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Optimization of a time continuous portfolio of assets and derivatives | ||
Journal of Mahani Mathematical Research | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 17 مرداد 1404 اصل مقاله (766.69 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22103/jmmr.2025.24238.1711 | ||
نویسنده | ||
Omid Rabieimotlagh* | ||
Department of Mathematics, University of Birjand, Birjand, Iran | ||
چکیده | ||
We consider an incomplete market and suggest a self-financing time continuous investment strategy consisting of a risk-free asset (bond), a risky asset, and a financial derivative whose value moves inversely to that of the risky asset. We optimize the wealth process by introducing a parametric convex utility function that simultaneously maximizes wealth and minimizes the mean square of it. Using the HJB equation, we compute precisely the optimal portfolio process, where notably, a range of processes can optimize the problem. This advantage enables investors to gain fringe benefits while maintaining their overall investment strategy by adjusting their portfolios accordingly. As an application of the results, we optimize a portfolio process with a European put as the derivative and compute the corresponding optimal wealth numerically. Additionally, we will outline a method to calculate the market return rate and the martingale parameter, which are necessary for optimization. | ||
کلیدواژهها | ||
Self-finance portfolio؛ Dynamic programming؛ Wealth optimization؛ HJB equation | ||
مراجع | ||
[1] Aase, K.K., (1984). Optimum portfolio diversi cation in a general continuoustime model. Stochastic Processes and their Applications, 18(1), 81{98. https://doi.org/10.1016/0304-4149(84)90163-7.
[2] Callegaro, G., Gagi, M., Scotti, S., & et al. (2017). Optimal investment in markets with over and under-reaction to information. Mathematical Finance Economy, 11, 299{322. https://doi.org/10.1007/s11579-016-0182-8.
[3] Chiu, H., & Cont, R., (2023). A model-free approach to continuous-time nance. Mathematical Finance, 33(2), 257{273. https://doi.org/10.1111/ma .12370.
[4] Czichowsky, C., (2013). Time-consistent mean-variance portfolio selection in discrete and continuous time. Finance Stochastic, (17), 227{271. https://doi.org/10.1007/s00780-012-0189-9.
[5] Enkhsaikhan, B., & Jo, O., (2024). Risk-averse Reinforcement Learning for Portfolio Optimization. ICT Express, 10(4), 857-862. https://doi.org/10.1016/j.icte.2024.04.010.
[6] Escobar-Anel, M., Spies, B., & Zagst, R., (2024). Do jumps matter in discrete-time portfolio optimization? Operations Research Perspectives, 13, 100312. https://doi.org/10.1016/j.orp.2024.100312.
[7] Gourieroux, C., Laurent, G.P., & Pham, H., (2002). Mean-variance hedging and numeraire. Mathematical Finance, 8(3), 179{200. https://doi.org/10.1111/1467-9965.00052.
[8] He, H., & Pearson, N.D., (1991). Consumption and portfolio policies with incomplete markets and short-sale constraints: The in nite dimensional case. Journal of Economic Theory, 54, 259{304. https://doi.org/10.1016/0022-0531(91)90123-L.
[9] Jin, H., & Zhou, X.Y., (2008). Behavioral portfolio selection in continuous time. Mathematical Finance, (3), 385{426. https://doi.org/10.1111/j.1467-9965.2008.00339.x.
[10] Jin H., & Zhou, X.Y., (2010). Erratum to \behavioral portfolio selection in continuous time". Mathematical Finance, 20(3), 521{525. https://doi.org/10.1111/j.1467-9965.2010.00409.x.
[11] Kang, M., Templeton, G., F., Kwak, D., & Um, S., (2024). Development of an AI framework using neural process continuous reinforcement learning to optimize highly volatile nancial portfolios. Knowledge-Based Systems, 300, 112017. https://doi.org/10.1016/j.knosys.2024.112017. [12] Korn, R., & MULLER, L., (2022). Optimal portfolio choice with crash risk and model ambiguity. International Journal of Theoretical and Applied Finance, 25(01), 22-50. https://doi.org/10.1142/S0219024922500029.
[13] Markowitz, H., (1952). Portfolio selection. The journal of nance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.
[14] Merton, R., C., (1971). Optimum consumption and portfolio rules in a continuous-time model. Journal of Economic Theory, 3(4), 373-413. https://doi.org/10.1016/0022-0531(71)90038-X.
[15] Pham, H., (2009). Continuous-Time Stochastic Control and Optimization with Financial Applications. Springer, New York. https://dx.doi.org/10.1007/978-3-540-89500-8.
[16] Shreve, S., E., (2004). Stochastic Calculus for Finance II, Continuous-Time Models. Springer, New York. https://link.springer.com/book/9780387401010.
[17] Uratani, T., (2014). A Portfolio Model for the Risk Management in Public Pension. Springer International Publishing, Cham, 183-186. http://dx.doi.org/10.1007/978-3-319-05014-0 41.
[18] Wu, B., & Li, L., (2023). Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market. Journal of Economic Dynamics and Control, 158, 104787. https://dx.doi.org/10.2139/ssrn.4415531.
19] Xia, J., (2011). Risk aversion and portfolio selection in a continuoustime model. SIAM Journal on Control and Optimization, 49(5), 1916{1937. https://doi.org/10.1137/10080871X.
[20] Xie, S., Li, Z., & Wang, Z., (2008). Continuous-time portfolio selection with liability: Mean{variance model and stochastic lq approach. Insurance: Mathematics and Economics, (3), 943{953. http://dx.doi.org/10.1016/j.insmatheco.2007.10.014.
[21] Zhang, C., Zhibin, L., & Kam, C.,Y., (2021). Optimal portfolio and consumption for a Markovian regime-switching jump-di usion process. The ANZIAM Journal, 63, 1{25. https://doi.org/10.21914/anziamj.v63.14546. | ||
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