Prof. Dr. Rüdiger Kiesel

Chairholder

Prof. Dr. Rüdiger Kiesel

Room:
R11 T07 D39
Phone:
+49 201 18-34963
Fax:
+49 201 18-34974
Email:
Consultation Hour:
Nach Vereinbarung
Address:
Universitätsstr 2, 45141 Essen

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:

Filter:
  • Blasberg, A.; Hellmich, M.; Kiesel, R.: ESG Ratings and Credit Default Spreads, 2022. CitationDetails
  • 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.: Climate Default Swap – Disentangling the Exposure to Transition Risk Through CDS, 2021. Full textCitationDetails

    The substantial economic transformation required to mitigate and adapt to climate change will lower the value of certain businesses as well as some firms' assets in the not-too-distant future. Firms will need to transition to a less carbon-intensive business model, but may do so at different times and at different speeds, incurring different costs and risks in the process. We propose and implement a novel market-based measure of exposure to transition risk (transition risk factor) and examine how this risk affects firms' creditworthiness. We discipline the exercise by using Credit Default Swap (CDS) spreads to capture differential exposure to transition risk across economic sectors. We show that the transition risk factor is a relevant determinant of CDS spreads and provide evidence of the relationship between the differential exposure to transition risk and firms' cost of default protection. This effect is particularly pronounced during deteriorating credit market movements. However, effects vary substantially across industries, reflecting the fact that transition risk impacts firms' valuation differently depending on their sector. Our findings also suggest that investors seek greater protection against transition risks in the short– to medium-term, indicating an expectation of a swift transformation of the entire economic structure.

  • Kramer, A.; Kiesel, R.: Exogenous factors for order arrivals on the intraday electricity market. In: Energy Economics, Vol 97 (2021), p. 10518. doi:10.1016/j.eneco.2021.105186Full textCitationDetails
  • 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 textCitationDetails

    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 textCitationDetails

    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
  • R. Kiesel; Paraschiv, F.: Econometric analysis of 15-minute intraday electricity prices. In: Energy Economics, Vol 64 (2017), p. 77-90. Full textCitationDetails
  • Stahl, G.; J. Zheng, R. Kiesel; ̈Hlicke, R. Ru: 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
  • R. Kiesel, M. Mroz; ̈Ller, U. Stadtmu: Time-Varying Copula Models for Financial Time Series. In: Probability, Analysis and Number Theory, Vol 48 (2016), p. 159-180. Full textCitationDetails
  • 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
  • S. Ebbeler, F. E. Benth; 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
  • R. Kiesel, A. Rupp; Urban, K.: Valuation of structured financial products by adaptive multilevel. In: Al., S. Dalhlke Et. (Ed.): Extraction of Quantifiable Information from Complex Systems. Springer, Heidelberg 2014. CitationDetails
  • F. E. Benth, R. Kiesel; 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
  • R. Biegler-König, F. E. Benth; Kiesel, R.: Electricity Options and Additional Information, Working Paper. F. E. Benth, V. Kholodnyi; Laurence, P. (Ed.), Quantitative Energy Finance, Springer 2013. CitationDetails
  • R. Biegler-König, F. E. Benth; Kiesel, R.: An Empirical Study of the Information Premium on Electricity Markets, 36:55-77. Energy Economics, 2013. Full textCitationDetails
  • 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
  • D. Bauer, F. E. Benth; 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
  • J. Gernard, R. Kiesel; Stoll, S. - O: Valuation of Commodity-Based Swing Options. In: Journal of Energy Markets (2010) No 3, p. 91-112. Full textCitationDetails
  • N. H. Bingham, J. M. Fry; 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: R. Kiesel, M. Scherer; 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 textCitationDetails

    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
  • R. Kiesel, R. Börger; 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
  • F. E. Benth, A. Cartea; 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
  • R. Kiesel, L. Veraart: 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
  • R. Kiesel, S. Kassberger: 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
  • R. Kiesel, R. Schmidt: A survey of dependency modelling: Copulas, tail dependence and estimation. In: Perraudin, W. (Ed.): Structured Credit Products. RISK Book, 2005. CitationDetails
  • R. Kiesel, T. Kleinow: 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
  • R. Kiesel, H. Höfling; Löffler, G.: Understanding the Corporate Bond Yield Curve. In: The Pension Forum, Vol 15 (2004), p. 2-34. CitationDetails
  • R. Kiesel, S. Kassberger: F. Black und M.Scholes als Aktuare: Anwendungen der Optionspreistheorie in der Lebensversicherungsmathematik. In: Spremann, K. (Ed.): Versicherung im Umbruch. Springer, 2004. CitationDetails
  • R. Kiesel, W. Perraudin; 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
  • R. Kiesel, W. Perraudin; 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
  • R. Kiesel, Y. -T. Hu; 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
  • R. Kiesel: 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
  • R. Kiesel, T. Kleinow: Sensitivity analysis of credit portfolio models. In: in G. Stahl W. Härdle, T. Kleinow (Ed.): Applied Quantitative Finance. Springer, 2002. CitationDetails
  • R. Kiesel, U. Stadtmüller: 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
  • R. Kiesel, N. H. Bingham: 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
  • R. Kiesel, N. H. Bingham: Modelling asset returns with hyperbolic distributions. In: Knight, J.; Satchel, S. (Ed.): Asset return distributions. Butterworth-Heinemann, 2001, p. 1-20. CitationDetails
  • R. Kiesel, W. Perraudin; 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
  • (Ed.): Mathematical framework for integrating market and credit risk, . CitationDetails
  • Bannör, K.; Kiesel, R.; Nazarova, A.; Scherer, M.: Model Risk for Energy Markets. In: Energy Economics, Vol 59 , p. 423-434. doi:10.1016/j.eneco.2016.08.004CitationDetails

Courses:

Lehrveranstaltungen im WS

Tutored Theses:

Filter:
  • Hedging carbon risk (Bachelor Thesis Business Administration, in progress) Details

    Carbon risk exposure gains in importance for long-term investors. Due to climate change or climate policies stranded assets might diminish the return of portfolios. Therefore, hedging possibilities need to be assessed.

    See Andersson, M., Bolton, P., & Samama, F. (2016). Hedging climate risk. Financial Analysts Journal, 72(3), 13-32.

  • High-Frequency Trading auf Commodity-Märkten (Bachelor Thesis Business Administration) Details

    Diskussion der Anwendbarkeit der Modellierungsansätze auf Commodity-Märkten.

    Literatur:

    - Balancing energy strategies in electricity portfolio management. Möller Christoph; Rachev Svetlozar T.; Fabozzi, Frank J.. Energy Economics 33 (2011). S. 2–11.

    - Buy Low Sell High: a High Frequency Trading Perspective. Cartea, Alvaro; Jaimungal, Sebastian; Ricci, Jason. Mai, 2012. 

    Abrufbar unter: Social Science Research Network

  • Liquiditätsabhängige Markt-Risikomaße (Bachelor Thesis Business Administration) Details

    Diskussion liquiditätsadjustierter Risikomaße.

    Literatur:

    - Liquidity-adjusted Market Risk Measures with Stochastic Holding Period. Brigo, Damiano and Nordio, Claudio. October, 2010.

    Abrufbar unter:  Cornell University

  • Construction of hourly price forward curves in electricity markets (Bachelor Thesis Business Administration) Details

    Give an overview and compare different models for estimation of hourly price forward curves.  Price electricity Derivates based on those HPFCs.

    Literatur:

    - Pricing Electricity Derivatives on an Hourly Basis. Branger, Nicole, Reichmann, Oleg and Wobben, Magnus. 2009. 

    - Electric Load Forecasting - USING KERNEL-BASED MODELING FOR NONLINEAR SYSTEM IDENTIFICATION. ESPINOZA, MARCELO; SUYKENS, JOHAN A.K.; BELMANS, RONNIE and DE MOOR, BART.  10.1109/MCS.2007.904656. IEEE CONTROL SYSTEMS MAGAZINE 2007. S. 43-57

    - Constructing forward price curves in electricity markets. Fletena, Stein-Erik and Lemming, Jacob. Energy Economics 25 (2003). S. 409–424

  • Risikoprämien in Commodity Märkten (Bachelor Thesis Business Administration) Details

    <span style="color: rgb(25, 25, 25); font-family: Arial, Helvetica, sans-serif; font-size: 13px; line-height: 19.5px; text-align: justify; ">Diskussion von Modellen zur Ermittlung von Risikoprämien in Commodity Märken, insbesondere Elektrizitätsmärkten.</span>

    Literatur:

    - Variance risk premia in energy commodities. Trolle, Anders B. and Schwartz, Eduardo S.. 2009. 

    - Modelling the structure of long-term electricity forward prices at Nord Pool. Povh, Martin; Golob, Robert  and Fleten, Stein-Erik. 2009.

    - Time-varying risk aversion: An application to energy hedging. Cotter, John and Hanly, Jim. Energy Economics 32 (2010). S. 432–441.

    - Computing the market price of volatility risk in the energy commodity markets. Doran, James and Ronn, Ehud I.. Journal of Banking & Finance 32 (2008). S. 2541–2552.

    - Strategic Forward Contracting in the Wholesale Electricity Market. Holmberg, Pär. The Energy Journal, Vol. 32, No. 1. 2009. S. 169-202.

  • Liquidity modeling on financial markets (Bachelor Thesis Business Administration) Details

    Literaturübersicht hinsichtlich verschiedener Modelle.

  • Optimization of trading strategies for the secondary control reserve market (Bachelor Thesis Business Administration) Details

    Masterarbeit im Bereich "Finanzmathematik".

    Zur Sicherstellung der Frequenz im Stromsystem wird von den Stromnetzbetreibern sogenannte Sekundärregelleistung vermarktet In Forschungsarbeiten der Abteilung wurde ein finanzmathematisches Modell für diesen Markt entwickelt.

    Im Rahmen der Masterarbeit soll darauf aufbauend eine Methodik entwickelt werden, die die Gebotsabgabe für eine Teilnahme am Sekundärregelleistungsmarkt optimiert. Dabei sind verschiedene Zielfunktionen und ggf. deren Interaktionen zu untersuchen. Die Arbeit baut auf das o. g. stochastisches Modell zur Generierung von Preisszenarien auf. Das Optimierungsmodell soll anhand einer Überprüfung von Optimalitätskriterien validiert und ausführlich beschrieben werden. Flankiert wird der Kern der Arbeit von einer Beschreibung der wesentlichen Aspekte der Sekundärregelleistung und einer Darstellung des deutschen und österreichischen Regelleistungsmarktes. Alle Modelle und Methoden sind in Python oder R zu implementieren. Die Ergebnisse der Arbeit sollen für die Publikation in einer deutsch- oder englischsprachigen Fachzeitschrift geeignet aufbereitet werden.

    Spezifisches Profil der Abteilung
    Die Abteilung Finanzmathematik am Fraunhofer ITWM beschäftigt sich unter anderem mit der Modellierung und Simulation von Finanzmärkten und der Bewertung von Derivaten. Im Rahmen des Schwerpunktes „Finanzmathematik für die Energiewirtschaft“ werden aktuelle und zukunftsweisende finanzmathematische Themen mit Bezug zur Energiewirtschaft adressiert.

    Kontakt
    Dr. Andreas Wagner
    Abteilungsleiter Finanzmathematik/Fraunhofer ITWM
    Telefon: 0631 31600 4571
    E-Mail: andreas.wagner@itwm.fraunhofer.de

    Zur PDF

  • Comparison of order book data for the intraday electricity market from EPEX SPOT and Wattsight (Bachelor Thesis Business Administration) Details

    Am Lehrstuhl sind zwei vergleichbare Datensätze mit Auftragsbuchdaten für den gleichen Zeitraum verfügbar. Zunächst sollen die Datensätze miteinander verglichen werden. Im Anschluss können z.B. Auswirkungen der Unterschiede auf eine Handelsstrategie untersucht werden.

  • Valuation of EEX Location Spreads (Bachelor Thesis Business Administration) Details

    Masterarbeit im Bereich "Finanzmathematik".

    Im Rahmen der Masterarbeit soll eine Methodik entwickelt werden, die die
    Bewertung der Location Spreads an der Strombörse EEX ermöglicht. Hierfür ist
    ein Modell des europäischen Strommarktes aufzustellen, welches sowohl die
    Eigenschaften der einzelnen Länder, als auch deren Korrelation ausreichend
    berücksichtigt. Prinzipiell geeignet sind hierfür Fundamentalmodelle oder
    Faktormodelle. In beiden Fällen ist eine Analyse der zu modellierenden Märkte
    erforderlich, entweder mit dem Fokus auf die physischen Marktstruktur oder
    mit Hilfe statistischer Methoden wie der Hauptkomponentenanalyse. In einem
    nächsten Schritt muss das Modell an Marktdaten kalibriert werden um die
    Stabilität der Parameterschätzung zu beurteilen. In dem kalibrierten Modell
    erfolgt die Bewertung der Spread-Optionen mit Monte Carlo Methoden oder
    einer zuvor aus dem Modell abgeleiteten geschlossenen Formel sowie der
    Vergleich mit dem Standard-Ansatz von Margrabe.

    Flankiert wird der Kern der Arbeit von einer Beschreibung des europäischen
    Strommarktgefüges, einer statistischen Auswertung der relevanten Märkte
    sowie der Implementierung der entwickelten Modelle und Methoden in Matlab
    oder R. Die Ergebnisse der Arbeit sollen für die Publikation in einer
    Fachzeitschrift geeignet aufbereitet werden.

    Spezifisches Profil der Abteilung
    Die Abteilung Finanzmathematik am Fraunhofer ITWM beschäftigt sich unter
    anderem mit der Modellierung und Simulation von Finanzmärkten und der
    Bewertung von Derivaten. Im Rahmen des Schwerpunktes „Finanzmathematik
    für die Energiewirtschaft“ werden aktuelle und zukunftsweisende
    finanzmathematische Themen mit Bezug zur Energiewirtschaft adressiert.

    Kontakt
    Dr. Andreas Wagner
    Abteilungsleiter Finanzmathematik/Fraunhofer ITWM
    Telefon: 0631 31600 4571
    E-Mail: andreas.wagner (at) itwm.fraunhofer.de

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  • Continuous trading vs. continuous auction for the intraday electricity market (Bachelor Thesis Business Administration) Details

    Die Frage nach dem Marktdesign für den Intraday Strommarkt wird schon länger diskutiert, siehe z.B. https://www.diw.de/documents/publikationen/73/diw_01.c.525734.de/dp1544.pdf

    Ende 2018 hat die Deutsche Börse eine kontinuierliche Auktion vorgeschlagen, siehe https://www.deutsche-boerse.com/resource/blob/1458710/717470c265afd9428f43a60cd5e27791/data/7markets-m7-proposal-power-market-model_de.pdf

    Das Konzept soll mit dem des kontinuierlichen Handels verglichen werden. Im Anschluss soll versucht werden, mit vorliegenden Auftragsbuchdaten für den Intraday Strommarkt eine kontinuierliche Auktion zu simulieren. Nach Möglichkeit sollen die Marktergebnisse miteinander verglichen werden.