Topics for Master Theses Prof. Dr. Marcel Prokopczuk, CFA

Transcription

1 Topics for Master Theses Prof. Dr. Marcel Prokopczuk, CFA

2 Remarks The following slides are suggestions for potential topics for MSc theses. Students are free to suggest their own topics. If you are interested in one of the topics or would like to suggest your own topic, please send an to Prof. Dr. Marcel Prokopczuk. Please attach your CV, and transcript of records to this . The MSc thesis will be supervised by Prof. Dr. Marcel Prokopczuk or Dr. Fabian Hollstein. 2

3 Which Asset Pricing Model for Commodities? Several recent papers have tried to explain the cross section of commodity prices using common factors. It remains unsolved, which factors, if any, are able to price the commodity universe. In the first part of the thesis, the objective is to review extant literature. In the second and main part, the objective is to conduct, based on the candidate factors identified in the first step, a separate empirical analysis. Bakshi G., Gao X., Rossi A (2014): A Better Specified Asset Pricing Model to Explain the Cross- Section and Time Series of Commodity Returns. Working Paper. Daskalaki, C., Kostakis, A. and Skiadopoulos, G. (2014): Are there common factors in commodity futures returns? Journal of Banking and Finance, 40,

4 Investing in Commodity Markets Commodity Markets have attracted the attention of financial investors. Theoretically, an expansion of the investment universe, should yield improved portfolio performance. However, whether this holds empirically, is an open question. The first objective is an review of the empirical evidence that has been presented in extant work. The second objective and main task is an empirical investigation. Daskalaki, C. And Skiadopoulos, G. (2011): Should investors include commodities in their portfolios after all? New evidence. Journal of Banking and Finance, 35, Bessler, W. and Wolff, D. (2015): Do commodities add value in multi-asset portfolios? An out-ofsample analysis for different investment strategies. Journal of Banking and Finance, 60,

5 Estimating Factor Pricing Models: OLS vs. GMM Empirical testing an asset pricing model is a non-trivial task. Several econometric approaches have been put forward in the literature. Most prominent are simple time-series and cross sectional regressions employing OLS and the Generalized Method of Moments. The objective of this topic is to first comprehensively review these approaches and discussing their theoretical advantages and disadvantages. In the second part, a detailed simulation study should be performed to analyze the practical implications of the theoretical arguments. Cochrane (2005): Asset Pricing. Princeton University Press. 5

6 Long-Run Risks Model Calibrations One of the most important theoretical contributions in the field of asset pricing during the last 20 years is the so-called long-run risk model of Bansal and Yaron (2004). This model solves many of the puzzles of classical asset pricing. However, the model s ability to capture these puzzles strongly depends on the model calibration. The two tasks are to review of the literature on long-run risks models. And secondly to analytically evaluate different calibrations for long-run risks models. Bansal, R., & Yaron, A. (2004). Risks for the long run: A potential resolution of asset pricing puzzles. Journal of Finance, 59(4), Schlag, C., Semenischev, M., & Thimme, J. (2016). Predictability and the Cross-Section of Expected Returns in Models with Long-Run Risks. Available at SSRN

7 Idiosyncratic Volatility According to classical theory, idiosyncratic volatility can be fully diversified and, thus, should not be priced in the market. However, Ang et al. (2006) show that idiosyncratic volatility is strongly negatively priced, a finding which is often referred to as idiosyncratic volatility puzzle. However, the measurement of idiosyncratic volatility depends on the factor model used. The two tasks are to first review of the literature on idiosyncratic volatility. Secondly, you should empirically evaluate the idiosyncratic volatility puzzle with the newly developed factor models. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross section of volatility and expected returns. Journal of Finance, 61(1), Fama, E.F., French, K.R., A five-factor pricing model. Journal of Financial Economics, 116, Fama, E.F., French, K.R., Dissecting anomalies with a five-factor model. Review of Financial Studies, 29, Hou, K., Xue, C., Zhang, L., Digesting anomalies: An investment approach. Review of Financial Studies, 28,

8 Good Beta, Bad Beta Asset price changes are determined by news affecting two major channels. On the one hand, there are direct news about future cash flows. On the other, investors might react to other news by increasing the discount rate applied to future dividend streams. Using this insight, the Campbell Shiller decomposition delivers a powerful tool that enables us to split the market beta into two parts a cash-flow beta and a discount-rate beta. The main task is the empirical replication of the Study of Campbell and Vuolteenaho (2004) for a more recent dataset. Campbell, J. Y., & Shiller, R. J. (1988). The dividend-price ratio and expectations of future dividends and discount factors. Review of financial studies, 1(3), Campbell, J. Y., & Vuolteenaho, T. (2004). Bad beta, good beta. American Economic Review, 94(5),

9 Fundamentals-based Priors for Beta Estimation Prior information can be useful to hedge the measurement error in beta estimates. The paper cited below uses a fundamentals-based prior estimated with the Markov Chain Monte Carlo (MCMC) method. The authors show that the betas using the fundamentals-based prior have superior properties. The main task is to replicate the study of Cosemans et al. (2016) for a European dataset. Cosemans, M., Frehen, R., Schotman, P. C., & Bauer, R. (2016). Estimating security betas using prior information based on firm fundamentals. Review of Financial Studies, 29(4),

10 Analysis of the International Coal Market Coal is an important source of energy in many economies and traded worldwide. In contrast to oil, coal reserves are more dispersed around the globe. Traditionally, coal markets had regional character but this a changed over the last years. Whether international coal markets are fully integrated remains an open question. Also, the relationship of coal prices with the price of other assets might have changed over time. The objective is to first provide a comprehensive tdescription of the current nature of coal trading internationally. In the second and main part, an empirical analysis on the inegration and the most important risk factors of coal markets should be conducted Geman, H., & Liu, B. (2015). Are world natural gas markets moving toward integration? Evidence from the Henry Hub and National Balancing Point forward curves. Journal of Energy Markets, 8, Li, R., Joyeux, R., and Ripple, R. D. (2010). International steam coal market integration. Energy Journal, 31, Papiez, M. and Smiech, S. (2013). Causality-in-mean and causality-in-variance within the international steam coal market. Energy Economics, 36, Papiez, M. and Smiech, S. (2015). Dynamic steam coal market integration: Evidence from rolling cointegration analysis. Energy Economics, 51,

11 Analysis of Covered Bond Markets Covered Bonds (Pfandbriefe) are a special class of bonds which have been introduced by Friedrich the Great (Friedrich der Große) during the 18th century in Germany. Until recently, these fixed income securities were not popular outside of Germany. However, due to the recent financial crisis they have gained a lot of international attention. For example, the former chairman of the US central bank system Fed Ben Bernanke remarked that covered bonds do help to resolve some of the difficulties associated with the originate-to-distribute model, (Bernanke, 2009). The first objective of this topic is to provide a detailed review of the covered bond market. Secondly, an empirical analysis of the German or European covered bond market should be conducted as extensions of the studies by Prokopczuk and Vonhoff (2012) and Prokopczuk et al. (2013). Bernanke, B.S. (2009): The future of mortgage finance in the United States. B.E. Journal of Economic Analysis & Policy, 9, 1-9. Prokopczuk, M. & Vonhoff, V. (2012): Risk premia in covered bond markets. Journal of Fixed Income, 22, Prokopczuk, M., Siewert, J. & Vonhoff, V. (2013): Credit risk in covered bonds. Journal of Empirical Finance, 21,

12 Correlation Trading Correlation trading refers to a strategy that is exposed to the (average) correlation of different assets. Increasing (decreasing) correlations in the market lead to profit or losses. The objectives is to first provide an overview how correlation trading strategies can be implemented. The second objective is to empirically and theoretically analyze why and when correlation trading might be a worthwhile strategy. How is correlation trading related to volatility trading. Deng, Q. (2008). Volatility Dispersion Trading. Working Paper. Driessen, J., Maenhout, P. and Vilkov, G. (2012). Option-Implied Correlations and the Price of Correlation Risk. Working Paper. 12

13 Fractional Co-integration in Commodity Futures Markets Prices of many commodities are considered and often found to be cointegrated which poses certain implications for price discovery and market efficiency. However, recent econometric work suggests that these markets are fractionally cointegrated. The objective of this topic is to first provide a comprehensive review of fractional cointegration. In the second part, an empirical study should be performed which analyzes major commodity markets regarding these aspects. Dolatabadi, S., Nielsen, M. & Xu, K. (2015): A fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets. Journal of Futures Markets, 35, Lien, D. & Tse, Y. K. (1999). Fractional cointegration and futures hedging. Journal of Futures Markets, 19, Remark This MSc thesis topic will be jointly supervised by Professor Prokopczuk (FMT) and Professor Sibbertsen (Statistics) 13

14 Estimating Non-linear Term-structure Models with the Unscented Kalman Filter Kalman-filtering is a common econometric procedure for estimating term-structure models (and many, many other applications). However, the original Kalman filter requires that the measurement equations are linear, which is often not the case when considering options contracts. The first objective of this topic is to get a solid understanding of the Kalman filter and its extensions, mainly the extended and the unscented Kalman filter. In the second part, these two extensions should be compared through a detailed simulation study. Babbs, S. & Nowman, K. (1999): Kalman filtering of generalized Vasicek term structure models. Journal of Financial and Quantitative Analysis, 34, Harvey, A. (1989): Forecasting, structural time series models and the Kalman filter. Cambridge University Press. Lautier, D. & Galli, A. (2004): Simple and extended Kalman filters: an application to term structures of commodity prices. Applied Financial Economics, 14, Wan, E. & van der Merwe, R. (2000): The Unscented Kalman Filter for Nonlinear Estimation. Adaptive Systems for Signal Processing, Communications, and Control Symposium AS- SPCC,

15 Option Pricing Models The classical option pricing model of Black and Scholes (1973) is still very widely used but has clear shortcomings. The most important shortcoming is that it assumes constant volatility over the lifetime of the option. One extension in Heston (1993) allows for stochastic volatility. The two tasks are to review of the literature on option pricing models. And secondly to empirically evaluate the pricing abilities of these different models. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6(2),

16 Option Pricing under Long Memory Many financial time series exhibit long memory properties. This is especially true for volatility, which is a key input for valuing options. Traditional option pricing models often ignore the long memory feature. The objective of this thesis is first to review how long memory can be integrated in an option pricing model. In the second step, an empirical study should be performed in order to investigate whether such models can outperform classical approaches. Christoffersen, P. & Jacobs, C. (2004): Which GARCH Model for Option Valuation? Management Science, 50, Fedotov, S. & Tan, A. (2005): Long memory stochastic volatility in option pricing. International Journal of Theoretical and Applied Finance, 8, Taylor, S. (2014): Consequences for Option Pricing of a Long Memory in Volatility. In: Handbook of Financial Econometrics and Statistics, Chapter 32, Remark This MSc thesis topic will be jointly supervised by Professor Prokopczuk (FMT) and Professor Sibbertsen (Statistics) 16

17 Valuation of Weather Derivatives Weather derivatives are an interesting innovation in financial markets. For example, at the Chicago Mercantile Exchange temperature futures can be traded since Weather derivatives are distinct from normal financial derivatives, as the underlying, e.g. temperature or the occurrence of hurricanes, is not storable. However, in contrast to financial assets, weather might be easier to predict. The first objective of this topic is to provide a comprehensive review of the market for weather derivatives and approaches that have been proposed for pricing these securities. In the second part, an empirical study should be conducted comparing the most promising approaches. Dorfleitner, G. & Wimmer, M. (2011): The pricing of temperature futures at the Chicago Mercantile Exchange. Journal of Banking & Finance, 34, Jewson, S. & Brix, A. (2005): Weather Derivative Valuation. Cambridge University Press. Härdle, W. & López Cabrera, B. (2012): The Implied Market Price of Weather Risk, Applied Mathematical Finance, 19,