## ICM283-Economic Modelling and Analysis of Shipping Markets

Module Provider: ICMA Centre
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2017/8

Module Convenor: Dr Ilias Visvikis

Summary module description:

Aims:
Following the Introduction to Quantitative Methods for Finance module in the previous term, the primary objective of this module is to provide an application to the most common empirical methods used in the economic modelling and analysis of shipping markets. The module provides the students with the business modeling tools and skills necessary to conduct empirical analysis in shipping. It follows a “hands-on” approach where students use real shipping market data and dedicated software and apply the empirical assessment methods through case studies. Emphasis is placed on practical applications of the research techniques used in international shipping markets..

Assessable learning outcomes:
By the end of the module, it is expected that the students will be able to:
• Explain the fundamentals of the statistical theory underlying the tools employed to estimate and test econometric models in shipping
• Interpret and analyse the results from an estimated econometric model using shipping data
• Formulate and validate econometric models testing maritime economic theories and hypotheses
• Appraise and implement different econometric modeling procedures in maritime economics/finance

The module also aims to encourage the development of IT skills and in particular the employment of data using statistical software packages (i.e. Eviews). Students will also improve their ability to translate abstract theoretical concepts into practical solutions to maritime economics problems.

Outline content:
1. Linear Regression Modelling and Applications in Shipping
- Simple linear regression
- The assumptions of the Ordinary Least Squares
- Properties of OLS
- Precision and standard errors
- Hypothesis testing
- Normal and Student-t probability distributions
- Confidence intervals
- Modelling Seasonality in Shipping Markets
- Estimating Simple Piecewise Linear Functions
- Markov Switching Models
- Example of Markov Switching Models
Indicative Case Studies:
• Kavussanos, M. G., Juell-Skielse, A. and Forrest, M. (2003): International Comparison of Market Risks across Shipping-related Industries, Maritime Policy and Management, 30(2): 107-122.

2. Modelling Shipping Firms’ Stock Returns and Risk Factors
- Generalizing the simple OLS model
- How the parameters are calculated
- Testing single hypothesis: the t-test
- Testing multiple hypotheses: the F-test
- The relationship between the t- and F-distributions
- Goodness of fit statistics
Indicative Case Study:
• Wolfgang Drobetz, W., Schilling, D and Tegtmeier, L. (2010): Common Risk Factors in the Returns of Shipping Stocks, Maritime Policy and Management, 7(2): 93-120.

3. The Information Efficiency of Shipping Markets
- Violations of the OLS assumptions
- Multicollinearity
- Adopting the wrong functional form
- Omission of an important variable
- Inclusion of an irrelevant variable
- Parameter stability tests
- Stationarity and Unit Root testing (DF, ADF, PP and KPSS tests)
- Error-Correction Models (ECM)
- Engle and Granger and Johansen cointegration tests
- Vector Error Correction Models (VECM)
Indicative Case Study:
• Kavussanos, M., Visvikis, I. and Menachof, D. (2004): The Unbiasedness Hypothesis in the Freight Forward Market, Review of Derivatives Research, Volume 7, Issue 3, pp. 241-266.

4. Modelling and Forecasting Shipping Factors
- Strictly and weakly stationary process
- White noise process
- Moving Average (MA) processes
- Autoregressive (AR) processes
- Autoregressive Moving Average (ARMA) processes
- The Box-Jenkins ARMA model
- Information criteria for ARMA model selection
Indicative Case Studies:
• Batchelor, R. Alizadeh, A. and Visvikis, I. (2007): Forecasting Spot and Forward Prices in the International Freight Market, International Journal of Forecasting, Volume 23, pp. 101-114.

5. Economic Modelling and Cross-Effects between Freight and Commodity Markets
- Simultaneous equations
- Exogeneity principal and Tests
- Introduction to Vector Autoregressive (VAR) models
- Causality tests
- ARCH and GARCH Processes
- Estimation of ARCH/GARCH Models
- Extensions to the basic GARCH Model
- Asymetric GARCH models (GJR and EGARCH)
- Tests of asymmetries in Volatility
Indicative Case Study:
• Kavussanos, M., Visvikis, I. and Dimitrakopoulos, D. (2010): Information Linkages between Panamax Freight Derivatives and Commodity Derivatives Markets, Maritime Economics and Logistics, Volume 12, Issue 1, pp. 91-110.

Brief description of teaching and learning methods:
Lectures will be used for the exposition of theory. Classes will be used to discuss non-assessed problem sets and case studies. There will be 5 2-hour lectures and 5 1-hour seminars. The techniques used to achieve the stated module objectives will consist of a combination of active teaching, question-answer sessions, class examinations, assignments and class discussions.

Contact hours:
 Autumn Spring Summer Lectures 10 Practicals classes and workshops 5 Guided independent study 85 Total hours by term 100.00 Total hours for module 100.00

Summative Assessment Methods:
 Method Percentage Written assignment including essay 70 Class test administered by School 30

Other information on summative assessment:
2,000 word group assignment and mid-term test

Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Length of examination:

Requirements for a pass:
50% weighted average mark

Reassessment arrangements:
As part of the overall examination arrangements for the MSc programme.