Tony Van Gestel and Bart Baesens
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780199545117
- eISBN:
- 9780191720147
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199545117.003.0004
- Subject:
- Mathematics, Applied Mathematics, Mathematical Finance
This chapter highlights the conceptual aspects of a rating system without focusing on mathematical and technical aspects. An overview is provided of the different aspects of risk measurement and ...
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This chapter highlights the conceptual aspects of a rating system without focusing on mathematical and technical aspects. An overview is provided of the different aspects of risk measurement and modelling: data, modelling techniques, and implementation for use. All aspects of the development and implementation of a new model are discussed. The system life cycle is explained in section 4.2. Section 4.3 provides a high-level overview on credit scoring models. Such models rely on data, for risk measurement, model use, and model development. The data issues are discussed in Section 4.4. Section 4.5 provides a bird's eye view on the model development process of internal rating systems. Implementation aspects are discussed in Section 4.6. Section 4.7 explains that models need to be maintained and updated regularly. Section 4.8 explains the different, but also partially overlapping aspects of model validation, quality control, and backtesting.Less
This chapter highlights the conceptual aspects of a rating system without focusing on mathematical and technical aspects. An overview is provided of the different aspects of risk measurement and modelling: data, modelling techniques, and implementation for use. All aspects of the development and implementation of a new model are discussed. The system life cycle is explained in section 4.2. Section 4.3 provides a high-level overview on credit scoring models. Such models rely on data, for risk measurement, model use, and model development. The data issues are discussed in Section 4.4. Section 4.5 provides a bird's eye view on the model development process of internal rating systems. Implementation aspects are discussed in Section 4.6. Section 4.7 explains that models need to be maintained and updated regularly. Section 4.8 explains the different, but also partially overlapping aspects of model validation, quality control, and backtesting.
Guillaume Weisang
- Published in print:
- 2017
- Published Online:
- August 2017
- ISBN:
- 9780190607371
- eISBN:
- 9780190607401
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190607371.003.0016
- Subject:
- Economics and Finance, Financial Economics
Risk measurement and management is an important and complex subject for hedge fund stakeholders, managers, and investors. Given that hedge funds dynamically trade a wide range of financial ...
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Risk measurement and management is an important and complex subject for hedge fund stakeholders, managers, and investors. Given that hedge funds dynamically trade a wide range of financial instruments, their returns show tail risk and nonlinear characteristics with respect to many financial markets that require advanced downside risk measures, such as value-at-risk, expected shortfall, and tail risk, to capture risk adequately. This chapter reviews the nature of these risks and presents the measurement tools needed, focusing on fixed-income instruments, derivative securities, and equity risk measurement, and stressing the importance of frequent assessment to capture the possibly rapidly changing risk profiles of hedge funds. This chapter also provides an overview of the linear factor models that investors often use to measure hedge fund risk exposures along many risk factors.Less
Risk measurement and management is an important and complex subject for hedge fund stakeholders, managers, and investors. Given that hedge funds dynamically trade a wide range of financial instruments, their returns show tail risk and nonlinear characteristics with respect to many financial markets that require advanced downside risk measures, such as value-at-risk, expected shortfall, and tail risk, to capture risk adequately. This chapter reviews the nature of these risks and presents the measurement tools needed, focusing on fixed-income instruments, derivative securities, and equity risk measurement, and stressing the importance of frequent assessment to capture the possibly rapidly changing risk profiles of hedge funds. This chapter also provides an overview of the linear factor models that investors often use to measure hedge fund risk exposures along many risk factors.
Michael Powers
- Published in print:
- 2014
- Published Online:
- November 2015
- ISBN:
- 9780231153676
- eISBN:
- 9780231527057
- Item type:
- book
- Publisher:
- Columbia University Press
- DOI:
- 10.7312/columbia/9780231153676.001.0001
- Subject:
- Economics and Finance, Development, Growth, and Environmental
This book examines traditional insurance risks such as earthquakes, storms, terrorist attacks, and other disasters. It begins with a discussion of how the risk of such “acts of God and men” impact on ...
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This book examines traditional insurance risks such as earthquakes, storms, terrorist attacks, and other disasters. It begins with a discussion of how the risk of such “acts of God and men” impact on our lives, health, and possessions. It then proceeds to introduce the statistical techniques necessary for analyzing these uncertainties. It explains that quantifying the risks that such disasters pose is difficult but that it is crucial for achieving the financing objectives of insurance. The book guides readers through the methods available for identifying and measuring such risks, financing their consequences, and forecasting their future behaviour (within the limits of science). It also considers the experience of risk from the perspectives of both policyholders and insurance companies, and compares their respective responses. The discussion of the risks inherent in the private insurance industry leads to a discussion of the government's role as both market regulator and potential “insurer of last resort.” The book concludes with an interdisciplinary investigation into the nature of uncertainty, incorporating ideas from physics, philosophy, and game theory to assess science's limitations in predicting the ramifications of risk.Less
This book examines traditional insurance risks such as earthquakes, storms, terrorist attacks, and other disasters. It begins with a discussion of how the risk of such “acts of God and men” impact on our lives, health, and possessions. It then proceeds to introduce the statistical techniques necessary for analyzing these uncertainties. It explains that quantifying the risks that such disasters pose is difficult but that it is crucial for achieving the financing objectives of insurance. The book guides readers through the methods available for identifying and measuring such risks, financing their consequences, and forecasting their future behaviour (within the limits of science). It also considers the experience of risk from the perspectives of both policyholders and insurance companies, and compares their respective responses. The discussion of the risks inherent in the private insurance industry leads to a discussion of the government's role as both market regulator and potential “insurer of last resort.” The book concludes with an interdisciplinary investigation into the nature of uncertainty, incorporating ideas from physics, philosophy, and game theory to assess science's limitations in predicting the ramifications of risk.
Prasanna Gai
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199544493
- eISBN:
- 9780191747175
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199544493.003.0006
- Subject:
- Economics and Finance, Financial Economics
This chapter describes work at the Bank of England to develop a quantitative framework to guide macroprudential analysis and bank stress-testing work. The Risk Assessment Model for Systemic ...
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This chapter describes work at the Bank of England to develop a quantitative framework to guide macroprudential analysis and bank stress-testing work. The Risk Assessment Model for Systemic Institutions (RAMSI) explicitly characterizes bank balance sheets and allows for feedback effects associated with asset fire sales and network interactions. The model is calibrated to UK financial system data and the results present prototypical projections for system-wide banking assets. Despite the joint normality of the risk factors of the model, RAMSI generates a bimodal asset distribution, reflecting the possibility of second and higher round defaults associated with feedback effects of the kind described in earlier chapters. RAMSI is potentially a policy tool for risk assessment and macroprudential stress-testing exercises.Less
This chapter describes work at the Bank of England to develop a quantitative framework to guide macroprudential analysis and bank stress-testing work. The Risk Assessment Model for Systemic Institutions (RAMSI) explicitly characterizes bank balance sheets and allows for feedback effects associated with asset fire sales and network interactions. The model is calibrated to UK financial system data and the results present prototypical projections for system-wide banking assets. Despite the joint normality of the risk factors of the model, RAMSI generates a bimodal asset distribution, reflecting the possibility of second and higher round defaults associated with feedback effects of the kind described in earlier chapters. RAMSI is potentially a policy tool for risk assessment and macroprudential stress-testing exercises.
Michael R. Powers
- Published in print:
- 2014
- Published Online:
- November 2015
- ISBN:
- 9780231153676
- eISBN:
- 9780231527057
- Item type:
- chapter
- Publisher:
- Columbia University Press
- DOI:
- 10.7312/columbia/9780231153676.003.0003
- Subject:
- Economics and Finance, Development, Growth, and Environmental
This chapter examines complex probability distributions whose shapes make them appropriate for characterizing insurance and other financial risks. In particular, it introduces two important families ...
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This chapter examines complex probability distributions whose shapes make them appropriate for characterizing insurance and other financial risks. In particular, it introduces two important families of distributions: the Pareto family and the symmetric Lévy-stable family, both of which are frequently used to model particularly “risky” random variables with heavy tails (i.e. with large amounts of weight spread over the more extreme values of the random variable). To describe the measurement of risk, the chapter begins by defining the statistical moments of a distribution. It then shows how these quantities are used to compute the expected value (mean), standard deviation, and other helpful parameters.Less
This chapter examines complex probability distributions whose shapes make them appropriate for characterizing insurance and other financial risks. In particular, it introduces two important families of distributions: the Pareto family and the symmetric Lévy-stable family, both of which are frequently used to model particularly “risky” random variables with heavy tails (i.e. with large amounts of weight spread over the more extreme values of the random variable). To describe the measurement of risk, the chapter begins by defining the statistical moments of a distribution. It then shows how these quantities are used to compute the expected value (mean), standard deviation, and other helpful parameters.
