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Working Paper
Better the Devil You Know: Improved Forecasts from Imperfect Models
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- speci
ed model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspeci
cation of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we
nd signi
cant fore- cast improvements from ...
Working Paper
Local Estimation for Option Pricing: Improving Forecasts with Market State Information
We propose a novel estimation framework for option pricing models that incorporates local, state-dependent information to improve out-of-sample forecasting performance. Rather than modifying the underlying option pricing model, such as the Heston-Nandi GARCH or the Heston stochastic volatility framework, we introduce a local M-estimation approach that conditions on key state variables including VIX, realized volatility, and time. Our method reweights historical observations based on their relevance to current market conditions, using kernel functions with bandwidths selected via a validation ...