Dipl.-Math. Julian Thimme
Lehrstuhl für Derivate und Financial Engineering
- 1985: Geboren in Bielefeld
- 2004: Abitur am Gymnasium Elisabethschule, Marburg
- 2004-2005: Wehrersatzdienst an der Deutschen Blindenstudienanstalt, Marburg
- 2005-2010: Studium der Mathematik an der Westfälischen Wilhelms-Universität Münster, Schwerpunkte: Algebraische Geometrie und Algebraische Zahlentheorie
- Seit November 2010: Wissenschaftlicher Mitarbeiter am Lehrstuhl für Derivate und Financial Engineering
- Asset Pricing
- Decision Making under Ambiguity
- Thimme, J. (2014): Intertemporal Substitution in Consumption: A Literature Review, Working Paper. (Abstract)
This paper reviews the status quo of the empirical literature about the elasticity of intertemporal substitution (EIS) in consumption. Aiming to answer the question what the true magnitude of the parameter really is, it discusses several recent advances of the theory and highlights obstacles for the estimation. Although the general discussion still seems to be prevailed by Hall's early EIS estimates close to zero, we show that several deviations from the time additive rational expectations model speak in favor of considerably higher values. Our treatment is supposed to provide researchers a hint at which parameter is a reasonable and incontrovertible choice for the calibration of models in macroeconomics and finance.
- Branger, N., Thimme, J. (2014): Ambiguous Long Run Risks, Working Paper. (Abstract)
We present an extension of the long run risks model of Bansal and Yaron (2004) that allows for ambiguity about trend consumption growth and volatility. We use survey data to construct proxies of the model's state variables which allows a direct estimation and calibration of the model. Ambiguity about the level of future risk predicts excess returns and explains time-variation in the variance risk premium. Our model explains these stylized facts and highlights the importance of separating aggregate uncertainty into its parts.
- Branger, N., Konermann, P., Thimme, J. (2013): Returns on Cyclical and Defensive Stocks in Times of Scarce Information about the Business Cycle, Working Paper, March 2013. (available at SSRN)
- Thimme, J., Völkert, C. (2011): High Order Smooth Ambiguity Preferences and Asset Prices, Working Paper. (available at SSRN) (Abstract)
This paper extends the recursive smooth ambiguity decision model developed in Klibanoff, Marinacci, and Mukerji (2005, 2009) by relaxing the uniformity imposed on higher order acts. This generalization permits a separation of intertemporal substitution, risk aversion, and ambiguity aversion towards different sources of uncertainty. Our decision model is suited in situations where subjects may treat several kinds of uncertainty in different manners. We apply our preference specification to a consumption-based asset pricing model with long-run risks and assess the impact of ambiguity on asset prices and predictability patterns. We find that modeling attitudes towards uncertainty through high order smooth ambiguity preferences has important implications for asset prices. Our model significantly improves upon the special cases of Epstein and Zin (1989) utility and standard smooth ambiguity preferences.
- Thimme, J., Völkert, C. (2015): Ambiguity in the Cross-Section of Expected Returns: An Empirical Assessment, Journal of Business and Economic Statistics, Vol. 33, Issue 3, July 2015, 418-429. (Abstract)
This article estimates and tests the smooth ambiguity model of Klibanoff, Marinacci, and Mukerji based on stock market data. We introduce a novel methodology to estimate the conditional expectation, which characterizes the impact of a decision maker’s ambiguity attitude on asset prices. Our point estimates of the ambiguity parameter are between 25 and 60, whereas our risk aversion estimates are considerably lower. The substantial difference indicates that market participants are ambiguity averse. Furthermore, we evaluate if ambiguity aversion helps explaining the cross-section of expected returns. Compared with Epstein and Zin preferences, we find that incorporating ambiguity into the decision model improves the fit to the data while keeping relative risk aversion at more reasonable levels. Supplementary materials for this article are available online.