Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S021952591850025X | ADVANCES IN COMPLEX SYSTEMS |
Keywords | Field | DocType |
Information theory,stochastic volatility,Bayesian analysis | Information theory,Econometrics,Stochastic volatility,Financial economics,Volatility (finance),Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 1 | 0219-5259 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Pfante | 1 | 0 | 0.34 |
| Nils Bertschinger | 2 | 225 | 21.10 |