A non-linear filtering approach to stochastic volatility models with an application to daily stock returns

1999 ◽  
Vol 14 (2) ◽  
pp. 101-121 ◽  
Author(s):  
Toshiaki Watanabe
Keyword(s):  
Stock Returns ◽  
Stochastic Volatility ◽  
Linear Filtering ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Non Linear ◽  
Daily Stock Returns ◽  
Filtering Approach

scholarly journals VOLATILITY INFERENCE AND RETURN DEPENDENCIES IN STOCHASTIC VOLATILITY MODELS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950013
Author(s):  
OLIVER PFANTE ◽  
NILS BERTSCHINGER
Keyword(s):  
Mutual Information ◽  
Stock Returns ◽  
Stochastic Volatility ◽  
Wide Class ◽  
Factor Model ◽  
Stochastic Volatility Models ◽  
Information Theoretic ◽  
Random Dynamics ◽  
Fundamental Information ◽  
Volatility Models

Stochastic volatility models describe stock returns [Formula: see text] as driven by an unobserved process capturing the random dynamics of volatility [Formula: see text]. The present paper quantifies how much information about volatility [Formula: see text] and future stock returns can be inferred from past returns in stochastic volatility models in terms of Shannon’s mutual information. In particular, we show that across a wide class of stochastic volatility models, including a two-factor model, returns observed on the scale of seconds would be needed to obtain reliable volatility estimates. In addition, we prove that volatility forecasts beyond several weeks are essentially impossible for fundamental information theoretic reasons.

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