美联储-期权定价的局部估计：利用市场状态信息改进预测（英）

Finance and Economics Discussion SeriesFederal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online)Local Estimation for Option Pricing: Improving Forecasts withMarket State InformationHyung Joo Kim, Dong Hwan Oh2025-076Please cite this paper as:Kim, Hyung Joo, and Dong Hwan Oh (2025).“Local Estimation for Option Pricing:Improving Forecasts with Market State Information,” Finance and Economics Discus-sion Series 2025-076.Washington: Board of Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2025.076.NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment. The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.Local Estimation for Option Pricing: Improving Forecasts withMarket State Information*Hyung Joo Kim„Dong Hwan Oh…July 30, 2025AbstractWe propose a novel estimation framework for option pricing models that incorporates local,state-dependent information to improve out-of-sample forecasting performance. Rather thanmodifying the underlying option pricing model, such as the Heston-Nandi GARCH or the Hestonstochastic volatility framework, we introduce a local M-estimation approach that conditions onkey state variables including VIX, realized volatility, and time. Our method reweights historicalobservations based on their relevance to current market conditions, using kernel functions withbandwidths selected via a validation procedure. This adaptive estimation improves the model’sresponsiveness to evolving dynamics while maintaining tractability. Empirically, we show thatlocal estimators substantially outperform traditional non-local approaches in forecasting near-term option implied volatilities. The improvements are particularly pronounced in low-volatilityenvironments and across the cross-section of options.The local estimators also outperformthe non-local estimators in explaining future option returns. Our findings suggest that localinformation, when properly incorporated into the estimation process, can enhance the accuracyand robustness of option pricing models.*The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other membersof the research staff or the Board of Governors.„Federal Reserve Board; hyungjoo.kim@frb.gov…Federal Reserve Board; donghwan.oh@frb.gov1IntroductionAccurately pricing options is essential for trading, hedging, risk management, and regulatory pur-poses. While the ability to forecast future option prices or their distributions is often more relevantfor these applications, much of the literature on option valuation has focused on fitting histori