The objectives of this chapter are to switching models. In this chapter, you will learn how to: Use intercept and slope dummy variables to allow for seasonal behaviour in time series, motivate the use of regime switching models in financial econometrics, specify and explain the logic behind Markov switching models,. | ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Chapter 10 Switching models Introductory Econometrics for Finance Copyright 2013, Chris Brooks ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Switching Models Motivation: Episodic nature of economic and financial variables. What might cause these fundamental changes in behaviour? - Wars - Financial panics - Significant changes in government policy - Changes in market microstructure - . big bang - Changes in market sentiment - Market rigidities Switches can be one-off single changes or occur frequently back and forth. Introductory Econometrics for Finance Copyright 2002, Chris Brooks ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Switching Behaviour: A Simple Example for One-off Changes Dealing with switching variables We could generalise ARMA models (again) to allow the series, yt to be drawn from two or more different generating processes at different times. . yt = 1 + | ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Chapter 10 Switching models Introductory Econometrics for Finance Copyright 2013, Chris Brooks ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Switching Models Motivation: Episodic nature of economic and financial variables. What might cause these fundamental changes in behaviour? - Wars - Financial panics - Significant changes in government policy - Changes in market microstructure - . big bang - Changes in market sentiment - Market rigidities Switches can be one-off single changes or occur frequently back and forth. Introductory Econometrics for Finance Copyright 2002, Chris Brooks ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Switching Behaviour: A Simple Example for One-off Changes Dealing with switching variables We could generalise ARMA models (again) to allow the series, yt to be drawn from two or more different generating processes at different times. . yt = 1 + 1 yt-1 + u1t before observation 500 and yt = 2 + 2 yt-1 + u2t after observation 500 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 How do we Decide where the Switch or Switches take Place? It may be obvious from a plot or from knowledge of the history of the series. It can be determined using a model. It may occur at fixed intervals as a result of seasonalities. A number of different approaches are available, and are described below. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 Seasonality in Financial Markets If we have quarterly or monthly or even daily data, these may have patterns in. Seasonal effects in financial markets have been widely observed and are often termed “calendar anomalies”. Examples include day-of-the-week effects, open- or close-of-market effect, January effects, or bank holiday effects. These result in statistically significantly different behaviour during some seasons compared with others. Their existence is not .
