Topics – Master Theses 2010
I. Topics in Finance
1. Production-Based Factor Models of Asset Pricing Assistant: Philipp Rindler
Overview:
The Fama-French (1993) 3-Factor model has long been the workhorse asset pricing model. Similar to most other asset pricing models in general and factor models in particular, the Fama-French factors are motivated from the consumption side of the economy (Breeden, Gibbons, and Litzenberger, 1989). Based on Cochrane (1991), recent articles have motivated factor models from production theory. Particularly noteworthy are Liu, Whited, and Zhang (2009) who explore the return-characteristics link via structural estimation as well as Chen and Zhang (2010) who estimate a three-factor model based on q-theory.
The purpose of this thesis is to synthesize these new fields of research and provide a comparison of in-sample fit and out-of-sample forecasting performance.
References:
Breeden, Douglas T., Michael R. Gibbons, and Robert H. Litzenberger, 1989, Empirical Tests of the Consumption-oriented CAPM, Journal of Finance 44, 231–262.
Chen, Long and Zhang, Lu, 2010, A Better Three-Factor Model That Explains more Anomalies, Journal of Finance, forthcoming.
Cochrane, John H., 1991, Production-Based Asset pricing and the link between stock returns and economic fluctuations, Journal of Finance 46, 209–237.
Fama, Eugene F., and Kenneth R. French, 1993, Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics 33, 3–56.
Gibbons, Michael R., Stephen A. Ross, and Jay Shanken, 1989, A Test of the Efficiency of a given Portfolio, Econometrica 57, 1121–1152.
Liu, Laura Xiaolei, Toni M. Whited, and Lu Zhang, 2009, Investment-Based Expected Stock Returns, Journal of Political Economy, forthcoming.
2. When to fire an Active Portfolio Manager? A Guideline for the Hedge Fund Industry
Roland Füss Overview:
For investors allocating to active investment managers it is essential to eliminate their exposure to underperforming fund managers. While this is easy in the traditional investment field where investment managers can be easily assessed vis-a-vis their respective benchmarks, it is challenging with absolute return managers. One possibility could of be course to assess by the absolute return or even by hedge fund indexes, however, due to the high dispersion of single hedge fund returns versus hedge fund benchmarks this does not seem to be the right approach. In this thesis, various systematic ideas on “when to fire your active managers” should be explored and tested using a large sample of hedge fund return series. In particular, it should be tested if the process of bootstrapping the maximum drawdown of the respective single hedge funds leads to positive results. It should also be tested when the bootstrapped
maximum drawdown is outside the 99% confidence interval, if this would be a valid “exit signal”.
References:
Bradley Efron: Bootstrap Methods: Another Look at the Jackknife. In: The Annals of Statistics. 7, Nr. 1, 1979, S. 1–26.
B. Efron, R.J. Tibshirani: An introduction to the bootstrap, New York: Chapman & Hall, 1993 CCP Newsletter (Confidential)
3. A No-Arbitrage Square-Root Vectorautoregressive Model of the Term-Structure of Interest Rates using Macroeconomic and Latent Factors
Assistant: Philipp Rindler Overview:
No arbitrage modeling of the yield curve was first done by Vasicek (1978) building directly on the theory of Black and Scholes (1972). Later, Cox, Ingersoll, and Ross (1985) built a general equilibrium model and derived its implications for the yield curve. A final characterization of these so-called affine yield models was finally provided by Duffie and Kan (1996). On the macroeconomic side, Taylor (1993) first proposed the Vector Autoregressive (VAR) model of the term-structure to estimate the effects of monetary policy on interest rates. In a seminal paper, Ang and Piazzesi (2003) combined the two approaches and imposed no-arbitrage restrictions on a Taylor-type VAR model. Their analysis, however, was restricted to a Gaussian setup corresponding to a discrete multi-dimensional Vasicek model.
The purpose of this thesis is to extend their model and estimation to a square root CIR-type model.
References:
Ang, Andrew, and Monika Piazzesi, 2003, A No-Arbitrage Vector Autoregression of term structure dynamics with macroeconomic and latent variables, Journal of Monetary Economics, 50, 745-787.
Black, Fischer, and Myron Scholes, 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 81, 637-654.
Cox, John C., Jonathan E. Ingersoll, and Stephen A. Ross, 1985, A Theory of the Term Structure of Interest Rates, Econometrica, 53, 385–407.
Duffie, D., and R. Kan, 1996. A Yield-Factor Model of Interest Rates. Mathematical Finance 6, 379–406.
Taylor, J.B., 1993. Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy 39, 195–214.
Vasicek, O., 1977. An equilibrium Characterization of the Term Structure. Journal of Financial Economics, 5, 177–188.
4. A Comparison of Volatility Estimators: Realized Variance vs. Realized Kernels Assistant: Lu Zhao
Overview:
Realized variance is a widely-used volatility estimator in the current literature for high-frequency financial data analysis. However, it is evidenced that in the presence of market
friction RV is biased and inconsistent. In order to solve the problem, Zhou (1996) utilized the subsampling method and kernel-based estimator to alleviate the influence of market microstructure. Based on Zhou’s work, Hansen and Lunde developed the realized kernel as an unbiased and consistent estimator for volatility. In this paper, the realized volatility and realized kernels of German stock market need to be calculated and compared. Which is better to capture the true volatility in the empirical stock market?
References:
Andersen, T.G., and L. Benzoni, 2009, Realized Volatility, In T.G. Andersen, R.A. Davis, J.P. Kreiss, and T. Mikosch, eds., Handbook of Financial Time Series, Springer Verlag, 555–576.
Barndorff-Nielsen, O.E.,P.R. Hansen, A. Lunde, and N. Shephard, 2008, Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise, Econometrica 76, 1481–1536.
Zhou, B., 1996, High-frequency data and volatility in foreign exchange rates, Journal of Business & Economic Statistics 14(1), 45–52.
5. An Introduction and Valuation of Passport Options Assistant: Lu Zhao
Overview:
A passport option is an option on the profits or losses of a trading account. The option holder can execute a trading strategy which he chooses himself in the class of strategies specified in the contract. The pricing of passport options is a major issue since the holder can change the position many times which results in an optimisation problem. It can be calculated as a solution to a partial differential equation. In this paper, the development, the trading, and the pricing of passport options should be analysed.
References:
Andersen, L., J. Andreasen, and R. Brotherton-Ratcliffe, 1998, The Passport Option, Aarhus University, Department of Mathematics, Working Paper.
Hyer T., A. Lipton-Lifschitz, and D. Pugachevsky, 1997, Passport to Success, Risk Magazine 10, 127-131.
Nagayama, I., 1999, Pricing of Passport Option, J.Math.Sci.Univ.Tokyo 5, 747-785.
Shreve, S. and Vecer, J., 2000, Options on a Traded Account, Finance and Stochastics 4, 255-274
II. Topics in Political Economy of Financial Markets
6. How do Partisan Politics in Financial Markets matter in Germany? An Empirical Analysis of Relationships between Financial Market Performance (Stock Returns, Volatility) and Political Decisions of Different Governments
Assistant: Jana Lenz Overview:
There is a close relationship between partisan politics and the stock market performance. The German State represents a good opportunity to examine the influences between financial
market performance (stock returns, volatility) and the political decisions between left-leaning, right-leaning and grand coalition governments.
An application on recent data (i.e. the German federal election in 2009) is expected as well as interest in econometric methods and the use of econometric software.
References:
Füss, R. and Bechtel, M., 2008, Partisan Politics and Stock Market Performance: The Effect of Expected Government Partisanship on Stock Returns in the 2002 German federal election, Public Choice, 135(3-4), 131-150.
Bechtel, M., 2009, The Political Sources of Systematic Risk: Lessons from a Consensus Democracy, Journal of Politics, 71(2), 661-672.
Tsay, R. S., 2005, Analysis of Financial Time Series, Wiley.
Wooldridge, J., 2003, Introductory Econometrics. A Modern Approach, Ch. 13-14, Thomson South-Western.
III Topics in Applied Econometrics
7. Do Islamic Financial Banks and Products Perform better in Times of Crisis? Assistant: Jana Lenz
Overview:
Islamic financial products are based on the principles of the Sharia, the Islamic law. Under conditions such as the prohibition of interest, some Islamic banks and products have been very successful. At the beginning of the financial crisis it was sometimes maintained that Islamic banks are unaffected by the subprime mortgage crisis because they are immune due to inherent business ethics within Islamic banking. Is this true? Do Islamic banks perform better in times of crisis?
To analyze this question, a performance comparison of the Dow Jones Islamic Market (DJIM) Indexes with a benchmark is expected as well as interest in econometric methods and the use of econometric software.
References:
Aggarwal, R. K. and Yousef, T., 2000, Islamic Banks and Investment Financing, Journal of Money, Credit and Banking, 32(1), 93-120.
Chapra, U., 2009, The global financial crisis: can Islamic finance help?, http://www.newhorizon-islamicbanking.com/index.cfm?section=academicarticles&
action=view&id=10733.
El Qorchi, M., 2005, Islamic Finance Gears Up, Finance & Development, 42(4).
Wooldridge, J., 2003, Introductory Econometrics. A Modern Approach, Ch. 13-14, Thomson South-Western.
Tsay, R. S., 2005, Analysis of Financial Time Series, Wiley and Sons.
Zaher, T. S. and Hassan, M. K., 2001, A Comparative Literature Survey of Islamic Financeand Banking, Financial Markets, Institutions and Instruments, 10(4), 155-99.
8. What Determines the Size of Value-at-Risk Exceedances? A Tobit Regression Assistant: Zeno Adams
Overview:
Value-at-Risk (VaR) is a widely used risk measure among financial institutions because of its attractive property of expressing an asset’s risk in only one number. From the academic side VaR models have attracted much attention because of their challenging statistical properties. During the financial crisis the conventional VaR proved to be inappropriate as the number and size of VaR exceedances turned out to be much higher than predicted by the model. The purpose of this thesis is to identify the influencing factors of the size of VaR exceedances. Since the exceedances are bounded at zero, i.e. the distribution of the dependent variable is truncated, a Tobit model is appropriate. The student will be guided through most of the estimation procedure but is expected to provide interest in econometric methods and the use of econometric software.
References:
Füss, R., Adams, Z., and Kaiser, D.G., 2009, The Predictive Power of Value-at-Risk,Models in Commodity Futures Markets, Journal of Asset Management, forthcoming.
Jorion, P., 2007, Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed., McGraw-Hill.
Kleiber, C. and Zeileis, A., 2008, Applied Econometrics with R, Ch.5, Springer.
9. Equal Weights, Principal Component Analysis and the Representative Index Assistant: Zeno Adams
Overview:
For publicly traded assets it has become standard to use the market capitalization as weights for a representative index. Some assets, e.g. hedge funds, however are not publicly traded and an index that uses equal weights may not be truly representative. Principal Component Analysis (PCA) has been used in the past with the aim of creating an index that points in “the main direction of the data”. But even this method is susceptible to random noise in the data as well as the selection of the index constituents. The aim of this thesis is to create a representative index using PCA and confidence bands within which the index can be regarded as being truly representative. While the problem of constituent selection can be tackled with leaving-one-series-out procedures, the problem of random noise should be mitigated using an appropriate time-series bootstrap. The student will be guided through most of the estimation procedure but is expected to provide interest in multivariate statistics and the use of econometric software.
References:
Everitt, B.S., 2005, An R and S-Plus Companion to Multivariate Analysis, Springer. Härdle, W. and Simar, L., 2007, Applied Multivariate Statistical Analysis, Springer.
Vinod, H.D. and Lopez-de-Lacalle, J., 2009, Maximum Entropy Bootstrap for Time Series: The meboot R Package, Journal of Statistical Software, 29(5), 1-19.
10. The Long-Run Relation Between Macroeconomic Fundamentals and Asset Market Correlation
Assistant: Thorsten Glück Overview:
Macroeconomic fundamentals may serve as indicator variables for changing asset market volatility and correlation. The aim of this paper is the long-run analysis of this relation. First, the attributes of ‘realized correlation’ dynamics have to be analyzed in the frequency domain. In a second step the relation between fundamentals and realized correlation should be established.
References:
Schert, W., 1989, Why Does Stock Market Volatility Change Over Time?, Journal of Finance, 44, 115-1153.
Engle, R. and Rangel, J., 2004, The Spline-GARCH Model for Low Frequency Volatility and its Global Macroeconomic Causes. Working Paper.
Stoffer, D. and Shumway, R. (2006), Time Series Analysis and Its Applications with R Examples. Springer, Ch. 4.
III. Topics in Alternative Investments
11. Tail Properties of Commodity Futures Return Distributions Assistant: Thorsten Glück
Overview:
In contrast to stock returns, commodity futures returns tend to have a positive skewness. However, skewness varies widely within the class of commodity futures and may be biased by singular outlier events. However, a detailed analysis of the distribution determinants has not been done. To this aim a quantile regression might provide some insights. Furthermore, the tail properties have only been analyzed statically without focusing on the distribution dynamics within the business cycle. The thesis is aimed to fill this gap with a special focus on the distribution tail properties in a dynamic macroeconomic context.
References:
Gorton, G., Rouwenhorst, G., 2006, Facts and Fantasies about Commodities, Financial Analysts Journal, 62(2).
Kat, H and R. Oomen, 2007, What Every Investor Should Know About Commodities, Part I, Journal of Investment Management, 5(1).
Erb, C., and C. Harvey, 2005, The Tactical and Strategic Value of Commodity Futures, Financial Analysts Journal, 62(2).
Tsay, R. S., 2005, Analysis of Financial Time Series. Wiley.
12. The Added Value of Due Diligence in Investment Management: Theoretical and Empirical Evidence from Hedge Funds
Roland Füss Overview:
Total risk exposure of hedge funds is not only reflected in past returns. As a result of the lack of transparency, hedge fund investors face a definite level of uncertainty about the actual risk exposures and the quality of the hedge fund management. Because of the unusual risk profiles
and low transparency, investors are urged to analyze potential hedge fund investments by means of a due diligence.
Because it is argued that operational risk is unrewarded risk, due diligence or operational risk must be reflected in the alpha. Thus, the aim of this thesis is to estimate a factor model for hedge funds and then analyze alpha by separating manager’s skill from operational risk. In doing so, one should also compare the influence of operational risk between dead and live hedge funds.
References:
Brown, Stephen J., Thomas L. Fraser, and Bing Liang, 2008, Hedge Fund Due Dilligence: A Source of Alpha in a Hedge Fund Portfolio Strategy, Journal of Investment Management 6, 23-33.
Brown, Stephen J., William N. Goetzman, Bing Liang, and Christopher Schwarz, 2008, Mandatory disclosure and Operational Risk: Evidence from Hedge Fund Registration, Journal of Finance 63, 2785-2815.
Fontnouvelle, Patrick de, Virginia DeJesus-Rueff, John Jordan, and Eric Rosengren, 2003, Using Loss Data to Quantify Operational Risk, Working paper, Federal Reserve Bank of Boston.
Fontnouvelle, Patrick de, Virginia DeJesus-Rueff, John Jordan, and Eric Rosengren, 2006, Capital and Risk: New Evidence on Implications of Large Operational Losses, Journal of Money, Credit and Banking 38, 1819-1846.
IV. Topics in Cooperation with Union Investment
13. Performance Measures to judge the Quality of Asymmetric Investment Concepts Overview:
The aim of this thesis is to evaluate different key rations to judge the quality of asymmetric portfolios.
An asymmetric concept is characterized by a non-symmetric return distribution whereby a certain floor must not be infringed at the end of a pre-defined investment horizon (portfolio insurance concepts).
It is very likely that useful key figures take performance as well as risk into account. Sortino Ratio, MaR-Ratio (Calmar-Ratio) are subjects to be covered.
References:
Sortino, F., and L. Price, 1994, Performance measurement in a downside risk framework, The Journal of Investing 3(3), 59-65.
Young, Terry W. (01 October 1991), Calmar Ratio: A Smoother Tool, Futures.
14. Optimal Use of Risk Budgets in Asymmetric Portfolio Management Overview:
When managing risk controlled portfolios the calculation of risk budgets and the transformation of these risk budgets into an adequate asset allocation are the two central questions. While the calculation of risk budgets contains some academic questions the use of that risk budget in well diversified portfolios can be addressed in many different ways. Beginning with Markowitz’ Modern Portfolio Theory several approaches have been made to
find the most promising method to allocate limited risk budget. Black-Litterman approaches, Maximum Diversified Portfolios, Minimum Variance approaches, Stable Distributions, Sharpe Ratio Maximization, Equal Risk and Expected Utility Asset Allocations are key words in this respect.
The paper should give an in-depth overview of the existing research. Promising solutions should be identified and analyzed in a comparative manner.
References:
Black, Fischer, and Robert Littermann, 1992, Global Portfolio Optimization, Financial Analysts Journal 48(5), 28–43.
Sharpe, William F., 2007, Expected Utility Asset Allocation, Financial Analysts Journal 63(5), 18–30.
