INFORMATION-THEORETIC ANALYSIS OF STOCHASTIC VOLATILITY MODELS

被引：0
|
作者：
Pfante, Oliver [1 ]
Bertschinger, Nils [1 ]
机构：
[1] Frankfurt Inst Adv Studies, Syst Risk Grp, D-60438 Frankfurt, Hesse, Germany
来源：
ADVANCES IN COMPLEX SYSTEMS | 2019年 / 22卷 / 01期
基金：
欧洲研究理事会;
关键词：
Information theory; stochastic volatility; Bayesian analysis; MAXIMUM-LIKELIHOOD-ESTIMATION; OPTIONS;
D O I：
10.1142/S021952591850025X
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Stochastic volatility models describe asset prices S-t as driven by an unobserved process capturing the random dynamics of volatility sigma(t). We quantify how much information about sigma(t) can be inferred from asset prices S-t in terms of Shannon's mutual information in a twofold way: theoretically, by means of a thorough study of Heston's model; from a machine learning perspective, by means of investigating a family of exponential Ornstein-Uhlenbeck (OU) processes fitted on S&P 500 data.
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页数：21
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