Innovations in Quantitative Risk Management : TU München, September 2013
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The work Innovations in Quantitative Risk Management : TU München, September 2013 represents a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.
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Innovations in Quantitative Risk Management : TU München, September 2013
Resource Information
The work Innovations in Quantitative Risk Management : TU München, September 2013 represents a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.
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 Innovations in Quantitative Risk Management : TU München, September 2013
 Title remainder
 TU München, September 2013
 Statement of responsibility
 edited by Kathrin Glau, Matthias Scherer, Rudi Zagst
 Language
 eng
 Summary

 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 enduser 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, multiplecurve interest ratemodels, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed
 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 enduser 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, multiplecurve interest ratemodels, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed
 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 enduser 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, multiplecurve interest ratemodels, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed
 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 enduser 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, multiplecurve interest ratemodels, and model risk are addressed. Finally, regulatory developments and possible limits of mathematical modeling are discussed
 Dewey number
 519
 LC call number
 HB135147
 Series statement
 Springer Proceedings in Mathematics & Statistics,
 Series volume
 99
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