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Titolo:
Volatility trading via temporal pattern recognition in quantised financialtime series
Autore:
Tino, P; Schittenkopf, C; Dorffner, G;
Indirizzi:
Aston Univ, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England Aston Univ Birmingham W Midlands England B4 7ET 7ET, W Midlands, England Austrian Res Inst Artificial Intelligence, Vienna, Austria Austrian Res Inst Artificial Intelligence Vienna Austria ienna, Austria
Titolo Testata:
PATTERN ANALYSIS AND APPLICATIONS
fascicolo: 4, volume: 4, anno: 2001,
pagine: 283 - 299
SICI:
1433-7541(2001)4:4<283:VTVTPR>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
SYMBOLIC SEQUENCES; LENGTH; SYSTEM;
Keywords:
fractal geometry; Markov models; options; prediction suffix trees; straddle; volatility;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
41
Recensione:
Indirizzi per estratti:
Indirizzo: Tino, P Aston Univ, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England Aston Univ Birmingham W Midlands England B4 7ET Midlands, England
Citazione:
P. Tino et al., "Volatility trading via temporal pattern recognition in quantised financialtime series", PATTERN A A, 4(4), 2001, pp. 283-299

Abstract

We investigate the potential of the analysis of noisy non-stationary time series by quantising it into streams of discrete symbols and applying finite-memory symbolic predictors. Careful quantisation can reduce the noise in the time series to make model estimation more amenable, We apply the quantisation strategy in a realistic setting involving financial forecasting and trading, In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 on a daily basis, based on predictions of the daily volatility differences in the underlying indexes. We propose a parametric, data-driven quantisation scheme which transforms temporal patterns in the series of daily volatility changes into grammatical and statistical patterns in the corresponding symbolic streams. As symbolic predictors operating on the quantised streams, we use the classical fixed-order Markov models, variable memory length Markov models and a novel variation of fractal-based predictors, introduced in its original form in Tino and Dorffner [1]. The fractal based predictors are designed to efficiently use deep memory. We compare the symbolic models with continuous techniques such as time-delay neural networks with continuous and categorical outputs, and GARCH models. Our experiments strongly suggest that the robust information reduction achieved by quantising the real-valued time series is highly beneficial. To deal with non-stationarity in financial daily time series, we propose two techniques that combine 'sophisticated' models fitted on the training data with a fixed set of simple-minded symbolic predictors notusing older (and potentially misleading) data in the training set. Experimental results show that by quantising the volatility differences and then using symbolic predictive models, market makers can sometimes generate a statistically significant excess profit. We also mention some interesting observations regarding the memory structure in the series of daily volatility differences studied.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 14/07/20 alle ore 13:16:22