Curriculum Vitae:

Rüdiger Kiesel heads the chair for “Energy Trading and Financial Services” and is member of the board of the “House of Energy, Climate  and Finance” at the University Duisburg-Essen. Previously he has been Director of the Institute for Mathematical Finance at the University of Ulm. He also held positions for actuarial science and financial mathematics at Birkbeck College, University of London, the London School of Economics (full-time and visiting), and the Department of Mathematics at the University of Oslo (as a Visiting Professor). 

Rüdiger Kiesel is Co-author of the monographs “Carbon Finance”, and “Risk-Neutral Valuation” and has written more than seventy published research papers. He is a frequent speaker at international conferences and organized several conferences, summer schools, and practitioner seminars. Professor Kiesel also consults financial institutions, utilities and regulators on (carbon, credit- and energy-) risk management, derivative pricing models and asset allocation.

Fields of Research:

Climate Finance. The risks arising from climate change are uniquely global, uniquely long-term, uniquely irreversible, and uniquely uncertain. Because of their complex structure and as their consequences are beyond financial losses climate risks are difficult to price. Clearly, risk that is not quantified adequately is difficult to manage effectively. 

Thus, we develop approaches to improve the analysis of climate risks within the usual risk categories (credit, market, etc.). Acknowledging the special character of climate risks,  we investigate measures to increase the resilience of companies and suggest ways to implement risk-oriented dialogues on transition plans. Also, new financial products and structures to help smooth the decarbonisation path are designed and evaluated.

Risk management in the face of climate change. 

Climate change is a source of systemic risk for financial institutions and financial markets as physical damages due to natural disasters or disruptive events due to a rapid transition can disturb key economic activities. In particular, the so-called climate insurance protection gap has to be addressed by appropriate insurance policies, alternative risk transfer or public private partnerships.

We use risks related to climate change as an archetype of systemic risks in order to highlight challenges in the current risk management process.  The non-linear feedback structure of complex systems shows the need for agile thinking in interactions in a high-dimensional world.

Publications:

• Blasberg, A.; Kiesel, R.: Climate Risk in Structural Credit Models. In: Benth, F. E.; Veraart, A. E. D. (Ed.): Quantitative Energy Finance: Recent Trends and Developments. Springer, 2023, p. 247-267. doi:10.1007/978-3-031-50597-3_7Full textCitationAbstractDetails

  This survey article reviews the current state of literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. We discuss how the two prominent types of climate risk, physical and transition risk, are captured by the seminal Merton model and its well-known extensions. Theoretical and practical advantages and drawbacks are worked out and an outlook on possible model improvements is provided.

• Kremer, M.; Kiesel, R.; Paraschiv, F.: An econometric model for intraday electricity trading. In: Philosophical Transactions of the Royal Society A, Vol 379 (2021) No 2202. doi:10.1098/rsta.2019.0624Full textCitationDetails
• Blasberg, A.; Kiesel, R.; Taschini, L.: Carbon Default Swap – Disentangling the Exposure to Carbon Risk Through CDS, 2021. Full textCitationAbstractDetails

  Using Credit Default Swap spreads, we construct a forward-looking, market-implied carbon risk factor and show that carbon risk affects firms’ credit spread. The effect is larger for European than North American firms and varies substantially across industries, suggesting the market recognises where and which sectors are better positioned for a transition to a low-carbon economy. Moreover, lenders demand more credit protection for those borrowers perceived to be more exposed to carbon risk when market-wide concern about climate change risk is elevated. Finally, lenders expect that adjustments in carbon regulations in Europe will cause relatively larger policy-related costs in the near future.

• Kramer, A.; Kiesel, R.: Exogenous factors for order arrivals on the intraday electricity market. In: Energy Economics, Vol 97 (2021) No 105186, p. 1-14. doi:10.1016/j.eneco.2021.105186Full textCitationAbstractDetails

  We examine if the trading activity on the German intraday electricity market is linked to fundamental as well as market-induced factors. Thus, we propose a novel point process model in which the intensity process of order arrivals consists of a self-exciting term and additional exogenous factors, such as the production of renewable en- ergy or the activated volume on the balancing market. The model parameters are estimated by a maximum like- lihood approach that explicitly accounts for such factor processes. By comparing the proposed model to several nested models, we investigate whether adding the exogenous factors significantly increases the accuracy of the model fit. We find that intensity processes that only take into account exogenous factors are improved if we add a self-exciting term. On the other hand, to capture the market dynamics correctly, pure self-exciting models need to be extended such that they additionally account for exogenous impacts.

• Graf von Luckner, N.; Kiesel, R.: Modeling Market Order Arrivals on the Intraday Market for Electricity Deliveries in Germany with the Hawkes Process. In: Journal of Risk and Financial Management, Vol 14 (2021) No 4. doi:10.3390/jrfm14040161Full textCitationAbstractDetails

  We use point processes to analyze market order arrivals on the intraday market for hourly electricity deliveries in Germany in the second quarter of 2015. As we distinguish between buys and sells, we work in a multivariate setting. We model the arrivals with a Hawkes process whose baseline intensity comprises either only an exponentially increasing component or a constant in addition to the exponentially increasing component, and whose excitation decays exponentially. Our goodness-of-fit tests indicate that the models where the intensity of each market order type is excited at least by events of the same type are the most promising ones. Based on the Akaike information criterion, the model without a constant in the baseline intensity and only self-excitation is selected in almost 50% of the cases on both market sides. The typical jump size of intensities in case of the arrival of a market order of the same type is quite large, yet rather short lived. Diurnal patterns in the parameters of the baseline intensity and the branching ratio of self-excitation are observable. Contemporaneous relationships between different parameters such as the jump size and decay rate of self and cross-excitation are found.

• Kremer, M.; Kiesel, R.; Paraschiv, F.: Intraday electricity pricing of night contracts. In: Energies, Vol 13 (2020) No 17, p. 4501. doi:10.3390/en13174501Full textCitationDetails
• Kremer, M.; Benth, F. E.; Felten, B.; Kiesel, R.: Volatility and liquidity on high-frequency electricity futures markets: Empirical analysis and stochastic modeling. In: International Journal of Theoretical and Applied Finance, Vol 23 (2020) No 4. doi:10.1142/S0219024920500272Full textCitationDetails
• Glas, S.; Kiesel, R.; Kolkmann, S.; Kremer, M.; Graf von Luckner, N.; Ostmeier, L.; Urban, K.; Weber, C.: Intraday renewable electricity trading: Advanced modeling and numerical optimal control. In: Journal of Mathematics in Industry, Vol 10 (2020) No 3, p. 1-17. doi:10.1186/s13362-020-0071-xFull textCitationDetails
• Blasberg, A.; Graf von Luckner, N.; Kiesel, R.: Modeling the Serial Structure of the Hawkes Process Parameters for Market Order Arrivals on the German Intraday Power Market. In: 16th International Conference on the European Energy Market (EEM) (2019), p. 1-6. doi:10.1109/EEM.2019.8916326Full textCitationAbstractDetails

  Existing research indicates that on the intraday market for power deliveries in Germany market orders tend to arrive in clusters. To capture such clustering, point processes with an intensity depending on past events, so-called Hawkes processes, appear to be promising. We consider the question whether there is a temporal structure prevalent in the parameters of Hawkes processes estimated for adjacent delivery hours. First we model a diurnal seasonality pattern found in the data and provide an economic intepretation for it. For the remaining decomposed series, we then propose simple (vector) autoregressive models to describe the serial structure. To evaluate our model we conduct a forecasting study. Testing against a benchmark model and a model without any serial structure, we find evidence for our proposed model. Our study reveals that capturing the serial structure in the parameters proves to be useful in understanding the underlying market microstructure.

• Glas, S.; Kiesel, R.; Kolkmann, S.; Kremer, M.; Graf von Luckner, N.; Ostmeier, L.; Urban, K.; Weber, C.: Intraday renewable electricity trading: Advanced modeling and optimal control. In: Faragó, I.; Izsák, F.; Simon, P. (Ed.): Progress in Industrial Mathematics at ECMI 2018. Mathematics in Industry, vol 30. Springer, Cham, 2019, p. 469-475. doi:10.1007/978-3-030-27550-1_59Full textCitationDetails
• Kiesel, R.; Paraschiv, F.: Econometric analysis of 15-minute intraday electricity prices. In: Energy Economics, Vol 64 (2017), p. 77-90. Full textCitationDetails
• Kiesel, R.; Rühlicke, R.; Stahl, G.; Zheng, J.: The Wasserstein Metric and Robustness in Risk Management. In: Risks, Vol 4 (2016) No 32. doi:10.3390/risks4030032CitationDetails
• Kiesel, R.; Rahe, F.: Option pricing under time-varying risk aversion with applications to risk forecasting. In: Journal of Banking and Finance, Vol 76 (2016) No 3, p. 120-138. Full textCitationDetails
• Kiesel, R.; Mroz, M.; Stadtmüller, U.: Time-Varying Copula Models for Financial Time Series. In: Probability, Analysis and Number Theory, Vol 48 (2016), p. 159-180. doi:10.13140/RG.2.1.4894.5368CitationDetails
• Kiesel, R.; Kustermann, M.: Structural Models for Coupled Electricity Markets. In: Journal of Commodity Markets, Vol 3 (2016) No 1, p. 1638. Full textCitationDetails
• Harms, C.; Kiesel, R.: Application of electricity bid stack models for dynamic hedging purposes. In: Journal of Energy Markets, Vol 10 (2015) No 1, p. 1-29. CitationDetails
• Kiesel, R.; Ya, Wen: Modelling the market price of risk for emission allowance certificates. In: Nunno, G. Di; Benth, F. E. (Ed.): Stochastics of environmental and financial economics. Springer Proceedings in Mathematics & Statistics, 2015. CitationDetails
• Ebbeler, S.; Benth, F. E.; Kiesel, R.: Indifference Pricing of Weather Derivatives based on Electricity Futures. In: Prokopczuk, M. (Ed.): Energy Pricing Models: Recent Advances, Methods, and Tools. Palgrave Macmillan, New York 2014. CitationDetails
• Kiesel, R.; Rupp, A.; Urban, K.: Valuation of structured financial products by adaptive multilevel. In: Dalhlke, S.; Dahmen, W.; Giebel, M.; Hackbusch, W.; Ritter, K.; Schneider, R.; Schwab, C.; Yserentant, H. (Ed.): Extraction of Quantifiable Information from Complex Systems. Springer, Heidelberg 2014, p. 321-345. doi:10.1007/978-3-319-08159-5_16CitationDetails
• Benth, F. E; Kiesel, R.; Nazarova, A.: A critical empirical study of three electricity spot price models. In: Energy Economics journal, Vol 34 (2013) No 5, p. 1589-1616. doi:10.1016/j.eneco.2011.11.012Full textCitationDetails
• Bannör, K.; Kiesel, R.; Nazarova, A.; Scherer, M.: Model Risk for Energy Markets. In: Energy Economics, Vol 59 (2013), p. 423-434. doi:10.1016/j.eneco.2016.08.004CitationDetails
• Biegler-König, R.; Benth, F. E.; Kiesel, R.: Electricity Options and Additional Information, Working Paper. F. E. Benth, V. Kholodnyi; Laurence, P. (Ed.), Quantitative Energy Finance, Springer 2013. CitationAbstractDetails

  Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114177

• Biegler-König, R.; Benth, F. E.; Kiesel, R.: An Empirical Study of the Information Premium on Electricity Markets, 36:55-77. Energy Economics, 2013. Full textCitationAbstractDetails

  Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114196

• Kiesel, R.; Metka, K.: A Multivariate Commodity Analysis with Time-Dependent Volatility - Evidence from the German Energy Market. In: Zeitschrift für Energiewirtschaft, Vol 37 (2013) No 2, p. 107-126. doi:10.1007/s12398-012-0102-4Full textCitationDetails
• Grüll, G.; Kiesel, R.: Quantifying the CO2 Permit Price Sensitivity. In: Zeitschrift für Energiewirtschaft, Vol 36 (2012) No 2, p. 101-111. doi:10.1007/s12398-012-0082-4Full textCitationDetails
• Bauer, D.; Benth, F. E.; Kiesel, R.: Modelling the forward surface of mortality. In: SIAM Journal on Financial Mathematics, Vol 3 (2012) No 1, p. 639-666. doi:10.1137/100818261Full textCitationDetails
• Kiesel, R.: Martingales. In: Lovric, M. (Ed.): International Encyclopedia of Statistical Science. Springer, 2011, p. 779-781. CitationDetails
• Gernard, J.; Kiesel, R.; Stoll, S. - O: Valuation of Commodity-Based Swing Options. In: Journal of Energy Markets (2010) No 3, p. 91-112. Full textCitationDetails
• Bingham, N. H.; Fry, J. M.; Kiesel, R.: Multivariate elliptical processes. In: Statistica Neerlandica (2010) No 64 (3), p. 352-366. Full textCitationDetails
• Kiesel, R.; Scherer, P.: The Freight Market and its Derivatives. In: Kiesel, R.; Scherer, M.; Zagst, Rudi (Ed.): Alternative Assets and Strategies. World Scientific, 2010, p. 71-90. CitationDetails
• Kiesel, R.; Scherer, M.: Structural default risk models. In: Encyclopedia of Quantitative Finance. John Wiley & Sons, Ltd. All , 2010. CitationDetails
• Kiesel, R.; Lutz, M.: Efficient pricing of CMS spread options in a stochastic volatility LMM. In: Journal of Computational Finance, Vol 14 (2010) No 3, p. 37-72. Full textCitationAbstractDetails

  Working Paper available at:

  papers.ssrn.com/sol3/papers.cfm

• D. Bauer, D. Bergmann; Kiesel, R.: On the risk-neutral valuation of life insurance contracts with numerical methods in view. In: Astin Bulletin (2010) No 40, p. 65-95. Full textCitationDetails
• Kiesel, R.; Börger, R.; Schindlmayr, G.: A two-factor model for the electricity forward market. In: Quantitative Finance, Vol 9 (2009) No 3, p. 279-287. Full textCitationDetails
• Börger, R.; Cartea, A.; Kiesel, R.; Schindelmayer, G.: A multivariate commodity analysis and applications to risk management. In: Journal of Future Markets (2009) No 29 (3), p. 197-217. Full textCitationDetails
• Benth, F. E.; Cartea, A.; Kiesel, R.: Pricing forward contracts in power markets by the certainty equivalence principle: Explaining the sign of the market risk premium. In: Journal of Banking and Finance, Vol 32 (2008) No 10, p. 2006-2021. doi:10.1016/j.jbankfin.2007.12.022Full textCitationDetails
• Kiesel, R.; Veraart, L.: Asset-based Estimates for Default Probabilities for Commercial Banks. In: Journal of Credit Risk, Vol 4 (2008) No 2. Full textCitationDetails
• Kiesel, R.; Liebmann, T.; Kassberger, S.: Fair valuation of insurance contracts under Lévy process specifications. In: Insurance: Mathematics and Economics, Vol 42 (2007) No 1, p. 419-433. Full textCitationDetails
• Kiesel, R.; Bauer, D.; Kling, A.; Ruß, J.: Risk neutral valuation of with profit life insurance contracts. In: Insurance: Mathematics and Economics, Vol 39 (2006), p. 171-183. Full textCitationDetails
• Kiesel, R.; Kassberger, S.: A fully parametric approach to return modelling and risk management for hedge funds. In: Financial Markets and Portfolio Management, Vol 4 (2006), p. 472-491. Full textCitationDetails
• (Ed.): Mathematical framework for integrating market and credit risk, 2006. CitationDetails
• Kiesel, R.; Schmidt, R.: A survey of dependency modelling: Copulas, tail dependence and estimation. In: Perraudin, W. (Ed.): Structured Credit Products. RISK Book, 2005. CitationDetails
• Kiesel, R.; Kleinow, T.: Fair Value-basierende Optionspreisbewertung, R. Heyd, H. Bieg (Ed.), Vahlen, 2005. CitationDetails
• Kiesel, R.; Lesko, M.; Prestele, C.: Modellierung von Abhängigkeiten bei der Bewertung von Verbriefungen. In: Braun, H.; Gruber, J.; Gruber, W. (Ed.): Praktiker-Handbuch – Asset-Backed-Securities und Kreditderivate. Schäffer-Poeschel Verlag, Stuttgart 2005. CitationDetails
• Börger, R.; Kiesel, R.: Finanzmathematische Modelle für Strompreise. In: emw (2004) No 6. CitationDetails
• Kiesel, R.; Höfling, H.; Löffler, G.: Understanding the Corporate Bond Yield Curve. In: The Pension Forum, Vol 15 (2004), p. 2-34. CitationDetails
• Kiesel, R.; Kassberger, S.: F. Black und M.Scholes als Aktuare: Anwendungen der Optionspreistheorie in der Lebensversicherungsmathematik. In: Spremann, K. (Ed.): Versicherung im Umbruch. Springer, 2004. CitationDetails
• Kiesel, R.; Perraudin, W.; Taylor, A.: An extremes analysis of VaRs for emerging market benchmark bonds. In: Al., G. Bol Et (Ed.): Credit Risk: Measurement, Evaluation and Management. Physica-Verlag, 2004. CitationDetails
• Kiesel, R.; Bingham, N. H.; Schmidt, R.: A semi-parametric approach to risk management. In: Quantitative Finance, Vol 3 (2003), p. 426-441. Full textCitationDetails
• Kiesel, R.; Perraudin, W.; Taylor, A.: The structure of credit risk: Spread volatility and ratings transitions. In: Journal of Risk, Vol 6 (2003), p. 1-27. CitationDetails
• Bingham, N. H.; Kiesel, R.: Semi-parametric modelling in finance: theoretical foundations. In: Quantitative Finance, Vol 2 (2002), p. 241-250. Full textCitationDetails
• Kiesel, R.; Hu, Y. - T; Perraudin, W.: Estimation of transition matrices for sovereign credit risk. In: Journal of Banking and Finance, Vol 26 (2002) No 7, p. 1383-1406. Full textCitationDetails
• Kiesel, R.: Nonparametric statistical methods and the pricing of derivative securities. In: Journal of Applied Mathematics & Decision Sciences, Vol 6 (2002) No 1, p. 1-22. Full textCitationDetails
• Kiesel, R.; Kleinow, T.: Sensitivity analysis of credit portfolio models. In: in G. Stahl W. Härdle, T. Kleinow (Ed.): Applied Quantitative Finance. Springer, 2002. CitationDetails
• Kiesel, R.; Stadtmüller, U.: Dimensions of credit risk - Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V. In: M. Schwaiger, O. Opitz (Ed.): Exploratory Data Analysis in Empirical Research. Springer, 2002. CitationDetails
• Kiesel, R.; Bingham, N. H.: Modelling asset returns with hyperbolic distributions. In: Knight, J.; Satchel, S. (Ed.): Asset return distributions. Butterworth-Heinemann, 2001, p. 1-20. CitationDetails
• Kiesel, R.; Bingham, N. H.: Hyperbolic and semi-parametric models in finance. In: Sollich, P.; Coolen, A. C. C.; Houghston, L. P.; ; Streater, R. F. (Ed.): Disordered and Complex Systems. 2001. CitationDetails
• Kiesel, R.; Perraudin, W.; Taylor, A.: Estimating volatility for long holding periods. In: Measuring Risk in Complex Systems, eds. W.Härdle,J.Franke,G.Stahl, Springer (2000), p. 19-30. CitationDetails
• Kiesel, R.; Schmid, B.; Risklab, Germany: Aspekte der stochastischen Modellierung von Ausfallwahrscheinlichkeiten in Kreditportfoliomodellen. In: Kreditrisikomanagement, ed.K.Oehler, Schäffer-Poeschel Verlag (2000), p. 51-83. Full textCitationDetails

Courses:

Lehrveranstaltungen im WS