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Portfolio Optimization with Semi-Tsallis Entropy: Managing Flexible Downside Uncertainty in Uncertain Random Environments
(Elsevier, 2026-11) Ahmadzade, Hamed; Li, Qiqi; Gao, Jinwu; Celik, Esref Ugur; Sheng, Yuhong
This paper introduces semi-Tsallis entropy as a novel downside risk measure for uncertain random variables within the framework of chance theory. By integrating the parametric flexibility of Tsallis entropy with the asymmetric focus of semi-entropy, we develop a comprehensive framework that captures only unfavorable uncertainties while preserving the tuning capability of the deformation parameter q. We establish fundamental mathematical properties of semi-Tsallis entropy, including monotonicity and transformation rules, and derive closed-form expressions via inverse uncertainty distributions. A key theoretical contribution is the representation of partial semi-Tsallis entropy as an expectation, which enables efficient Monte Carlo simulation methods for practical implementation in complex optimization scenarios. The proposed measure addresses a critical gap in the literature: existing downside risk measures for uncertain random variables lack the parametric flexibility offered by Tsallis entropy formulations. Our framework fills this gap by providing a mathematically rigorous approach that combines the asymmetric focus of downside risk measurement with the adaptability of Tsallis entropy. The deformation parameter q facilitates custom-tailored risk assessment aligned with specific investor preferences and evolving market conditions, while the semi-entropy structure ensures concentrated measurement of unfavorable uncertainties. We formulate and solve portfolio optimization problems that integrate semi-Tsallis entropy as a downside risk constraint in uncertain random environments. The framework offers portfolio managers a flexible tool for tailoring risk assessment to hybrid markets characterized by both random fluctuations and epistemic uncertainty, where conventional symmetric measures often yield suboptimal risk management outcomes. The methodological contributions presented here establish a foundation for further advances in downside risk management and entropy-based optimization under uncertainty.
European Option Pricing under Markov-Switching Two-Factor Heston Model with Stochastic Interest Rate: Model Calibration Using a Neural Network Optimized by Bat Algorithm
(Elsevier B.V., 2026-11) Celik, Esref Ugur; Mehrdoust, Farshid; Noorani, Maryam
In this paper, we study the pricing of European options under a Markov-switching double Heston model with a stochastic interest rate governed by the CIR process. We assume that the mean-reversion levels of both stochastic volatilities and the interest rate switch between bull and bear market states. The stability of the proposed Markov-switching model is analytically investigated, and a semi-analytical pricing of European options is developed based on its moment-generating function. After obtaining the semi-analytical option pricing formula, we propose a hybrid calibration framework based on a neural network structure. This framework uses as inputs the bull and bear prices, their corresponding returns and volatilities, along with standard option parameters. Then, the option prices generated by the Markov-switching model are incorporated into the neural network via a concatenation layer. To optimize the parameters of this hybrid calibration structure, the bat optimization algorithm is employed. Finally, numerical experiments and empirical applications are conducted to demonstrate the accuracy of the proposed pricing formula and calibration framework, as well as the effectiveness of the bat algorithm in solving the calibration problem. © 2026 Elsevier B.V.
Citation - WoS: 1
Citation - Scopus: 1
Shaken, Stirred and Indebted: Firm-Level Effects of Earthquakes
(Elsevier Science inc, 2024-10) Arin, K. Peren; Arnau, Josep Marti; Boduroglu, Elif; Celik, Esref Ugur; Marti Arnau, Josep
Using firm-level data from Turkiye, we investigate the effects of earthquakes on firms' balance sheets. We find that earthquakes increase firms' liabilities but have a smaller effect on firms' assets, both in magnitude and significance. Using surveys sent to the finance and/or accounting managers of the largest 100 firms in Turkiye we identify common themes in their perceptions. Our findings reveal a consensus among respondents attributing the increased liabilities to exchange rate depreciation and lower business activity following a disaster. Conversely, higher availability of external credit is associated with a decrease in liabilities. Our analysis also indicates that finance managers with higher educational attainment may be underestimating the effects of earthquakes.

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