forecasting economic activity using asset prices

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  • University of Piraeus

    Department of Banking & Financial Management

    MSc in Banking and Financial Management

    Thesis: Forecasting Economic Activity using Asset Prices

    Graduate Student: Kouvelis Panagiotis

    Supervisor: Dr. Christina Christou

    Dr. George Diakogiannis

    Dr. Dimitrios Kyriazis

    Piraeus 2010

  • Forecasting Economic Activity using Asset Prices

    MSc in Banking & Financial Management 2

    To my sister

  • Forecasting Economic Activity using Asset Prices

    MSc in Banking & Financial Management 3

    ABSTRACT

    This dissertation evaluates how well the asset prices and, in particular the term

    spread, the short rate and the real stock returns, forecast the GDP growth and the

    Industrial Production. The study is applied with data of seven countries (Canada,

    France, Germany, Italy, Japan, United Kingdom and United States) and it covers a

    period of time between 1966 until now. The research finds that the asset prices have

    forecasting power for one quarter/month but they lose their power when the

    forecasting horizon increases. Moreover, the paper evaluates that the real stock return

    is the best predictor of the GDP growth and that the short rate has more predictive

    content than the term spread.

    Keywords: Term spread, short rate, stock returns, output growth, forecasting

    horizon, out-of-sample statistics

  • Forecasting Economic Activity using Asset Prices

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    Acknowledgements

    This work would not have been possible without the support and encouragement of

    my professor Dr. Christina Christou, under whose supervision I chose this topic and

    began the thesis. With her inspiration and her great efforts to explain things clearly

    and simply, she helped me to pursue the dissertation. Throughout my thesis-writing

    period, she provided encouragement, sound advice, good teaching and she was always

    willing to answer any of my questions. I am very grateful also to the PhD student

    Christos Bouras who offered me his advice and help. Above all I would like to

    sincerely thank my beloved family; my parents, Evangelos and Konstantina, and my

    sister, Lily, who supported me throughout the whole year.

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    PAGE

    Abstract 3

    Acknowledgements 4

    Table of Contents 5

    CHAPTER 1 INTRODUCTION 6

    CHAPTER 2 LITERATURE REVIEW 8

    2.1 Stock Returns, output growth and/or inflation 8

    2.2 Term Spread, output growth and/or inflation 13

    2.3 Stock Returns and Stock volatility 19

    CHAPTER 3 PREVIOUS METHODOLOGY 19

    CHAPTER 4 METHODS FOR EVALUATING FORECASTING

    ABILITY 24

    4.1 In-sample measures 24

    4.2 Out-of-sample measures 25

    CHAPTER 5 DATA 26

    5.1 Data for the Industrial Production 26

    5.2 Data for the GDP growth 27

    CHAPTER 6 METHODOLOGY AND MODELS 29

    6.1 Forecasting Models 29

    6.2 Methodology 32

    CHAPTER 7 RESULTS 34

    7.1 Results from the in-sample tests 34

    7.1.1 Industrial Production 34

    7.1.2 GDP growth 37

    7.2 Results from the out-of-sample tests 39

    7.2.1 GDP growth 40

    7.2.2 Industrial Production 45

    CHAPTER 6 CONCLUSIONS 48

    Appendix 51

    References 56

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    1. INTRODUCTION

    Many papers were written and a lot of studies were executed in order to find

    methods and variables, which can help people to predict the output growth and the

    inflation. A lot of researchers consider that the asset prices are forward-looking

    variables and as a result of this behavior they constitute a great tool in the technique

    of forecasting. But, all those who are interested to create forecasts they need to know

    exactly which predictor is the most reliable, which predicts better the GDP growth

    than the inflation and which predicts better one quarter than two years ahead.

    The purpose of this paper is to evaluate whether the asset prices are good

    predictors of the output growth. The study is applied with data of seven countries

    (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and it

    covers a period of time between 1966 until now. The predictive power of the

    candidate variables was evaluated with two methods. Using the Granger causality

    method with the whole sample (in-sample) we checked the causality between the

    dependent and the independent variable. Using the out-of-sample method we

    compared the mean square forecasting error (MSFE) of a benchmark model with the

    MSFE of the model, which involved the candidate variable. In our study the variables

    we used as candidate predictors are the term spread, the real stock returns and the

    short rate.

    This study can be useful for anyone who wants to understand the main

    methods of the forecasting and how the results of these methods are evaluated. This

    work is an effort to extend the research until nowadays using a large sample of data

    and to give the opportunity to someone to find a useful guide for the forecasting

    power of three main variables. Moreover, it tests in detail the predictive content of

    one predictor (the short rate) in which had not been given the appropriate emphasis in

    previous studies.

    The main results of this paper can be summarized as follows. First, using the

    whole sample we can check whether one indicator can be used as a predictor. But,

    although, the in-sample tests like the Granger causality test can be helpful and easy to

    make this work we cannot be sure about the reliability and the stability of this test.

    Second, although, most of the indicators prove to have a predictive power to forecast

    the output growth and the industrial production for forecasting period of one quarter

    or one month ahead, it seems to lose their ability to forecast when the forecasting

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    period increases. Third, one indicator appears to be a useful tool to predict a specific

    dependent variable for one country, but not for other countries. From the second and

    the third results we conclude that some asset prices have substantial and statistically

    significant predictive content for some countries and for some specific forecasting

    horizon. In this conclusion have Stock and Watson also been led in their paper in

    2003. Finally, we can note more thoroughly for our candidate variables that for the

    forecasting period of one quarter or one month ahead the research found similar

    predictive power in the short rate and the real stock returns when they are used to

    predict the GDP growth. However, when the forecasting horizon extend the real stock

    returns provide the most accurate forecasts since their predictive content remains

    quite stable independent of the horizon. For the Industrial production our results

    showed that no variable is a good predictor since their predictive content becomes

    really low when the forecasting horizon increases.

    The paper is organized as follows. In section 2 we reviewed all the previous

    papers that deal with the link between asset prices and other variables. We tried to

    separate this section in parts in order to be easier for reading. Thus, we present all the

    papers that examined the link between stock returns/term spread/short rate with either

    the inflation or output growth. In section 3 we write in detail the previous

    methodology of some papers. In section 4 we present the basic methods for evaluating

    the predictive content. Section 5 presents all the data that have been used in the

    dissertation. In section 6 we describe the models, the variables and the methodology

    applied. In section 7 we present the results of our tests and finally the section 8

    concludes.

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    2. LITERATURE REVIEW

    The current survey reviews papers that use asset prices (stock returns, yield

    curve, short rate and metal and oil prices.) as predictors of economic activity

    (industrial production, output growth and / or inflation).

    Forecasts using asset prices:

    2.1 Stock returns, output growth and/or inflation.

    Output growth

    It has been observed that stock price changes represent the efficient source of

    new information. Thus, stock returns could become a useful tool with great ability to

    forecast output growth and / or inflation. This link between economic activity and

    stock prices was examined by several researchers. Mitchell and Burns (1938),

    Grossman and Shiller (1981) and Fischer and Merton (1984) are some of these.

    The stock market contains information helpful for predicting GNP. However, this

    forecasting ability is not as accurate as someone would want and any predictive

    content is decreased by including lagged output growth (Harvey 1989). In another

    study, Titman and Warga (1989) observed whether stock returns predict changes in

    interest rates. They use both short-term and long-term interest rates on stock returns

    lagged one and two months an

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