Course description: the course is intended to prepare 2nd-year master students for writing their master theses. It briefly overviews some of the most influential and/or controversial papers in financial economics, econometrics and macroeconomy. Papers are replicated in R using original datasets. During class students are obliged to take 2 individual projects, make 4 peer-reviews and one literature survey. All homeworks, R models and codes, datasets, reports, peer-reviews, and discussions are stored in internal knowledge base.
Prerequisites: course is taught to master students of the final year, final semester. It is assumed that student had successfully completed classes on financial markets, corporate governance, statistics, econometrics, financial mathematics, portfolio theory. Knowledge of R language is preferable, but not necessary.
Week 1. Class intro.
What is financial economics? Why it is important for investment professional? What makes good master dissertation in financial economics?
Lab work: Granger causality: what was first, egg or chicken? Philips curve case. Granger causality of unemployment and GDP.
Homework: Writing research proposal.
Project: Testing Philips curve for assigned country.
Week 2. Stylized facts about financial time series and econophysics
Bachelier, Mandelbrot, Fama contributions. Non-stationarity. Memory. Fractals. Intrinsic time.
Homework: Peer review of assigned research proposal.
Project: Time series analysis and report writing.
Week 3. GARCH models
Engle, 1987 contribution: volatility of macro series. Volatility spillovers, MDH. Unit roots, cointegration.
Homework: Answer to peer-review of research proposal.
Project: Writing report on volatility spillovers between regional markets and causality.
Week 4. Equity premium puzzle
Approaches to measure equity premiums. Use of GARCH and CDS.
Homework: Final version of research proposal.
Project: Writing report on equity premium puzzle.
Week 5. Benchmarking, efficiency, productivity
Data Envelopment Analysis as multi-criteria decision making method. DEA as total factor productivity measure. DEA models of Olympic teams, regions, construction firms, banks. Malmquist productivity index.
Homework: Developing pension fund DEA model.
Week 6. Sustainability and energy economics
Building models in Long-Range Energy Alternatives Planning (LEAP) environment.
Project: Building regional LEAP model. Panel regression/causality of regional growth to emissions.
Week 7. Productivity spillovers: regional, firm and sectoral levels
Various forms of panel regression.
Homework: first peer-review of assigned research report.
Week 8. Classic experiments of behavioral finance
Homework: second peer-review of assigned research report. Answer to peer-reviewers feedback.
Week 9. Portfolio optimization.
Factor models. Black-Litterman, Black-Treynor. Frontier resampling. Monte-Carlo, Meucci. Random forest.
Homework: backtest Black-Litterman model. Document it, comment R code.
Week 10. Market efficiency: Fama-French contribution
In-class replication and discussion of BL backtests.
Homework: prepare integrated survey of in-class research reports and external literature.
Week 11. Performance analysis
Sharpe et. al. contribution to mutual fund performance. Performance attribution. Funds DEA model: results and class discussion.
Week 12. In-class presentation of individual projects: advances, controversies, flaws, further research.