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Risk management for financial institutions is one of the key topics
the financial industry has to deal with. The present volume is a
mathematically rigorous text on solvency modeling. Currently, there
are many new developments in this area in the financial and
insurance industry (Basel III and Solvency II), but none of these
developments provides a fully consistent and comprehensive
framework for the analysis of solvency questions. Merz and Wuthrich
combine ideas from financial mathematics (no-arbitrage theory,
equivalent martingale measure), actuarial sciences (insurance
claims modeling, cash flow valuation) and economic theory (risk
aversion, probability distortion) to provide a fully consistent
framework. Within this framework they then study solvency questions
in incomplete markets, analyze hedging risks, and study
asset-and-liability management questions, as well as issues like
the limited liability options, dividend to shareholder questions,
the role of re-insurance, etc. This work embeds the solvency
discussion (and long-term liabilities) into a scientific framework
and is intended for researchers as well as practitioners in the
financial and actuarial industry, especially those in charge of
internal risk management systems. Readers should have a good
background in probability theory and statistics, and should be
familiar with popular distributions, stochastic processes,
martingales, etc.
Risk management for financial institutions is one of the key topics
the financial industry has to deal with. The present volume is a
mathematically rigorous text on solvency modeling. Currently, there
are many new developments in this area in the financial and
insurance industry (Basel III and Solvency II), but none of these
developments provides a fully consistent and comprehensive
framework for the analysis of solvency questions. Merz and Wuthrich
combine ideas from financial mathematics (no-arbitrage theory,
equivalent martingale measure), actuarial sciences (insurance
claims modeling, cash flow valuation) and economic theory (risk
aversion, probability distortion) to provide a fully consistent
framework. Within this framework they then study solvency questions
in incomplete markets, analyze hedging risks, and study
asset-and-liability management questions, as well as issues like
the limited liability options, dividend to shareholder questions,
the role of re-insurance, etc. This work embeds the solvency
discussion (and long-term liabilities) into a scientific framework
and is intended for researchers as well as practitioners in the
financial and actuarial industry, especially those in charge of
internal risk management systems. Readers should have a good
background in probability theory and statistics, and should be
familiar with popular distributions, stochastic processes,
martingales, etc.
This open access book discusses the statistical modeling of
insurance problems, a process which comprises data collection, data
analysis and statistical model building to forecast insured events
that may happen in the future. It presents the mathematical
foundations behind these fundamental statistical concepts and how
they can be applied in daily actuarial practice. Statistical
modeling has a wide range of applications, and, depending on the
application, the theoretical aspects may be weighted differently:
here the main focus is on prediction rather than explanation.
Starting with a presentation of state-of-the-art actuarial models,
such as generalized linear models, the book then dives into modern
machine learning tools such as neural networks and text recognition
to improve predictive modeling with complex features. Providing
practitioners with detailed guidance on how to apply machine
learning methods to real-world data sets, and how to interpret the
results without losing sight of the mathematical assumptions on
which these methods are based, the book can serve as a modern basis
for an actuarial education syllabus.
This open access book discusses the statistical modeling of
insurance problems, a process which comprises data collection, data
analysis and statistical model building to forecast insured events
that may happen in the future. It presents the mathematical
foundations behind these fundamental statistical concepts and how
they can be applied in daily actuarial practice. Statistical
modeling has a wide range of applications, and, depending on the
application, the theoretical aspects may be weighted differently:
here the main focus is on prediction rather than explanation.
Starting with a presentation of state-of-the-art actuarial models,
such as generalized linear models, the book then dives into modern
machine learning tools such as neural networks and text recognition
to improve predictive modeling with complex features. Providing
practitioners with detailed guidance on how to apply machine
learning methods to real-world data sets, and how to interpret the
results without losing sight of the mathematical assumptions on
which these methods are based, the book can serve as a modern basis
for an actuarial education syllabus.
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