Building Econometric Models


Neural Networks – Some Disadvantages



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Ch9 slides

Neural Networks – Some Disadvantages

  • ‘Introductory Econometrics for Finance’ © Chris Brooks 2013
  • Neural networks are not very popular in finance and suffer from several problems:
    • The coefficient estimates from neural networks do not have any real theoretical interpretation
    • Virtually no diagnostic or specification tests are available for estimated models
    • They can provide excellent fits in-sample to a given set of ‘training’ data, but typically provide poor out-of-sample forecast accuracy
    • This usually arises from the tendency of neural networks to fit closely to sample-specific data features and ‘noise’, and so they cannot ‘generalise’
    • The non-linear estimation of neural network models can be cumbersome and computationally time-intensive, particularly, for example, if the model must be estimated repeatedly when rolling through a sample.

Models for Volatility

  • ‘Introductory Econometrics for Finance’ © Chris Brooks 2013
  • Modelling and forecasting stock market volatility has been the subject of vast empirical and theoretical investigation
  • There are a number of motivations for this line of inquiry:
    • Volatility is one of the most important concepts in finance
    • Volatility, as measured by the standard deviation or variance of returns, is often used as a crude measure of the total risk of financial assets
    • Many value-at-risk models for measuring market risk require the estimation or forecast of a volatility parameter
    • The volatility of stock market prices also enters directly into the Black–Scholes formula for deriving the prices of traded options
  • We will now examine several volatility models.

Historical Volatility

  • ‘Introductory Econometrics for Finance’ © Chris Brooks 2013
  • The simplest model for volatility is the historical estimate
  • Historical volatility simply involves calculating the variance (or standard deviation) of returns in the usual way over some historical period
  • This then becomes the volatility forecast for all future periods
  • Evidence suggests that the use of volatility predicted from more sophisticated time series models will lead to more accurate forecasts and option valuations
  • Historical volatility is still useful as a benchmark for comparing the forecasting ability of more complex time models

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