Risk Hedging Logic and Theoretical Boundary of Options and Futures Instruments
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Abstract
Against the background of increasing volatility and complex risk factors in global markets, options and futures have become important instruments for risk hedging and uncertainty management. This paper systematically explores the risk hedging logic of options and futures, clarifies their theoretical boundaries and applicable constraints, and discusses feasible paths for stock market risk mitigation. First, based on financial derivatives theory, the internal mechanisms of directional hedging using futures and structured hedging using options are analyzed. Second, the theoretical boundaries of the two instruments are defined from the perspectives of pricing model assumptions, market efficiency requirements, and risk factor coverage. Furthermore, empirical analysis is conducted using data from major global stock indexes and derivatives markets to verify hedging effectiveness under different market environments. Finally, a multidimensional risk mitigation framework integrating tool matching, strategy optimization, and regulatory coordination is constructed. The findings provide theoretical support for quantitative risk management and offer analytical references for uncertainty modeling, dynamic response mechanisms, and information propagation in complex engineering systems.
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References
K. Maria, H. Michael, and W. Alex, “Estimating time-varying risk aversion from option prices and realized returns,” Quantitative Finance, vol. 23, no. 1, pp. 1-17, 2023, doi: 10.1080/14697688.2022.2130086.
M. Shirzadi, M. Rostami, M. Dehghan, and X. Li, “American options pricing under regime-switching jumpdiffusion models with meshfree finite point method,” Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena; 2023. 166 p, doi: 10.1016/J.CHAOS.2022.112919.
M. Mahsa, M. Farshid, and A. N. Reza, “Lookback option pricing under the double Heston model using a deep learning algorithm,” Computational and Applied Mathematics, vol. 41, no. 8, pp. 378, 2022, doi: 10.1007/S40314-022-02098-5.
Self-Regulatory Organizations; NYSE American LLC,“Notice of Withdrawal of a Proposed Rule Change to Amend the NYSE American Options Fee Schedule Concerning the Options Regulatory Fee and F. I. N. D. ” The Federal Register, vol. 87, no. 237, pp. 76097, 2022.
X. Zhang and T. Zhang, “On pricing double-barrier options with Markov regime switching,” Finance Research Letters, pp. 51, 2023, doi: 10.1016/J.FRL.2022.103413.
A. M. Monteiro and A. A. F. Santos, “Parallel computing in finance for estimating risk-neutral densities through option prices,” Journal of Parallel and Distributed Computing, vol. 173, pp. 61-69, 2023, doi: 10.1016/J.JPDC.2022.11.010.
J. H. Venter and P. J. De Jongh, “Trading Binary Options Using Expected Profit and Loss Metrics,” Risks, vol. 10, no. 11, pp. 212-212, 2022, doi: 10.3390/RISKS10110212.
J. E. Hilliard, J. T. D. Ngo, M. Dempster, and J. Gatheral, “Bitcoin: jumps, convenience yields, and option prices,” Quantitative Finance, vol. 22, no. 11, pp. 2079-2091, 2022, doi: 10.1080/14697688.2022.2109989.
A. Sivaporn, S. Panumart, and S. Wannika, “An Analysis of the Fractional-Order Option Pricing Problem for Two Assets by the Generalized Laplace Variational Iteration Approach,” Fractal and Fractiona, vol. 6, no. 11, pp. 667, 2022, doi: 10.3390/FRACTALFRACT6110667.
G. D. Domenico, L. Arturo, and L. Debora, “Co-movements, option pricing and risk management: an application to WTI versus Brent spread options,” Annals of operations research, vol. 336, no. 1, pp. 1039-1061, 2024, doi: 10.1007/S10479-022-05059-7.
L. Deng, H. Tan, F. Wei, and Y. Wang, “Option Pricing for Uncertain Stock Model Based on Optimistic Value,” Jaciii, vol. 26, no. 6, pp. 1031-1039, 2022, doi: 10.20965/JACIII.2022.P1031.
S. Yang, K. Aretz, H. Liu, and Y. Zhang, “Consumption risks in option returns,” Journal of Empirical Finance, vol. 69, pp. 285-302, 2022, doi: 10.1016/J.JEMPFIN.2022.10.001.
D. O. Hwan and P. Yang-Ho, “GARCH option pricing with volatility derivatives,” Journal of Banking and Finance, pp. 146, 2023, doi: 10.1016/J.JBANKFIN.2022.106718.
S. Sergey and S. Correction: Smirnov, “A Guaranteed Deterministic Approach to Superhedging—The Case of Convex Payoff Functions on Options,” Mathematics 2019, 7, 1246. Mathematics, vol. 10, no. 23, pp. 4521, 2022, doi: 10.3390/MATH10234521.
J. Kris and L. Bingxin, “Option Returns, Risk Premiums, and Demand Pressure in Energy Markets,” Journal of Banking and Finance, vol. 146, Art. no. 106687, 2023, doi: 10.1016/J.JBANKFIN.2022.106687.
L. Shmuel and T. Avraham, “The option value of record-based sanctions,” Games and Economic Behavior, vol. 137, pp. 1-22, 2023, doi: 10.1016/J.GEB.2022.10.014.
G. Hannah and G. Christopher, “Dynamics of a binary option market with exogenous information and price sensitivity,” Communications in Nonlinear Science and Numerical Simulation, vol. 118, Art. no. 106994, 2023, doi: 10.1016/J.CNSNS.2022.106994.
P. Sozhaeswari, R. Sowrirajan, K. Loganathan, and S. Gyeltshen, “A Study on the Impact of Nonlinear Source Term in Black-Scholes Option Pricing Model,” Abstract and Applied Analysis, vol. 2022, no. 1, Art. no. 6385401, 2022, doi: 10.1155/2022/6385401.
M. H. Gagnon, G. J. Power, and D. Toupin, “The sum of all fears: Forecasting international returns using optionimplied risk measures,” Journal of Banking and Finance, vol. 146, Art. no. 106701, 2023, doi: 10.1016/J.JBANKFIN.2022.106701.
N. Bashiruddin and S. Wang, “Numerical Techniques for Determining Implied Volatility in Option Pricing,” Journal of Computational and Applied Mathematics, vol. 422, Art. no. 114913, 2023, doi: 10.1016/J.CAM.2022.114913.