Forecast Objectives Fin250f: Lecture 8.2 Spring 2010 Reading: Brooks, chapter 5.9-5.11.

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Presentation transcript:

Forecast Objectives Fin250f: Lecture 8.2 Spring 2010 Reading: Brooks, chapter

Outline  Forecasting methodology and dangers  Linear forecasts  Forecast objectives

Forecasting Methodology  Prediction about tomorrow  In sample/Out of sample forecasts  One step ahead/multi-step Direct/iterative  Recursive/rolling windows

AR(1): Zero mean form

Forecasting the AR(1)

Forecasting the AR(1): Multiperiods

Forecasting an MA(1)

Exponential Smoothing  Ad hoc forecasting tool  Primitive (but maybe effective)  Common in business forecasting environments

Exponential Smoothing

Forecast Objectives  Mean Squared Error (MSE)  Mean Absolute Error (MAE)  Theil's U-statistic  Sign predictions  Trading profits

Mean Squared Error

Mean Absolute Error

Theil's U-statistic

Sign Prediction

Reminder on Central Limit Theorem

Binomial Test

Trading Profits

Comparisons  How do you weight outliers? MSE: a lot Sign: Not much  How does this connect to economics MSE: not much Sign: more Trading: a lot  Mistakes connected to real losses  Weights problems with large returns in a sensible way  Actual trading is still more complex (costs, slippage)