Fields of Research:

My main research areas are the risk management for power utility companies, bank, and insurance companies, modeling of electricity markets, valuation and hedging of derivatives (interest-rate, credit- and energy-related), optimal portfolio allocation under frictions.

Projects:

  “Big risks”: perceptions, management and neuralgic societal risks in the 21st century (with Achim Goerres and Andreas Niederberger)

 This project is about the ways in which the public deals with neuralgic societal risks such as climate change, demographic change and state deficits in the 21st century (“big risks”). It aims to answer overarching questions from the three disciplinary perspectives of practical philosophy, political sociology and financial mathematics, all based at the interdisciplinary research cluster “Transformation of Contemporary Societies” at the University Duisburg-Essen.

Practical philosophy considers the epistemic difficulties of “knowing” risks and offers normative risk assessments and reactions to them. Political sociology studies the intersection between the political and the societal spheres and is equipped to deal with the effects of social and political positions on individual perceptions. Financial mathematics offers tools for the risk management of quantifiable risks and allows designing instruments for diversification and hedging of risks.

Whereas risk is a central concept in economics and business studies, its manifestations in a broader sense are rarely studied from a rigorous multi-disciplinary angle.

Analytics and Empirics of Intraday Trading of Electricity

(with Karsten Urban and Christoph Weber)

This project studies the empirics of electricity intraday markets using data on quarter-hour products. We will discuss the development of trading strategies and the construction of optimal portfolios for different market participants. We also aim to develop real-time trading strategies for practical applications. In addition, regulatory aspects for the generation of an efficient electricity markets will be investigated.

Model Risk in Energy Markets

While model risk has been studied in some detail in the context of financial mathematics model risk in the context of energy markets has been widely neglected. The aim of the project is to raise awareness of model risk and to provide tools for its quantification in energy markets. In particular, we consider the valuation of energy spread options which represent the financial alternative to investing in a (gas – or coal-fired) power plant. The valuation of such plants is important for the German market as they are regarded as bridging technology to provide capacity until electricity generated from renewable sources can be stored efficiently. We intend to apply our approach to other pricing question within the electricity market with a focus on short-term trading.

Structural Equilibrium Pricing Models

The aim of the project is the development and use of structural models for electricity prices, which will allow quantitative analysis for pricing and hedging of various electricity derivatives. We will also use the modeling approach to study the effect of market coupling on the prices of these derivatives.

Quantitative Climate Finance

Climate Change features a variety of uncertainties. Besides the physical implications, e.g. increased frequency and severity of storms, floods, draughts and extreme weather events, there are many economically relevant uncertainties in terms of political, social and regulatory reactions.

In particular, the quantification of climate risk in a probabilistic framework carries high uncertainties for probabilities of future developments (scenarios).

As a consequence, quantitative approaches are highly controversial in the academic and in particular in the public discussion. So far a systematic approach to the various degrees of uncertainty (ambiguity) is. We will provide a systematic classification of uncertainty for the discussion of the consequences of climate change and feed it in the discussion of the wider public.

Our focus will be the analysis of the consequences of the change of the world economy in the wake of climate change to aspects of financial markets. As during the climate summit 2015 in Paris far-reaching decisions towards a limitation of the global warming to the 2 degree Celsius have been taken, we will investigate the change towards a low-carbon world economy. So, we will investigate the consequences for financial institutions, investors and the regulation of financial and insurance markets.

A quantitative investigation needs a pricing of the economic costs of the carbon emissions to extend the standard pricing and risk management approaches. If such a pricing is done in the current literature typically CO2 permit prices are used and thus the price is too low by a significant margin. The basis of our investigation is therefore the construction of a carbon (-price) index, which will include a thorough treatment of the various aspects of uncertainty related to the modelling of climate change.  In doing so we use a decision-theoretic approach motivated from the asset pricing literature. In particular, it is necessary to use a realistic modelling of risk preferences as well as an explicit inclusion of the aversion towards ambiguity. Furthermore, in our analysis we separate risk and time preferences in the spirit of the approach of (Epstein-Zinn). 

As todays climate-policy decision will have long-term consequences, the above separation allows to appreciate the importance of the appropriate discount factor for the impact of these future consequences.

Our Index can be used to investigate the implication for capital markets and financial institutions of a more rigid climate policy. We will consider the valuation of companies on the capital markets, the analysis of companies towards their creditworthiness, and the structuring of carbon-friendly portfolios in asset allocation. In addition, we can quantify a carbon risk premium for companies, which can be used in terms of the portfolio management for equity as well as bond portfolios. Finally, we will be able to get a better view on the systemic risk that will be implied by a carbo-friendly revaluation of companies.

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