Table of contents

  1. Front Matter
    Pages i-xi
  2. Markets, Regulation, and Model Risk

    1. Front Matter
      Pages 1-1
    2. Damiano Brigo, Claudio Nordio
      Pages 3-18 Open Access
    3. Nils Detering, Natalie Packham
      Pages 39-56 Open Access
  3. Financial Engineering

    1. Front Matter
      Pages 57-57
    2. Florence Guillaume, Wim Schoutens
      Pages 59-74 Open Access
    3. Karl Friedrich Bannör, Matthias Scherer, Thorsten Schulz
      Pages 93-107 Open Access
    4. Jan Müller, Guido Hirsch, Alfred Müller
      Pages 109-128 Open Access
    5. Matthias Fischer, Kevin Jakob
      Pages 129-145 Open Access
    6. Stephan Höcht, Matthias Kunze, Matthias Scherer
      Pages 147-162 Open Access
    7. Matthew Ames, Gareth W. Peters, Guillaume Bagnarosa, Ioannis Kosmidis
      Pages 163-181 Open Access
  4. Insurance Risk and Asset Management

    1. Front Matter
      Pages 183-183
    2. Carole Bernard, Anne MacKay
      Pages 209-223 Open Access
    3. Thomas Dangl, Otto Randl, Josef Zechner
      Pages 239-266 Open Access
    4. Jan Natolski, Ralf Werner
      Pages 289-301 Open Access
  5. Computational Methods for Risk Management

    1. Front Matter
      Pages 303-303
    2. Rüdiger U. Seydel
      Pages 305-316 Open Access
    3. D. L. McLeish, Zhongxian Men
      Pages 317-335 Open Access
    4. German Bernhart, Jan-Frederik Mai
      Pages 337-345 Open Access
    5. Antonis Papapantoleon
      Pages 347-354 Open Access
  6. Dependence Modelling

    1. Front Matter
      Pages 355-355
    2. Christian Hering, Marius Hofert
      Pages 357-373 Open Access
    3. Raphael Hauser, Sergey Shahverdyan, Paul Embrechts
      Pages 375-392 Open Access
    4. Wolfgang Trutschnig, Juan Fernández Sánchez
      Pages 393-409 Open Access
    5. Jayme Pinto, Nikolai Kolev
      Pages 411-421 Open Access
    6. Dana Uhlig, Roman Unger
      Pages 423-438 Open Access

About these proceedings


Quantitative models are omnipresent –but often controversially discussed– in todays risk management practice. New regulations, innovative financial products, and advances in valuation techniques provide a continuous flow of challenging problems for financial engineers and risk managers alike. Designing a sound stochastic model requires finding a careful balance between parsimonious model assumptions, mathematical viability, and interpretability of the output. Moreover, data requirements and the end-user training are to be considered as well.

The KPMG Center of Excellence in Risk Management conference Risk Management Reloaded and this proceedings volume contribute to bridging the gap between academia –providing methodological advances– and practice –having a firm understanding of the economic conditions in which a given model is used. Discussed fields of application range from asset management, credit risk, and energy to risk management issues in insurance. Methodologically, dependence modeling, multiple-curve interest rate-models, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed.


91B30, 91B82, 91B25, 91B24 credit risk dependence modeling interest-rate modeling model risk risk management

Editors and affiliations

  • Kathrin Glau
    • 1
  • Matthias Scherer
    • 2
  • Rudi Zagst
    • 3
  1. 1.Chair of Mathematical FinanceTechnische Universität MünchenGarchingGermany
  2. 2.Chair of Mathematical FinanceTechnische Universität MünchenGarchingGermany
  3. 3.Chair of Mathematical FinanceTechnische Universität MünchenGarchingGermany

Bibliographic information

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