The benefit of information reduction for trading strategies

Applied Economics

Volumn 34, Issue 7, 2002, Pages 917-930

  Schittenkopf, Christian ^a   Tiňo, Peter ^a   Dorffner, Georg ^a  

^a AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

CAPITAL MARKET; MODEL; STOCK MARKET; THEORETICAL STUDY;

EID: 0036243130     PISSN: 00036846     EISSN: None     Source Type: Journal    
DOI: 10.1080/00036840110061938     Document Type: Article

References (24)

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2. Predicting equity returns from securities data
3. The pricing of options and corporate liabilities
4. A generalized autoregressive conditional heteroskedasticity
5. A conditionally heteroskedastic time series model for speculative prices and rates of return
6. Extreme events from the return and volume process: A discretization approach for complexity reduction
7. The informational content of implied volatility
8. Volatility forecasts and the enhancement of risk/return profiles through automated trading strategies
9. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation
10. Semiparametric ARCH models
11. Forecasting volatility
12. Noisy time series prediction using a recurrent neutral network and grammatical inference
13. Market volatility prediction and the efficiency of the S and P 100 index option market
14. Multilayer feedforward networks are universal approximators
15. The self-organizing map
16. Forecasting volatility and option prices of the S and P 500 index
17. Alternative models for conditional stock volatility
18. High frequency time series analysis and prediction using Markov models
19. Forecasting time-dependent conditional densities: A semi-nonparametric neural network approach
20. Common stock volatility expectations implied by option prices
21. Delta-neutral volatility trading with intra-day prices: An application to options on the DAX