, (2023).
  • [5] Sabbar, Y. Asymptotic extinction and persistence of a perturbed epidemic model with different intervention measures and standard Lévy jumps. Bulletin of Biomathematics, 1(1), 58-77, (2023).
  • [6] Ru, Y. and Ren, H.J. Application of ARMA model in forecasting aluminum price. in: Applied Mechanics and Materials, Vol. 155, Trans Tech Publ, pp. 66-71, (2012).
  • [7] Rossen, A. What are metal prices like? Co-movement, price cycles and long-run trends. Resources Policy, 45, 255–276, (2015).
  • [8] Haque, M.A., Topal, E. and Lilford, E. Iron ore prices and the value of the Australian dollar. Mining Technology, 124(2), 107-120, (2015).
  • [9] Cortez, C.T., Saydam, S., Coulton, J. and Sammut, C. Alternative techniques for forecasting mineral commodity prices. International Journal of Mining Science and Technology, 28(2), 309-322, (2018).
  • [10] Lee, J., List, J.A. and Strazicich, M.C. Non-renewable resource prices: Deterministic or stochastic trends?. Journal of Environmental Economics and Management, 51(3), 354–370, (2006).
  • [11] Uhlenbeck, G.E. and Ornstein, L.S. On the theory of the Brownian motion. Physical Review, 36(5), 823, (1930).
  • [12] Vasicek, O. An equilibrium characterization of the term structure. Journal of Financial Economics, 5(2), 177-188, (1977).
  • [13] Wylomanska, A. The dependence structure for symmetric α-stable CARMA (p, q) processes. In Proceedings, Workshop on Cyclostationary Systems and Their Applications (CSTA), pp. 189-206, Springer, (2014, February).
  • [14] Brockwell, P.J. Continuous-time ARMA processes. Handbook of Statistics, 19, 249-276, (2001).
  • [15] Tully, E. and Lucey, B.M. A power GARCH examination of the gold market. Research in International Business and Finance, 21(2), 316-325, (2007).
  • [16] Wyłoma´nska, A. Measures of dependence for Ornstein–Uhlenbeck process with tempered stable distribution. Acta Physica Polonica B, 42(10), 2049-2062, (2011).
  • [17] Obuchowski, J. and WYŁOMANSKA, A. The Ornstein–Uhlenbeck process with non-Gaussian structure. Acta Physica Polonica B, 44(5), 1123-1136, (2013).
  • [18] Nolan, J.P. Computational aspects of stable distributions. Wiley Interdisciplinary Reviews: Computational Statistics, 14(4), e1569, (2021).
  • [19] Zolotarev V.M., On representation of stable laws by integrals. Selected Translation in Mathematical Statistics and Probability, 6, 84-88, (1966).
  • [20] McCulloch, J.H. Simple consistent estimators of stable distribution parameters. Communications in Statistics-Simulation and Computation, 15(4), 1109–1136, (1986).
  • [21] Ho, T.S.Y., Lee, S.B. Term structure movements and pricing interest rate contingent claims. The Journal of Finance, 41(5), 1011-1029, (1986).
  • [22] Hull, J. and White, A. Pricing interest-rate-derivative securities. The Review of Financial Studies, 3(4), 573-592, (1990).
  • [23] Nolan, J.P. Univariate Stable Distributions: Models for Heavy Tailed Data. Springer: Switzerland, (2020).
  • [24] Zhang, S. and Zhang, X. A least squares estimator for discretely observed Ornstein-Uhlenbeck processes driven by symmetric α-stable motions. Annals of the Institute of Statistical Mathematics, 65, 89-103, (2013).
  • [25] Hu, Y. and Long, H. Parameter estimation for Ornstein–Uhlenbeck processes driven by α-stable Lévy motions. Communications on Stochastic Analysis, 1(2), 175-192, (2007).
  • [26] Cui, H. Estimation in partial linear EV models with replicated observations. Science in China Series A: Mathematics, 47, 144, (2004).
  • [27] Fan, J., Jiang, J., Zhang, C. and Zhou, Z. Time-dependent diffusion models for term structure dynamics. Statistica Sinica, 13(4), 965-992, (2003).
  • [28] Lévy P. Théorie des erreurs. La loi de Gauss et les lois exceptionnelles. Bulletin de la Société Mathématique de France, 52, 49-85, (1924).
  • [29] McCulloch, J.H. 13 Financial applications of stable distributions. Handbook of Statistics, 14, 393-425, (1996).
  • [30] Mittnik, S., Rachev, S.T., Doganoglu, T. and Chenyao, D. Maximum likelihood estimation of stable Paretian models. Mathematical and Computer Modelling, 29(10-12), 275–293, (1999).
  • [31] Nolan, J.P. Modeling financial data with stable distributions. In Handbook of Heavy Tailed Distributions in Finance (pp. 105–130). North-Holland, Holland: Elsevier, (2003).
  • [32] Rachev, S.T. and Mittnik, S. Stable Paretian models in finance. New York: Wiley: (2000).
  • [33] Samorodnitsky, G., Taqqu, M.S. and Linde, R.W. Stable non-gaussian random processes: stochastic models with infinite variance. New York; London: Chapman & Hall, (1994).
  • [34] Zolotarev, A. One-Dimensional Stable Distributions. USA: American Mathematical Society, Providence, (1986).
  • [35] Fama, E.F. and Roll, R. Parameter estimates for symmetric stable distributions. Journal of the American Statistical Association, 66, 331–338, (1971).
  • [36] Mandelbrot, B.B. The variation of certain speculative prices. In: Fractals and Scaling in Finance. New York: Springer, (1997).
  • [37] Bachelier, L. Théorie de la spéculation. Annales scientifiques de l’École Normale Supérieure, Serie 3, Vol 17, 21-86, (1900).
  • [38] Chambers, J.M., Mallows, C.L. and Stuck, B.W. A method for simulating stable random variables. Journal of the American Statistical Association, 71, 340–344, (1976).
  • [39] Koutrouvelis, I.A. An iterative procedure for the estimation of the parameters of stable laws. Communications in Statistics-Simulation and Computation, 10, 17-28, (1981).
  • [40] Koutrouvelis, I.A. Regression-type estimation of the parameters of stable laws. Journal of the American Statistical Association, 75, 918–928, (1980).
  • [41] Press, S.J. Estimation in univariate and multivariate stable distributions. Journal of the American Statistical Association, 67, 842–846, (1972).
  • [42] Weron, R. Performance of the estimators of stable law parameters. Hugo Steinhaus Center, Wroclaw University of Technology, HSC Research Reports, HSC/95/01, (1995).
  • [43] Nolan, J.P. Numerical calculation of stable densities and distribution functions. Communications in Statistics. Stochastic Models, 13(4), 759–774, (1997).
  • [44] Weron, A. and Weron, R. Inzynieria finansowa. HSC Books: Wydawnictwo NaukowoTechniczne, Warszawa, (1998).
  • [45] Revuz, D. and Yor, M. Continuous Martingales and Brownian Motion (Vol. 293). Springer: Berlin, pp. 14-39, (1991).
  • [46] Merton, R.C. Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141-183, (1973).
  • [47] Brennan, M.J. and Schwartz, E.S. An equilibrium model of bond pricing and a test of market efficiency. Journal of Financial and Quantitative Analysis, 17(3), 301-329, (1982).
  • [48] Dothan, L.U. On the term structure of interest rates. Journal of Financial Economics, 6(1), 59-69, (1978).
  • [49] Cox, J.C., Ingersoll Jr., J.E. and Ross, S.A. A theory of the term structure of interest rates. Econometrica, 53(2), 385-407, (1985).
  • [50] Black, F. and Karasinski, P. Bond and option pricing when short rates are lognormal. Financial Analysts Journal, 47(4), 52-59, (1991).
  • [51] Hastie, T., Friedman, J. and Tibshirani, R. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, (2009).
  • [52] Marsden, J. and Weinstein, A. Calculus II, Springer: New York, (1985).
  • [53] Saleh, A.M.E., Arashi, M. and Tabatabaey, S.M.M. Statistical Inference for Models with Multivariate T-Distributed Errors. John Wiley & Sons: USA, (2014).
  • [54] Cont, R. Encyclopedia of Quantitative Finance. (Vol. 4). John Wiley & Sons: USA, (2010).
  • [55] W Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829-836, (1979).
  • [56] Jaditz, T. and Riddick, L.A. Time-series near-neighbor regression. Studies in Nonlinear Dynamics & Econometrics, 4(1), 35-44, (2000).
  • [57] Elliott, G., Rothenberg, T.J. and Stock, J.H. Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813-836, (1996).
  • [58] Pozzi, F., Di Matteo, T. and Aste, T. Exponential smoothing weighted correlations. The European Physical Journal B, 85(175), 1-21, (2012).
  • [59] Breusch, T.S. and Pagan, A.R. The Lagrange multiplier test and its applications to model specifications in econometrics. The Review of Economic Studies, 47(1), 239-253, (1980).
  • [60] Nolan, J.P. Maximum likelihood estimation of stable parameters. In Levy processes: Theory and applications. (pp. 379–400). Boston: Birkhauser, (2001).
  • [61] Tan, P. and Drossos, C. Invariance properties of maximum likelihood estimators. Mathematics Magazine, 48, 37-41, (1975).
  • [62] Shanno, D.F. Conditioning of quasi-Newton methods for function minimization. Mathematics of Computation, 24(111), 647-656, (1970).
  • [63] Fox, L. and Mayers, D.F. Numerical Solution of Ordinary Differential Equations. Chapman and Hall: London, (1987).
  • [64] Stephens, M.A. EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69, 730-737, (1974).
  • [65] Brandimarte, P. Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics. John Wiley & Sons: USA, (2014).
  • [66] Sikora, G., Michalak, A., Bielak, Ł., Mi´sta, P. and Wyłoma´nska, A. Stochastic modeling of currency exchange rates with novel validation techniques. Physica A: Statistical Mechanics and its Applications, 523, 1202-1215, (2019).
There are 66 citations in total.

Details

Primary Language English
Subjects Numerical Solution of Differential and Integral Equations, Financial Mathematics
Journal Section Research Articles
Authors

Bakary D. Coulibaly 0000-0002-7711-2970

Chaibi Ghizlane 0000-0001-8393-1860

Mohammed El Khomssi 0009-0007-7250-811X

Publication Date June 30, 2024
Submission Date January 7, 2024
Acceptance Date May 27, 2024
Published in Issue Year 2024 Volume: 4 Issue: 2

Cite

APA Coulibaly, B. D., Ghizlane, C., & Khomssi, M. E. (2024). An approach to stochastic differential equations for long-term forecasting in the presence of $\alpha$-stable noise: an application to gold prices. Mathematical Modelling and Numerical Simulation With Applications, 4(2), 165-192. https://doi.org/10.53391/mmnsa.1416148


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