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The track “Empirical Finance” provides PhD students with the skills necessary to conduct cutting- edge empirical research in capital markets.
Typology: Lecture notes
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Track Coordinator : Dieter Hess
Participating Professors : Jörg Breitung, Dieter Hess, Alexander Kempf, Roman Liesenfeld, Alexander Pütz
(at least 3 courses, reconcile selection with advisor)
This course is designed to introduce statistical and econometric tools used in empirical research of financial data. In an accompanying tutorial students will apply these tools to real and simulated financial data sets using the software package EVIEWS.
Financial research issues often require to analyze cross-sectional data over several points in time (panel data), e.g., companies earnings evolution or returns of assets in a portfolio. This course introduces participants to panel data analysis and factor models as powerful tools for analyzing datasets with cross- section and time dimension.
The course provides skills in conducting empirical research in finance by enabling participants to manage and work with the most widely used databases (in part., CRSP, Compustat, IBES). Participants discuss and re-estimate papers in corporate earnings research, firm valuation and analyst behavior.
The course provides skills in conducting empirical research in asset management. It enables participants to work with frequently used data (e.g. CRSP Mutual Fund and Stock Data, Thomson Reuters Mutual Fund Holdings Data) and to find solutions to common problems in asset management research.
The course provides skills in preparing a research paper in finance. It helps participants finding appropriate research topics, enables them to set up a research proposal, and provides insights how to write and publish the resulting paper.
(max 2 courses, reconcile selection with advisor)
The course provides the theoretical background for doing capital markets research. It covers, in particular, asset pricing theories and financial market design.
The course provides the theoretical background for doing corporate finance research, focusing on companies’ capital structure decisions, financing instruments and agency problems.
The course provides the background for doing research in mutual funds. It covers topics in performance measurement, looks at the organization of mutual funds and fund families, and analyzes active trading strategies.
The course provides the theoretical background for doing research in corporate valuation. It deals with financial statement analysis, projecting value relevant payoffs and applying equity valuation methods.
The course deals with selected issues in international tax planning and tax accounting of multinational enterprises. Participants analyze in detail tax planning opportunities like transfer pricing and supply chain management as well as risks for companies which are engaged in cross-border activities.
The course provides skills in understanding update business taxation research and to develop new research questions. In particular participants will analyze important empirical papers within the field and develop a research strategy.
The course provides the methodological background for analyzing and implementing current quantitative risk management models. An introduction to risk measures, to the aggregation of risk, to stochastic processes as well as to the most common approaches regarding market risk, credit risk and operational risk is given. Participants practice basic simulation methods using the software package Matlab.
The course introduces the principles and methods of Bayesian inference in econometrics. It also covers modern methods for Bayesian posterior simulation, including Importance Sampling and Markov Chain Monte Carlo (MCMC) procedures. Participants apply Bayesian methods to analyze linear regression models and extensions of the regression model and practice these methods using the software package Matlab.
Econometric models which include unobservable and/or latent variables may easily be analyzed and applied when formulated in state space representation. This course provides an introduction to time discrete and time continuous, linear and nonlinear state space models as well as common Kalman filter techniques to estimate, nowcast and forecast the variables of interest. Participants practice these methods using the software package Matlab.
The course introduces participants to modern econometric methods used to analyze models for binary, multinomial and limited dependent variables as well as count data. Participants practice these methods using the software packages LIMDEP and Matlab.