Building Econometric Models

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