Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data

Jianqing Fan, Donggyu Kim (), Minseok Shin and Yazhen Wang
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Donggyu Kim: Department of Economics, University of California Riverside

No 202415, Working Papers from University of California at Riverside, Department of Economics

Abstract: This paper introduces a novel Ito diffusion process for both factor and idiosyncratic volatilities whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR-Ito (FIVAR-Ito) model. The FIVAR-Ito model considers dynamics of the factor and idiosyncratic volatilities and involve many parameters. In addition, the empirical studies have shown that the financial returns often exhibit heavy tails. To address these two issues simultaneously, we propose a penalized optimization procedure with a truncation scheme for a parameter estimation. We apply the proposed parameter estimation procedure to predicting large volatility matrices and investigate its asymptotic properties. Using high-frequency trading data, the proposed method is applied to large volatility matrix prediction and minimum variance portfolio allocation.

Pages: 58 Pages
Date: 2024-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-rmg
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