Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets
DOI:
https://doi.org/10.62054/ijdm/0103.03Keywords:
Artificial Neural Networks, Financial Markets, Modified Heston Model, Risk Management, Volatility ForecastingAbstract
This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions
References
Ayala, J., Garc´ıa-Torres, M., Jos´e, L., V´azquez, N., G´omezVela, F. and Divina, F. (2021). Technical analysis
strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems,
: 107119.
Beni, L. and Schultz, P. (1990). Pricing warrants an empirical study of the Black-Scholes model and its alternatives.
The Journal of Finance, 45 (4): 1181–1209.
Brandon, H. (2017). Implementing the Heston option pricing model in object-oriented cython.
Chandan, S. (2004). Financial modeling using excel and VBA. John Wiley and Sons.
Elias, M. S. and Jeremy, C. S. (1991). Stock price distributions with stochastic volatility: an analytic approach. The
review of financial studies, 4 (4): 727–752,
I-Ming, J., Jui-Cheng, H., and Chuan-San, W. (2014). Volatility forecasts: Do volatility estimators and evaluation
methods matter? Journal of Futures Markets, 34 (11): 1077– 1094.
Kazuhisa, M. (2004). Introduction to Merton jump diffusion model. Department of Economics. The Graduate Center,
the City University of New York.
Ladde, G., S. and Ling, W. (2009). Development of modified geometric Brownian motion models by using stock
price data and basic statistics. Nonlinear Analysis: Theory, Methods and Applications, 71 (12): 1203–1208.
Looney, C., G. (1997). Pattern recognition using neural networks: theory and algorithms for engineers and scientists:
Oxford University press. New York,
Luca, D. P., and Oleksandr, H. (2016). Artificial neural networks approach to the forecast of stock market price
movements. International Journal of Economics and Management Systems
http://iaras.org/iaras/journals/ijems
Maina, A., Mwalili, S., Malenje, B. (2023). Forecasting Stock Prices Using Heston-Artificial Neural Network Model.
International Journal of Data Science and Analysis. Vol. 9, No. 2, pp. 22-33. doi:
11648/j.ijdsa.20230902.11
Naiga, B. C., Mung’atu, J., Nafiu, L. A., and Adjei, M. (2022): On Modified Heston Model for Forecasting Stock
Market Prices," International Journal of Mathematics Trends and Technology (IJMTT), 68(1), 115-
https://doi.org/10.14445/22315373/IJMTT-V68I1P513
OpenAI. (2024). ChatGPT (Mar 20 version) [Large language model]. https://chat.openai.com/chat
Ormonde, F, T. (2017). On the numerical methods for the Heston model. PhD thesis. All Graduate Plan B and other
Reports, Spring 1920 to Spring 2023. 1146. https://digitalcommons.usu.edu/gradreports/1146
Pierre, G. and Possama, D. (2010). Efficient Simulation of the Double Heston Model (January 11, 2010). Available
at SSRN: https://ssrn.com/abstract=1434853 or http://dx.doi.org/10.2139/ssrn.1434853.
Preethi, G. and Santhi, B., (2012). Stock market forecasting techniques: A survey. Journal of Theoretical and
Applied Information Technology, 46 (1).
Reichert, B. and Souza, A. M. (2022.). Can the Heston Model Forecast Energy Generation? A Systematic Literature
Review. International Journal of Energy Economics and Policy, 12(1), 289–295.
https://doi.org/10.32479/ijeep.11975
Roumen, T., Radoslav, Y., Galyac, P., and Georgi, T. (2017). Artificial neural network intelligent method for
prediction. In AIP Conference Proceedings, volume 1872, page 020021. AIP Publishing LLC.
Shuaiqiang, L., Cornelis, W., Oosterlee, S. and Bohte, M. (2018) Pricing options and computing implied volatilities
using neural networks: Applied Mathematics (DIAM), Delft University of Technology, Amsterdam,
Netherlands. 7(1), 16: doi: 10.3390/risks7010016
Shunrong, S., Haomiao, J., and Tongda, Z. (2012). Stock market forecasting using machine learning algorithms.
Department of Electrical Engineering, Stanford University, Stanford, CA, pages 1–5.
Steven L Heston (1993). A closed-form solution for options with stochastic volatility, with applications to bond and
currency options,” review of financial studies.
Teneng, D. (2011). Limitations of the Black-Scholes model. Collection of Papers, 1: 143.
Downloads
Published
Data Availability Statement
We made used of Random data from the net
Issue
Section
License
Copyright (c) 2024 International Journal of Development Mathematics (IJDM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are solely responsible for obtaining permission to reproduce any copyrighted material contained in the manuscript as submitted. Any instance of possible prior publication in any form must be disclosed at the time the manuscript is submitted and a
copy or link to the publication must be provided.
The Journal articles are open access and are distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License, which permits use,
distribution, and reproduction in any medium, provided the original work is properly cited.
No modifications or commercial use of the articles are permitted.
