Pre-prints available - Neural Networks in the Capital Markets

[Neil Burgess]

Neural Networks in the Capital Markets:
The following NNCM-96 pre-prints are now available on request.

Please send your postal address to: boguntula at lbs.ac.uk

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ASSET ALLOCATION ACROSS EUROPEAN EQUITY INDICES USING
    A PORTFOLIO OF DYNAMIC COINTEGRATION MODELS

                  A. N. BURGESS
           Department of Decision Science
                London Business School
         Regents Park, London, NW1 4SA, UK

In modelling financial time-series, the model selection process is
complicated by the presence of noise and possible structural
non-stationarity. Additionally the near-efficiency of financial
markets combined with the flexibility of advanced modelling techniques
creates a significant risk of "data-snooping". These factors combine
to make trading a single model a very risky proposition, particularly
in a situation which allows for high leverage, such as futures
trading. We believe that the risks inherent in relying on a given
model can be reduced by combining a whole set of models and, to this
end, describe a population-based methodology which involves building a
portfolio of complementary models. We describe an application of the
technique to the problem of modelling a set of European equity indices
using a portfolio of cointegration-based models.

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   FORECASTING VOLATILITY MISPRICING

        P. J. BOLLAND & A. N. BURGESS
           Department of Decision Science
                London Business School
         Regents Park, London, NW1 4SA, UK

A simple strategy is employed to exploit volatility mispricing based
on discrepancies between implied and actual market volatility.  The
strategy uses forward and Log contracts to either buy or sell
volatility depending on whether volatility is over or under priced. 
As expected, buying volatility gives small profits on average but with
occasional large losses in adverse market conditions.  In this paper
multivariate non-linear methods are used to forecast the returns of a
Log contract portfolio.  The explanatory power of implied volatility
and the volatility term structure from several indices (FTSE, CAC,
DAX) are investigated.  Neural network methodologies are benchmarked
against linear regression.  The use of both multivariate data and
non-linear techniques are shown to significantly improve the accuracy
of predictions.

Keywords: Options, Volatility Mispricing, Log contract, Volatility
Term Structure,
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