The topic of this term’s seminar is:
Empirical analysis of volatility and liquidity on cryptocurrency markets
Participants are required to write a seminar paper on their chosen topic and give a short presentation by the end of the term. Each participant’s topic will be agreed upon based on adequacy and personal interest. The focus of this seminar is on quantitative analysis which involves computing in statistical software, such as Matlab, R, or Python. Depending on the number of participants, purely literature-based topics might be offered as well.
Students are able to
- select, acquire, and process necessary market data.
- use statistical software, such as Matlab, R, or Python, to analyze market data.
- interpret and discuss results critically, and put them into economically meaningful context.
- write a scientific paper and present its content in a scientific talk.
M. Smirlock and L. Starks. An empirical analysis of the stock price--volume relationship. Journal of Banking and Finance, 12(1):31-41, 1988.
A. Serletis. Maturity effects in energy futures. Energy Economics, 14(2):150-157, 1992.
J.H. Herbert. Trading volume, maturity and natural gas futures price volatility. Energy Economics, 17(4):293-299, 1995.
W.D. Walls. Volatility, volume and maturity in electricity futures. Applied Financial Economics, 9(3):283-287, 1999.
R.D. Ripple and I.A. Moosa. The effect of maturity, trading volume, and open interest on crude oil futures price range-based volatility. Global Finance Journal, 20(3):209-219, 2009.
M. Mougoué and R. Aggarwal. Trading volume and exchange rate volatility: Evidence for the sequential arrival of information hypothesis. Journal of Banking & Finance, 35(10):2690-2703, 2011.
Preparation of a seminar paper (20-30 pages) and its presentation (15-20 minutes).
The first meeting (details see above) serves to introduce potential topics. Please register for this meeting by email to marcel.kremer (at) uni-due.de with subject "Seminar Selected Topics in RM SS19". Allocation of topics is planned to be completed by May 3, 2019. Please note that deregistration and thus avoiding receiving malus points for not fulfilling the examination requirements is only possible until May 31, 2019.
A basic knowledge of statistical software, such as Matlab, R, or Python, is sufficient. Having taken the course Zeitreihenanalyse is ideal.