(LOXR271) -  DRAKOS KONSTANTINOS

Περιγραφή Μαθήματος

EDUCATIONAL AIM

 

The course aims to provide an in-depth understanding of the econometric tools used in analysing shipping and financial data. Apart from the theoretical presentation of methods, the course will place emphasis on the implementation and explanation of techniques.  

 

EDUCATIONAL OBJECTIVES

 

• Introduce students to the nature of data and their statistical properties.  
• Familiarize students with data analysis and model building.
• Familiarize students with modern time series techniques.

 

LEARNING OUTCOMES

 

On completing the course participants will:

• Have an understanding of data properties and analyzing them.
• Understand the process of model building and testing.
• Be able to address and tackle issues such as:

*      Dealing with data

*      Modelling economic and financial relationships

*      Use Multivariate models

*      Model time-varying volatility

*      Modelling long-run relationships

 

 

THEMATIC AREAS

 

• Thematic area 1 Simple      Regression

Review of main statistical concepts and types of economic/financial data by variation source (cross-sectional, time series, panel). Linear Regression with one Regressor, Ordinary Least Squares Principle, Interpretation of regression output, fitted values, residuals, measures of fit, hypothesis tests and confidence intervals.

• Thematic area 2 Multiple      Regression

Multiple Regression, interpretation of regression output, measures of fit, multicollinearity, dummy variable trap, Joint hypotheses tests on multiple coefficients, Other types of hypotheses involving multiple coefficients.

• Thematic area 3 Non-Linear Functions     

Nonlinear functions of one variable (polynomials, logarithmic transformations), interaction effects between independent variables (continuous/continuous, binary/binary, continuous/binary, binary/continuous).

• Thematic area 4 Diagnostic      Testing

Testing for violations of regression assumptions, Testing for Heteroscedasticity, Testing for Autocorrelation, parameter Stability Tests.     

• Thematic      area 5 Time-Varying Volatility

Detection, Identification, Estimation of Autoregressive Conditionally Heteroscedastic (ARCH) Models, Multivariate GARCH Models.

• Thematic area 6 Non-Stationary Time      Series

Types of Non-Stationarity, Random Walks, implications, Testing for Unit Roots (Dickey-Fuller, Phillips-Perron, Banerjee et al. and Zivot and Andrews Procedures).

• Thematic      area 7 Long Run Relationships and      Cointegration

Dynamic Specification, Defining Cointegration, Error Correction Mechanism, the Engle-Granger Approach.          

 

   

BRIEF DESCRIPTION OF THEMATIC AREAS

 

• Simple Regression

What is and why we employ a regression model. What are the underlying assumptions and how parameters are estimated. How do we interpret the estimation results. How Hypothesis Testing is conducted.     

• Multiple Regression

Why do we use a multiple regression model. Estimation, interpretation of results, Joint Hypotheses Testing.

• Non-Linear Functions

How to detect possible non-linearities and how to model them. The use of dummy variables and polynomials.  

• Diagnostic Testing

Testing for violations of regression assumptions, Testing for Heteroscedasticity, Autocorrelation and Parameter Stability.

•  Time-Varying Volatility

How to detection, identify, and estimate Autoregressive Conditionally Heteroscedastic (ARCH) Models. The use of Multivariate GARCH Models.

• Non-Stationary Time Series

Which are the types of Non-Stationarity. What are the properties of Random Walks and the implications for modelling. How to formally test for Unit Roots (the Dickey-Fuller test).  

• Long Run Relationships and      Cointegration

How one can move from static to Dynamic models. What is the Error Correction Model and its relationship with Cointegration. The Engle-Granger Approach.          

 

 

 

COURSE ASSESSMENT

2 take home projects each representing 15% of the final mark and written final exam representing 70%. 

 

 

 

READING ΜΑTERIAL

 

Useful Textbooks

• Draper, N. and Smith, H. (1998). “Applied Regression      Analysis”, Wiley 3^rd      edition.
• Greene, W., (2003).      “Econometric Analysis”, 7^th      edition.
• Griffiths,      W., Hill, C., Judge, G., (1993). “Learning      and Practicing Econometrics”, Wiley.
• Wooldrige, J., (2012). “Introductory      Econometrics: A Modern Approach”, Cengage Learning.

 

 

Useful Articles

Bollerslev, T. (1986). “Generalised Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, 31, 307-327.

Box, G.E.P. and G.M. Jenkins, (1970). “Time Series Analysis: Forecasting and Control”, Holden Day, San   Francisco

Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427{431.

Engle, R. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United   Kingdom Inflation”, Econometrica, 50, 987-1007.

Engle, R. F., and Granger, C. W. J. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55, 251{276.

Granger, C.W.J., (1969). “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods”, Econometrica, 37, 424-438.

Granger, C. W. J. (1981). “Some properties of time series data and their use in econometric model specification”, Journal of Econometrics, 16, 121{130.

Johansen, S. (1991). “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models”, Econometrica, 59, 1551-1580. 

Johansen, S. (1992). “Testing Weak Exogeneity and the Order of Cointegration in UK Money Demand Data”, Journal of Policy Modelling, 14, 313-334.

Kavussanos, M. and Alizadeh, A. (2002). “Seasonality Patterns in Tanker Spot Freight Rate Markets”, Economic Modelling, 19, 747-782.

Kavussanos, M. and Nomikos, N. (2003). “Price Discovery, Causality and Forecasting in Freight Futures Markets”, Review of Derivatives Research, 6, 203-230.

Kavussanos, M. and Visvikis, I. (2008). “Hedging effectiveness of the Athens stock index futures contracts”, European Journal of Finance, 14(3), 243–270. 

 

 

In addition to the above, it is recommended to read:

• The finance related journals, such as: Journal of Finance, Review      of Financial Studies, Journal of Financial and Quantitative Analysis,      Journal of Financial Economics, Financial Analysts Journal, Journal of      Applied Corporate Finance, Journal of Portfolio Management, Journal of      Investment Management, Financial Management, Journal of Futures Markets,      Journal of Derivatives, etc.
• Financial periodicals/papers, which include: Financial Times,      Economist, Wall Street Journal, Nautemporiki.

 

Useful Databases for data collection:

Reuters, Bloomberg, Datastream, Web pages of Companies and Stock Exchanges.

 

Other references - publications in the area which may be used during lectures

 

Ημερομηνία δημιουργίας

Πέμπτη, 24 Σεπτεμβρίου 2015