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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

History of engineering & technology --- deposit insurance --- implied volatility --- static arbitrage --- parameterization --- machine learning --- calibration --- dichotomous response --- predictive model --- tree boosting --- GLM --- validation --- generalised linear modelling --- zero-inflated poisson model --- telematics --- benchmark --- cross-validation --- prediction --- stock return volatility --- long-term forecasts --- overlapping returns --- autocorrelation --- chain ladder --- Bornhuetter–Ferguson --- maximum likelihood --- exponential families --- canonical parameters --- prior knowledge --- accelerated failure time model --- chain-ladder method --- local linear kernel estimation --- non-life reserving --- operational time --- zero-inflation --- overdispersion --- automobile insurance --- risk classification --- risk selection --- least-squares monte carlo method --- proxy modeling --- life insurance --- Solvency II --- claims prediction --- export credit insurance --- semiparametric modeling --- VaR estimation --- analyzing financial data --- n/a --- Bornhuetter-Ferguson

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With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets.

insurance --- Solvency II --- risk-neutral models --- computational finance --- asset pricing models --- overnight price gaps --- financial econometrics --- mean-reversion --- statistical arbitrage --- high-frequency data --- jump-diffusion model --- instantaneous volatility --- directional-change --- seasonality --- forex --- bitcoin --- S&amp --- P500 --- risk management --- drawdown --- safe assets --- securitisation --- dealer behaviour --- liquidity --- bid–ask spread --- least-squares Monte Carlo --- put-call symmetry --- regression --- simulation --- algorithmic trading --- market quality --- defined contribution plan --- probability of shortfall --- quadratic shortfall --- dynamic asset allocation --- resampled backtests --- stochastic covariance --- 4/2 model --- option pricing --- risk measures --- American options --- exercise boundary --- Monte Carlo --- multiple exercise options --- dynamic programming --- stochastic optimal control --- asset pricing --- calibration --- derivatives --- hedging --- multivariate models --- volatility

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets.

Economics, finance, business & management --- insurance --- Solvency II --- risk-neutral models --- computational finance --- asset pricing models --- overnight price gaps --- financial econometrics --- mean-reversion --- statistical arbitrage --- high-frequency data --- jump-diffusion model --- instantaneous volatility --- directional-change --- seasonality --- forex --- bitcoin --- S&amp --- P500 --- risk management --- drawdown --- safe assets --- securitisation --- dealer behaviour --- liquidity --- bid–ask spread --- least-squares Monte Carlo --- put-call symmetry --- regression --- simulation --- algorithmic trading --- market quality --- defined contribution plan --- probability of shortfall --- quadratic shortfall --- dynamic asset allocation --- resampled backtests --- stochastic covariance --- 4/2 model --- option pricing --- risk measures --- American options --- exercise boundary --- Monte Carlo --- multiple exercise options --- dynamic programming --- stochastic optimal control --- asset pricing --- calibration --- derivatives --- hedging --- multivariate models --- volatility