Volatility in the stock market: ANN versus parametric models

Forecasting and adequately measuring equity returns volatility is crucial for portfolio selection and trading strategies. Implied volatility is often considered to be informationally superior to the realized volatility. When available, implied volatility is largely used by practitioners and investor...

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Vydáno v:Annals of operations research Ročník 299; číslo 1-2; s. 1101 - 1127
Hlavní autoři: D’Ecclesia, Rita Laura, Clementi, Daniele
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2021
Springer
Springer Nature B.V
Témata:
Autoregressive models
Business and Management
Combinatorics
Comparative analysis
Economic forecasting
Equity
Forecasts and trends
Neural networks
Operations research
Operations Research/Decision Theory
Parametric equations
Parametric statistics
Pricing
S.I.: Recent Developments in Financial Modeling and Risk Management
Securities markets
Stock markets
Technology application
Theory of Computation
Volatility
Volatility (Finance)
Italy
GARCH models
ANN
C22
Heston model
Conditional volatility
G14
Implied volatility
ISSN:0254-5330, 1572-9338
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Popis
Shrnutí:Forecasting and adequately measuring equity returns volatility is crucial for portfolio selection and trading strategies. Implied volatility is often considered to be informationally superior to the realized volatility. When available, implied volatility is largely used by practitioners and investors to forecast future volatility. To this extent we want to identify the best approach to track equity returns implied volatility using parametric and ANN approaches. Using daily equity prices and stock market indices traded on major international Exchanges we estimate time varying volatility using the E-GARCH approach, the Heston model and a novel ANN framework to replicate the corresponding implied volatility. Overall the ANN approach results the most accurate to track the equity returns implied volatility.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03374-0