A Stochastic Volatility Model With Realized Measures for Option Pricing

被引：8

作者：

Bormetti, Giacomo ^{[1
]}

Casarin, Roberto ^{[2
]}

Corsi, Fulvio ^{[3
,4
]}

Livieri, Giulia ^{[5
]}

机构：

[1] Univ Bologna, Dept Math, Piazza Porta S Donato 5, I-40126 Bologna, Italy

[2] Ca Foscari Univ Venice, Dept Econ, Fondamenta S Giobbe 873, Venice, Italy

[3] Univ Pisa, Dept Econ & Management, Via Cosimo Ridolfi 10, I-56124 Pisa, Italy

[4] City Univ London, Dept Econ, Northampton Sq, London EC1V 0HB, England

[5] Scuola Normale Super Pisa, Piazza Cavalieri 7, I-56126 Pisa, Italy

来源：

JOURNAL OF BUSINESS & ECONOMIC STATISTICS | 2020年 / 38卷 / 04期

关键词：

Bayesian inference; High-frequency data; Monte Carlo Markov chain; Option pricing; Realized volatility; STATE-SPACE MODELS; DYNAMIC JUMP INTENSITIES; RISK PREMIUMS EVIDENCE; MICROSTRUCTURE NOISE; RETURNS; TRANSFORM; VALUATION; SELECTION; VARIANCE; LEVERAGE;

D O I：

10.1080/07350015.2019.1604371

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence-the crucial parameter in pricing Standard and Poor's 500 Index options.

引用

页码：856 / 871

页数：16

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