Teaching
Theses

Theses

Theses at the Institute of Financial Markets include an independent empirical/quantitative study (pure literature studies are not possible). Thus it is generally necessary to apply appropriate statistical software (R or Matlab). We thus recommend to get familiar with the software at an early stage.

BACHELORTHESES

If you are interested to write your thesis at this institute, please send a numbered list with at least five topic preferences to Mr. Würsig.

Bachelortheses topics in the area of Capital Markets and Investments

  • Testing the Arbitrage Pricing Theory

    Task:

    • Ross (1976) has developed the Abitrage Pricing Theory (APT) as a general theory for pricing assets. The basis of this theory is no-arbitrage. Chen et al. (1986) provide an empirical examination of the APT.
    • Review of the literature on the APT.
    • Empirically test the APT using a European dataset.

    Basic Literature:

    • Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic Forces and the Stock Market. Journal of Business, 59(3), 383-403

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  • Multi-Factor Models

    Task:

    • Based on the failure of the CAPM, researchers have developed several empirical asset pricing models.
    • Empirical examination of the German stock market on the basis of the Fama-French 3-Factor Model and Carhart 4-Factor Model.
    • Compute the factors yourself and test whether the models can price assets via a GRS test.

    Basic Literature:

    • Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
    • Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3–56.
    • Gibbons, M. R., Ross, S. A., & Shanken, J. (1989). A test of the efficiency of a given portfolio. Econometrica: Journal of the Econometric Society, 1121-1152.

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  • The Conditional CAPM

    Task:

    • The static version of the Capital Asset Pricing Model (CAPM) fails in explaining cross-sectional differences between stock returns. However, one possible reason for this could be time-variation in beta factors.
    • Theoretical description of different versions of the conditional CAPM.
    • Empirical evaluation of the conditional CAPM with a European dataset.


    Basic Literature:

    • Jagannathan, R., & Wang, Z. (1996). The conditional CAPM and the cross‐section of expected returns. Journal of Finance, 51(1), 3-53.
    • Lewellen, J., & Nagel, S. (2006). The conditional CAPM does not explain asset-pricing anomalies. Journal of Financial Economics, 82(2), 289-314.

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  • Beta Estimation with Prior Information

    Task:

    • Prior information can be useful to hedge the measurement error in beta estimates. There are several possibilities of how to make use of this prior information. It can either be taken from the whole cross-sectional distribution or a subset of firms with similar characteristics.
    • Empirical investigation of the performance of betas estimated with prior information.

    Basic Literature:

    • Karolyi, G. A. (1992). Predicting risk: Some new generalizations. Management Science, 38(1), 57-74.
    • Vasicek, O. A. (1973). A Note on Using Cross‐sectional Information in Bayesian Estimation of Security Betas. Journal of Finance, 28(5), 1233-1239.

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  • Infrequent Trading and Beta Estimation

    Task:

    • If stocks are traded infrequently, betas of these stocks will be biased downward. Scholes and Williams (1977) and Dimson (1979) propose ways to adjust the estimates to be robust to this.
    • Empirical investigation of the performance of betas that account for infrequent trading using the method of McInish et al. (1986) for the German stock market.

    Basic Literature:

    • Dimson, E. (1979). Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics, 7(2), 197-226.
    • McInish, T. H., & Wood, R. A. (1986). Adjusting for beta bias: An assessment of alternate techniques: A note. Journal of Finance, 41(1), 277-286.
    • Scholes, M., & Williams, J. (1977). Estimating betas from nonsynchronous data. Journal of Financial Economics, 5(3), 309-327.

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  • Indiosyncratic Volatility

    Task:

    • 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”.
    • Review of the literature on idiosyncratic volatility.
    • Empirical evaluation of the idiosyncratic volatility puzzle for a European dataset.

    Basic Literature:

    • Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
    • Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2009). High idiosyncratic volatility and low returns: International and further US evidence. Journal of Financial Economics, 91(1), 1-23.
    • Bali, T. G., & Cakici, N. (2008). Idiosyncratic volatility and the cross section of expected returns. Journal of Financial and Quantitative Analysis, 43(01), 29-58.

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  • Betting Against Beta

    Task:

    • One popular explanation for the failure of the classical CAPM are so-called leverage-constraints. Because leverage is limited, investors have to buy high-beta stocks to increase the risk of their portfolio. High demand for these stocks causes them to have negative alphas and the empirical security market line to be too flat relative to the predictions by the CAPM.
    • Theoretical examination of leverage-constraints.
    • Empirical investigation of the performance of a betting-against-beta strategy.

    Basic Literature:

    • Black, F. (1972). Capital market equilibrium with restricted borrowing. Journal of Business, 45(3), 444-455.
    • Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.

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  • Liquidity Risk

    Task:

    • For illiquid stocks, trading a large position can substantially change the market price. If investors hold these illiquid stocks and they have to liquidate their position, this can create substantial costs. Hence, investors might require a premium for holding illiquid stocks.
    • Theoretical description of how liquidity might affect asset prices.
    • Empirical evaluation of liquidity risk for a European dataset.

    Basic Literature:

    • Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.
    • Pastor, L., & Stambaugh, R. F. (2001). Liquidity risk and expected stock returns. Journal of Political Economy, 111, 642-685.

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  • The Momentum Anomaly

    Task:

    • The momentum anomaly describes a pattern that in the medium term, “losers” on average continue to be “losers” and winners tend to further appreciate in their prices.
    • First review the empirical and theoretical literature on the momentum anomaly.
    • Empirically investigate momentum using portfolio sorts or regression tests.

    Basic Literature:

    • Goyal, A., & Jegadeesh, N. (2017). Cross-Sectional and Time-Series Tests of Return Predictability: What Is the Difference? Review of Financial Studies, 31(5), 1784-1824.
    • Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.

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  • Short-term Reversal

    Task:

    • In the stock market, monthly returns seem to be negatively autocorrelated. That is, there is a short-term reversal effect in stock prices.
    • First review the empirical and theoretical literature on short-term reversal.
    • Empirically investigate short-term reversal using portfolio sorts or regression tests.

    Basic Literature:

    • Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45(3), 881-898.

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  • The Value Anomaly

    Task:

    • In international stock markets, a prominent feature is that stocks with high book-to-market ratios (“value stocks”) typically outperform those with low book-to-market ratios (“growth stocks”).
    • First review the empirical and theoretical literature on the value effect.
    • Empirically investigate value strategies using portfolio sorts or regression tests.

    Basic Literature:

    • Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. Journal of Finance, 68(3), 929-985.
    • Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. Journal of Finance, 47(2), 427-465.

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  • Technical Analysis: Price-based Indicators

    Task:

    • In the last decades the technical analysis (TA) has been developed as a possibility to forecast future price movements. The TA represents an alternative to the fundamental analysis. Both approaches contradict the predictions of the well-known‚ Efficient Market Hypothesis‘ (Fama, 1972), but might lead to profitable trading strategies.
    • The objective is to introduce the TA, particularly with focus on price-based indicators.
    • Further, to develop and evaluate trading strategies based on the introduced indicators for the stock market.

    Basic Literature:

    • Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments. 10th Ed., McGraw-Hill: New York.
    • Chen, J. (2010). Essentials of Technical Analysis for Financial Markets. John Wiley & Sons: New Jersey.

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  • Portfolio Insurance: Constant proportion Portfolio Insurance

    Task:

    • Empirical investigation of the CPPI strategy for a chosen market.
    • (Partial) replication of Zhang et al. (2015).

    Basic Literature:

    • Zhang, T., Zhou, H., Li, L., & Gu, F. (2015). Optimal rebalance rules for the constant proportion portfolio insurance strategy – Evidence from China, Economic Systems, 39, 413-422.
    • Black, F., & Perold, A. F. (1992). Theory of constant proportion portfolio insurance, Journal of Economic Dynamics and Control, 16, 403-426.

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  • Mutual Fund Performance

    Task:

    • Review of the literature on mutual fund performance.
    • Empirical examination of the profitability of actively managed portfolios compared to index funds / passively managed funds.

    Basic Literature:

    • Fortin, R., & Michelson, S. (2002). Indexing versus active mutual fund management. Journal of Financial Planning, 15(9), 82-95.
    • Sharpe, W. F. (1991). The arithmetic of active management. Financial Analysts Journal, 47(1), 7-9.
    • Sharpe, W. F. (1992). Asset allocation: Management style and performance measurement. Journal of Portfolio Management, 18(2), 7-19.
    • Goetzmann, W., Ingersoll, J., & Spiegel, M. (2007). Portfolio Performance Manipulation and Manipulation-proof Performance Measures. Review of Financial Studies, 20(5), 1503-1546.

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  • The Performance of Sharia-Compliant Funds

    Task:

    • During the recent years, the demand for financial products that are in accordance with certain ethical standards has increased. For example, there are certain products and funds that seek strategies aligned with Islamic law. Typically, these strategies exclude certain products and sectors, which might cost diversification benefits.
    • Theoretical underpinnings and requirements of sharia-compliant products.
    • Empirical evaluation mutual funds that are sharia-compliant vs. usual funds.

    Basic Literature:

    • Zaher, T. S., & Kabir Hassan, M. (2001). A comparative literature survey of Islamic finance and banking. Financial Markets, Institutions & Instruments, 10(4), 155-199.
    • Derigs, U., & Marzban, S. (2008). Review and analysis of current Shariah-compliant equity screening practices. International Journal of Islamic and Middle Eastern Finance and Management, 1(4), 285-303.
    • Sadeghi, M. (2008). Financial performance of Shariah-compliant investment: evidence from Malaysian stock market. International Research Journal of Finance and Economics, 20, 15-26.

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Bachelortheses topics in the area of derivatives and risk management

  • Valut-at Risk - Backtesting

    Task:

    • The VaR measures the risk of investments and estimates the potential loss over a certain period. The estimate is based on different assumptions and methods such as the historical VaR, the parametric VaR and the Monte Carlo simulations.
    • While every method has its advantages and disadvantages the question arises which one to use for the companies. The evaluation of the VaR estimates is typically done by backtesting.
    • Empirical investigation and comparison of different VaR methods with the means of backtesting.

    Basic Literature:

    • Hull, J. C., (2011). Options, Futures, and Other Derivatives. 8th ed., Prentice Hall.
    • Christoffersen, P. F. (2012). Elements of financial risk management. 2nd ed., Academic Press. Jorion, P. (2001). Value at Risk. McGraw-Hill.

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  • Duration and Convexity Hedging

    Task:

    • Describe the concepts of Duration and Convexity.
    • Create a bond portfolio which is immunized against changes in the interest rate using the measures of Duration and Convexity.
    • Evaluate the performance of this method based on simulations or empirical analyses.

    Basic Literature:

    • Chambers, D.R., Carleton, W. T. & McEnally, R. W. (1988). Immunizing Default-Free Bond Portfolios with a Duration Vector. The Journal of Financial and Quantitative Analysis, 23(1), 89-104.
    • Bierwag, G.O., Fooladi, I. & Roberts, G. S. (1993). Designing an immunized portfolio: IS M-squared the key? Journal of Banking and Finance, 17(1), 1147-1170.

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  • Currency Carry Trades

    Task:

    • The currency carry factor is based on investment in high interest countries, while borrowing in low interest countries. The existence of the strategies could be traced to exploitable disparities in global macroeconomic conditions.
    • Display in and out of sample predictability of currency carry trading strategies

    Basic Literature:

    • Bakshi, G. and Panayotov, P. (2013). Predictability of currency carry trades and asset pricing implications. Journal of Financial Economics, 17(1), 139-163.

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  • Commodity Carry

    Task:

    • The Theory of normal backwardation states that the term structure of commodity futures Prices should determine the risk premium. A commodity carry strategy is taking advantage of this and invests in long run futures and sells short run futures. The concept of carry originated in the currency market and is transformed into multiple markets.
    • Calculate the returns of commodity carry strategies, following Koijen et al. (2018).

    Basic Literature:

    • Gorton, G. B., Hayashi, F., & Rouwenhorst, K. G. (2012). The fundamentals of commodity futures returns. Review of Finance, 17(1), 35-105.
    • Koijen, R. S.J., Moskowitz, T. J., Pedersen, L. H., & Vrugt, E. B. (2018). Carry. Journal of Financial Economics, 127(2), 197-225.

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  • Expectations Hypothesis Tests

    Task:

    • What determines the differences between long-term and short-term yields? According to the popular expectations hypothesis, the yield spread embeds important information on market expectations about future developments in interest rates.
    • Theoretical description of the expectations hypothesis for interest rates. Empirical evaluation of its validity.

    Basic Literature:

    • Campbell, J. Y., & Shiller, R. J. (1991). Yield spreads and interest rate movements: A bird's eye view. Review of Economic Studies, 58(3), 495-514.
    • Fama, E. F. (1984). The information in the term structure. Journal of Financial Economics, 13(4), 509-528.

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  • Volatility Modeling

    Task:

    • It is empirically shown that the volatility of financial asset returns changes over time. Volatility clustering describes the phenomenon of periods when volatility is exceptionally high interspersed with periods when volatility is usually low.
    • Volatility modeling plays an important role in risk management but also for the asset allocation and derivative pricing.
    • GARCH models introduced by Engle (1982) and Bollerslev (1986) are specifically designed to capture the volatility clustering of returns.
    • Empirical investigate models of the GARCH family and compare the forecasting performance.

    Basic Literature:

    • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 987-1007.
    • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
    • Alexander, C. (2008): Market risk analysis, Volume II: Practical financial econometrics.

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  • Intraday Volatility Pattern

    Task:

    • Theoretically, stock price variability should be ascribed to public news announcements since price changes reflect the arrival and processing of new information. But the overall volatility is not completely explained by news. The intraday pattern has high explanatory power as well.
    • Empirically investigate the intraday pattern of stock market volatility.

    Basic Literature:

    • Andersen, T. G., & Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4(2), 115-158.
    • Andersen, T. G., & Bollerslev, T. (1998). Deutsche mark–dollar volatility: Intraday activity patterns, macroeconomic announcements, and longer run dependencies. Journal of Finance, 53(1), 219-265.

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  • The Variance Risk Premium

    Task:

    • Typically, the price of a variance swap – the difference between the physical and the risk-neutral expectation of future variance – is negative for the stock market index. A negative price for the variance swap implies that investors are willing to pay a sizable risk premium in order to hedge against future increases in variance.
    • Theoretical description of the variance risk premium and discuss possible sources and interpretations
    • Empirical estimation of the variance risk premium.

    Basic Literature:

    • Carr, P., & Wu, L. (2009). Variance risk premiums. Review of Financial Studies, 22(3), 1311-1341.
    • Prokopczuk, M., & Wese Simen, C. (2014). Variance risk premia in commodity markets. Variance Risk Premia in Commodity Markets, Working Paper.

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  • Jumps in Financial Markets

    Task:

    • Stock returns are characterized by extreme observations, jumps which might arise for a number of different reasons, for example, extreme events, such as political upheaval.
    • Theoretical examination and comparison of non-parametric jump detection methods.
    • Empirical detection of jumps in multiple stock markets.

    Basic Literature:

    • Barndorff-Nielsen, O. E., & Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics, 4(1), 1-30.
    • Jiang, G. J., & Oomen, R. C. (2008). Testing for jumps when asset prices are observed with noise–a “swap variance” approach. Journal of Econometrics, 144(2), 352-370.
    • Lee, S. S., & Mykland, P. A. (2008). Jumps in financial markets: A new nonparametric test and jump dynamics. Review of Financial Studies, 21(6), 2535-2563.

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  • Tail Dependence

    Task:

    • Tail dependence refers to the dependence among extreme events of stock markets for example. This dependence might differ from dependence structures among ordinary observations.
    • Estimate the tail dependence for stock markets.

    Basic Literature:

    • Van Oordt, M. R., & Zhou, C. (2012). The simple econometrics of tail dependence. Economics Letters, 116(3), 371-373.
    • Hull, J. C., (2011). Options, Futures, and Other Derivatives. 8th ed., Prentice Hall.

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  • American Options and the Early Exercise Premium

    Task:

    • Unlike for European options, there is no close-form solution for the valuation of American options. But according to no arbitrage rules, the value of the American option has to be as least as high as the European counterpart. Barone-Adesi & Whaley (1987) decompose the American option into the European and an early exercise premium.
    • Review of the literature valuation methods for American options.
    • Empirical valuation of American options when taking the early exercise premium into account.

    Basic Literature:

    • Barone-Adesi, G., & Whaley, R. E. (1987). Efficient analytic approximation of American option values. Journal of Finance, 42(2), 301-320.

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MASTERTHESES

  • Which Asset Pricing Model for Commodities?

    Outline

    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.

    Literature

    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, 346-363.

  • Beta Estimation for Illiquid Stocks

    Outline

    Beta coefficients are very important for many applications, such as calculating risk-adjusted returns. They can be estimated using simple regressions methods. However, for infrequently traded stocks, such estimations might be severely biased. The literature has proposed a variety of methods to tackle this issue. The goal of this thesis is to compare the different methodologies.

    Literature

    Cohen K.J., Hawawini, G.A., Maier, S.F., Schwartz, R.A., and Whitcomb, D.K. (1983): Friction in the trading process and systematic risk. Journal of Financial Economics, 12, 263-278.

    Dimson, E. (1979): Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics, 7, 197-226.

    Fowler, D.J., Rorke, C.H. and Jog, V.M. (1989): A bias-correcting procedure for beta estimation in the presence of thin trading. Journal of Financial Research, 12, 23-32.

    Scholes, M. and Williams, J. (1977): Estimating betas from nonsynchronous data. Journal of Financial Economic, 5, 309-327.

  • Investing in Commodity Markets

    Outline

    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.

    Literature

    Daskalaki, C. And Skiadopoulos, G. (2011): Should investors include commodities in their portfolios after all? New evidence. Journal of Banking and Finance, 35, 2606-2626.

    Bessler, W. and Wolff, D. (2015): Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies. Journal of Banking and Finance, 60, 1-20.

  • Estimating Factor Pricing Models: OLS vs. GMM

    Outline

    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.

    Literature

    Cochrane (2005): Asset Pricing. Princeton University Press.

  • Terrorism and Asset Prices

    Outline

    Terrorists try to impose political and economic costs on governments. However, it is unclear whether they are able to affect stock prices. Review the literature on terrorism and the economy / asset markets and examine empirically how stock markets react to terrorist threats and attacks.

    Literature

    Abadie, A., & Gardeazabal, J. (2008). Terrorism and the world economy. European Economic Review, 52(1), 1-27.

    Arin, K. P., Ciferri, D., & Spagnolo, N. (2008). The price of terror: The effects of terrorism on stock market returns and volatility. Economics Letters, 101(3), 164-16.

    Chen, A. H., & Siems, T. F. (2004). The effects of terrorism on global capital markets. European Journal of Political Economy, 20(2), 349-366.

  • The Equity Premium Puzzle and Potential Solutions

    Outline

    Mehra and Prescott (1985) show that post-war equity returns in the U.S. are not consistent with the classical consumption-based model. Since then, several studies have tried to develop model extensions and alternatives to the consumption-based model to try to explain this puzzle. Review this literature and calibrate several of these extensions to German stock market and consumption data.

    Literature

    Bansal, R., & Yaron, A. (2004). Risks for the long run: A potential resolution of asset pricing puzzles. Journal of Finance, 59(4), 1481-1509.

    Campbell, J. Y., & Cochrane, J. H. (1999). By force of habit: A consumption-based explanation of aggregate stock market behavior. Journal of political Economy, 107(2), 205-251.

    Mehra, R., & Prescott, E. C. (1985). The equity premium: A puzzle. Journal of Monetary Economics, 15(2), 145-161.

  • Asset Pricing Factor Models in the German Stock Market

    Outline

    Recently, there have been various new developments of asset pricing factor models. Their common features are that they account for investment and profitability risk on top of the market and size factors in previous factor models.

    Review this literature and calculate and test these new asset pricing models for the German stock market.

    Literature

    Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.

    Hou, K., Xue, C., & Zhang, L. (2015). Digesting anomalies: An investment approach. Review of Financial Studies, 28(3), 650-705.

    Stambaugh, R. F., & Yuan, Y. (2016). Mispricing factors. Review of Financial Studies, 30(4), 1270-1315.

  • Idiosyncratic Volatility

    Outline

    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.

    Literature

    Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.

    Fama, E.F., French, K.R., 2015. A five-factor pricing model. Journal of Financial Economics, 116, 1-22.

    Fama, E.F., French, K.R., 2016. Dissecting anomalies with a five-factor model. Review of Financial Studies, 29, 69-103.

    Hou, K., Xue, C., Zhang, L., 2015. Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

  • Good Beta, Bad Beta

    Outline

    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.

    Literature

    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), 195-228.

    Campbell, J. Y., & Vuolteenaho, T. (2004). Bad beta, good beta. American Economic Review, 94(5), 1249-1275.

  • Fundamentals-based Priors for Beta Estimation

    Outline

    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.

    Literature

    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), 1072-1112.

  • Analysis of the International Coal Market

    Outline

    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

    Literature

    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, 47–65

    Li, R., Joyeux, R., and Ripple, R. D. (2010). International steam coal market integration. Energy Journal, 31, 181–202

    Papiez, M. and Smiech, S. (2013). Causality-in-mean and causality-in-variance within the international steam coal market. Energy Economics, 36, 594–604

    Papiez, M. and Smiech, S. (2015). Dynamic steam coal market integration: Evidence from rolling cointegration analysis. Energy Economics, 51, 510–520

  • Predicting Bond Returns with Machine Learning

    Outline

    Machine learning methods have recently gained attention in financial research. One particiular question is whether such methods can be succesfully employed to forecast bond returns. The objective of this topic is to implement various machine learning methods and analyze their performance for bond return predictability

    Literature

    Bianchi, B., Büchner, M, and Tamoni, A. (2018). Bond Risk Premia with Machine Learning, Working Paper

  • Machine Learning for Asset Pricing

    Outline

    Machine learning methods have recently gained attention in financial research. One of the key questions is whether such methods can succesfully be used in asset pricing. The objective of this project is to first review the most important machine learning techniques. These techniques should then be implemented in the context of asset pricing.

    Literature

    Gu, S., Kelly, B. and Xiu, D. (2018). Empirical Asset Pricing via Machine Learning, Working Paper.

  • Analysis of Covered Bond Markets

    Outline

    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).  

    Literature

    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, 19-30.

    Prokopczuk, M., Siewert, J. & Vonhoff, V. (2013): Credit risk in covered bonds. Journal of Empirical Finance, 21, 102-120.

  • Correlation Trading

    Outline

    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.

    Literature

    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.

  • Fractional Co-integration in Commodity Futures Markets

    Outline

    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.

    Literature

    Dolatabadi, S., Nielsen, M. & Xu, K. (2015): A fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets. Journal of Futures Markets, 35, 339-356.

    Lien, D. & Tse, Y. K. (1999). Fractional cointegration and futures hedging. Journal of Futures Markets, 19, 457–474.

  • Estimating Non-linear Term-structure Mode

    Outline

    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.

    Literature

    Babbs, S. & Nowman, K. (1999): Kalman filtering of generalized Vasicek term structure models. Journal of Financial and Quantitative Analysis, 34, 115-130.

    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, 963-973.

    Wan, E. & van der Merwe, R. (2000): The Unscented Kalman Filter for Nonlinear Estimation. Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC, 153-158.

  • Option Pricing Models

    Outline

    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.

    Literature

    Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 637-654.

    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), 327-343.

  • Option Pricing under Long Memory

    Outline

    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.

    Literature

    Christoffersen, P. & Jacobs, C. (2004): Which GARCH Model for Option Valuation? Management Science, 50, 1204-1221

    Fedotov, S. & Tan, A. (2005): Long memory stochastic volatility in option pricing. International Journal of Theoretical and Applied Finance, 8, 381-392.

    Taylor, S. (2014): Consequences for Option Pricing of a Long Memory in Volatility. In: Handbook of Financial Econometrics and Statistics, Chapter 32, 903-933

  • Valuation of Weather Derivatives

    Outline

    Weather derivatives are an interesting innovation in financial markets. For example, at the Chicago Mercantile Exchange temperature futures can be traded since 1999. 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.

    Literature

    Dorfleitner, G. & Wimmer, M. (2011): The pricing of temperature futures at the Chicago Mercantile Exchange. Journal of Banking & Finance, 34, 1360-1370

    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, 59-95

  • Wind Derivatives and the "Energiewende"

    Outline

    The amount of electricity generated through renewables sources, especially wind power, has dramatically increased over the last years in Germany. However, wind generation has the big problem of being uncontrolled as wind speed fluctuates over time. The political agenda called “Energiewende” subsidizes renewable sources, making traditional production facilities often unprofitable. Taken together, these developments could cause electricity shortages. Newly introduced wind power derivatives at the European Energy Exchange have the potential of mitigated such risks. The objective of this thesis is to first, carefully review the economic consequences of the “Energiewende”, study the aforementioned risk of blackouts, and analyze to what extend wind derivatives can provide a risk mangement solution.

    Literature

    Wind Power Futures at the EEX.


THESES ABROAD

MSc Thesis Abroad

The Institute for Financial Market Theory offers Master students from the Major Finance the opportunity to write their thesis at a partner institution abroad. The thesis is supervised by a colleague on campus. The following partnerships currently exist:

  • Norwegian University of Science and Technology, Trondheim (Norway)
  • Macquarie University, Sydney (Australia)

If you are interested, please send your application (incl. CV, excerpt of grades, short letter of motivation) to Prof. Dr. Marcel Prokopczuk (prokopczuk@fmt.uni-hannover.de). The application deadline for the summer semester 2020 is 15.8.2019.

There are no tuition fees. The partnership with NTNU is also supported by the Erasmus programme. For a stay in Australia there is the possibility to apply for a scholarship of the International Office of the LUH.


CONTACT FOR GENERAL QUESTIONS ABOUT YOUR THESIS

M.Sc. Christoph Würsig
Research Staff
Office hours
Di. 14:00 - 15:00 Uhr
Address
Königsworther Platz 1
30167 Hannover
Building
Room
033
M.Sc. Christoph Würsig
Research Staff
Office hours
Di. 14:00 - 15:00 Uhr
Address
Königsworther Platz 1
30167 Hannover
Building
Room
033