Estimating "Realized" Skewness using Convolutional Neural Network

Haozhe Jiang, Ostap Okhrin, Michael Rockinger

Abstract

We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks followed by Multi Layer Perceptrons. Monte Carlo experiments involving stochastic volatility models, with and without jumps, show that the estimator reduces finite-sample bias relative to existing realized-skewness estimators and remains accurate under model misspecification. Empirically, our estimator exhibits temporal stability and delivers cross-sectional performance in actual skewness-sorted portfolios. We characterize the finite-sample bias of conventional plug-in skewness estimators and establish risk consistency properties of the proposed NN estimator under standard regularity conditions.

Comment: This paper first proves that bias exist given a fixed-horizon of high-frequency data to estimate the low-frequency skewness by using the plug-in method. Then it provides an innovation machine learning method to approximate the optimal estimator by fixing a temporal aggregation architecture for neural network before learning

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