Optimal risk management considering environmental and climatic changes
Ramzi Benkraiem
Audencia Business School Nantes, Nantes, France
Search for more papers by this authorYoussef El-Khatib
Department of Mathematical Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE
Search for more papers by this authorCorresponding Author
Stéphane Goutte
Université Paris-Saclay, UMI SOURCE, IRD, UVSQ, Guyancourt, France
Paris School of Business, Paris, France
Correspondence
Stéphane Goutte, Université Paris-Saclay, UMI SOURCE, IRD, UVSQ, 78280 Guyancourt, France.
Email: stephane.goutte@uvsq.fr
Search for more papers by this authorTony Klein
Faculty of Business and Economics, Technische Universität Chemnitz, Chemnitz, Germany
Search for more papers by this authorRamzi Benkraiem
Audencia Business School Nantes, Nantes, France
Search for more papers by this authorYoussef El-Khatib
Department of Mathematical Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE
Search for more papers by this authorCorresponding Author
Stéphane Goutte
Université Paris-Saclay, UMI SOURCE, IRD, UVSQ, Guyancourt, France
Paris School of Business, Paris, France
Correspondence
Stéphane Goutte, Université Paris-Saclay, UMI SOURCE, IRD, UVSQ, 78280 Guyancourt, France.
Email: stephane.goutte@uvsq.fr
Search for more papers by this authorTony Klein
Faculty of Business and Economics, Technische Universität Chemnitz, Chemnitz, Germany
Search for more papers by this authorAbstract
Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equations are used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model's superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climate policy.
REFERENCES
- Akhavizadegan, F., Ansarifar, J., Wang, L., & Archontoulis, S. V. (2022). Risk-averse stochastic optimization for farm management practices and cultivar selection under uncertainty. arXiv. https://doi.org/10.48550/arXiv.2208.04840
10.48550/arXiv.2208.04840 Google Scholar
- Chronopoulos, M., & Lumbreras, S. (2016). Optimal regime switching under risk aversion and uncertainty. European Journal of Operational Research, 256(2), 543–555.
- Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223.
- Cristelli, M. (2013). Complexity in financial markets: Modeling psychological behavior in agent-based models and order book models. Springer Science & Business Media.
- El Karoui, N., Peng, S., & Quenez, M. C. (1997). Backward stochastic differential equations in finance. Mathematical Finance, 7(1), 1–71.
- Elliott, R. J., Kuen Siu, T., & Chan, L. (2007). Pricing volatility swaps under Heston's stochastic volatility model with regime switching. Applied Mathematical Finance, 14(1), 41–62.
10.1080/13504860600659222 Google Scholar
- Elliott, R. J., Siu, T. K., & Chan, L. (2006). Option pricing for Garch models with Markov switching. International Journal of Theoretical and Applied Finance, 9(6), 825–841.
10.1142/S0219024906003846 Google Scholar
- Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. Springer New York.
- Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384.
- Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
10.2307/j.ctv14jx6sm Google Scholar
- Hamilton, J. D., & Gang, L. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics, 11(5), 573–593.
- Hamilton, J. D., & Owyang, M. T. (2012). The propagation of regional recessions. Review of Economics and Statistics, 94(4), 935–947.
- Hamilton, J. D., & Raj, B. (2002). Advances in Markov-switching models. Empirical Economics, 27(2), 149–162.
- Hu, Y., Imkeller, P., & Müller, M. (2005). Utility maximization in incomplete markets. The Annals of Applied Probability, 15(3), 1691–1712.
- Jin, Z., Liu, G., & Yang, H. (2020). Optimal consumption and investment strategies with liquidity risk and lifetime uncertainty for Markov regime-switching jump diffusion models. European Journal of Operational Research, 280(3), 1130–1143.
- Khaliq, A. Q. M., & Liu, R. H. (2009). New numerical scheme for pricing American option with regime-switching. International Journal of Theoretical and Applied Finance, 12(3), 319–340.
10.1142/S0219024909005245 Google Scholar
- Kim, C. J., & Nelson, C. R. (2017). State-space models with regime switching: Classical and Gibbs-sampling approaches with applications. MIT Press.
10.7551/mitpress/6444.001.0001 Google Scholar
- Kim, S. W., & Lee, B. S. (2008). Stock returns, asymmetric volatility, risk aversion, and business cycle: Some new evidence. Economic Inquiry, 46(2), 131–148.
- Krolzig, H. M. (1997). Markov switching vector autoregression, modelling, statistical inference and application to business cycle analysis. Springer.
10.1007/978-3-642-51684-9 Google Scholar
- Landen, C. (2000). Bond pricing in a hidden Markov model of the short rate. Finance and Stochastics, 4(4), 371–389.
10.1007/PL00013526 Google Scholar
- Leiva-Leon, D. (2017). Measuring business cycles intra-synchronization in US: A regime-switching interdependence framework. Oxford Bulletin of Economics and Statistics, 79, 513–545.
- Liu, R. H. (2012). A new tree method for pricing financial derivatives in a regime-switching mean-reverting model. Nonlinear Analysis: Real World Applications, 13(6), 2609–2621.
- Liu, Z., Waggoner, D. F., & Zha, T. (2011). Sources of macroeconomic fluctuations: A regime-switching DSGE approach. Quantitative Economics, 2(2).
- Lontzek, T. S., & Narita, D. (2011). Risk-averse mitigation decisions in an unpredictable climate system. The Scandinavian Journal of Economics, 113(4), 937–958.
- Owyang, M. T., Piger, J., & Wall, H. J. (2005). Business cycle phases in U.S. states. Review of Economics and Statistics, 87, 604–616.
- Romano, M., & Touzi, N. (1997). Contingent claims and market completeness in a stochastic volatility model. Mathematical Finance, 7(4), 399–412.
- Savku, E., & Weber, G. (2022). Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market. Annals of Operations Research, 312, 1171–1196.
- Sims, C. A., & Tao, Z. (2006). Were there regime switches in U.S. monetary policy? American Economic Review, 96(1), 54–81.
- Song, Y., & Woźniak, T. (2020). Markov switching. arXiv. https://doi.org/10.48550/arXiv.2002.03598
10.48550/arXiv.2002.03598 Google Scholar
