Like any other company, life insurance companies maintain a combination of debt and equity for their long-term financing, which forms their capital structure. Many theories have been developed in the literature to focus upon the determinants that are likely to affect leverage decisions of the life insurance firms in the post life Risk-Based Capital (RBC) regulation era. This report documents the application of multiple regression techniques to derive and analyze a Capital Asset Ratio (CAP) model based on the data pertaining to a large number of life insurance companies during 2000 to 2004. The data set is organized as panel data. Model coefficients, together with the error structure, are analyzed using SAS software to develop a valid model that tries to explain the Capital to Asset Ratio (CAP) for life insurers in terms of various variables of interest. The latter include return on capital, total assets, and two measures of risk: asset risk and product risk, etc. A balanced panel dataset was extracted from the given unbalanced input dataset containing missing entries. In addition, a selected few of the explanatory variables were chosen from a large group present in the input dataset based on previous work on relations among asset risk, product risk and capital in the life insurance industry by Etti G. Baranoff and Thomas W. Sager (2002). Fixed Effects model was chosen based on the assumption that the firm-specific effects were correlated to the explanatory variables. Differencing method was employed so that OLS estimator could safely be used for the coefficients in the regression model. Based on the proposed model, it is found that Capital to Asset Ratio has positive relationships with product risk and return on capital, with the corporate form of organization, and with membership in an affiliated group of companies. On the other hand, it has a negative relationship with company's size and the ratio of life premiums or annuity premiums to the total premiums generated.

This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.

Understand Up-to-Date Statistical Techniques for Financial and Actuarial Applications Since the first edition was published, statistical techniques, such as reliability measurement, simulation, regression, and Markov chain modeling, have become more prominent in the financial and actuarial industries. Consequently, practitioners and students must acquire strong mathematical and statistical backgrounds in order to have successful careers. Financial and Actuarial Statistics: An Introduction, Second Edition enables readers to obtain the necessary mathematical and statistical background. It also advances the application and theory of statistics in modern financial and actuarial modeling. Like its predecessor, this second edition considers financial and actuarial modeling from a statistical point of view while adding a substantial amount of new material. New to the Second Edition Nomenclature and notations standard to the actuarial field Excel exercises with solutions, which demonstrate how to use Excel functions for statistical and actuarial computations Problems dealing with standard probability and statistics theory, along with detailed equation links A chapter on Markov chains and actuarial applications Expanded discussions of simulation techniques and applications, such as investment pricing Sections on the maximum likelihood approach to parameter estimation as well as asymptotic applications Discussions of diagnostic procedures for nonnegative random variables and Pareto, lognormal, Weibull, and left truncated distributions Expanded material on surplus models and ruin computations Discussions of nonparametric prediction intervals, option pricing diagnostics, variance of the loss function associated with standard actuarial models, and Gompertz and Makeham distributions Sections on the concept of actuarial statistics for a collection of stochastic status models The book presents a unified approach to both financial and actuarial modeling through the use of general status structures. The authors define future time-dependent financial actions in terms of a status structure that may be either deterministic or stochastic. They show how deterministic status structures lead to classical interest and annuity models, investment pricing models, and aggregate claim models. They also employ stochastic status structures to develop financial and actuarial models, such as surplus models, life insurance, and life annuity models.

The Basel agreements and the associated European directives have made banking prudential capital contingent on their risk profile rather than on their size or turnover. The Solvency 2 Directive (hereinafter the "Directive") repeats this process for European insurers and reinsurers. It constitutes a total paradigm shift for the majority of European insurers. It defines the main regulatory principles aimed at regulating their activity and in particular determining the amount of prudential capital associated with the risks inherent to their activity.In accordance with the directive, the prudential capital corresponds in principle to an insurer with a 99.5% percentile of the change in its basic own funds over the coming year. Such a prospective risk measure requires for an insurer the ability to address two problems: a valuation problem and a simulation problem. In practice, the 99.5% percentile of the change in basic own funds is estimated using the MonteCarlo method. It is particularly sensitive to the one-year law retained for the risk factors vector. Its Monte Carlo valuation would ideally require the simulation of one year risk factor vector x and the valuation of the associated equity values. Given the significant calculation time required for numerical evaluation, this approach is in practice unsuitable. In order to circumvent this problem, the insurers have developed many approximate methods or "proxies" which make it possible to approximate the basic own funds value instantaneously. Today, these methods are rarely accompanied by error controls that would measure the simulation quality. More precisely, the methods currently used by the insurers do not make it possible to control naturally the approximation error generated by the use of the proxy model instead. The proposed error checks are therefore always empirical and too approximate.In order to solve this problematic, we propose, in a first part of this thesis, a new method of constructing the proxy that is both resource-efficient and offers rigorous error control. The second part of this thesis aims at applying the extreme value theory to the prudential capital estimate when information on the covariate is available. In particular, when the covariate is high dimensional, we are confronted with the problem of the curse of dimensionality, which translates into a decrease in the fastest possible convergence rates of estimators of the regression function to their target curve. This problem refers to the phenomenon where the volume of the covariate increases so rapidly that available data become sparse. To obtain a statistically reliable result, the amount of data needed to support the result often increases exponentially with dimensionality, which is generally problematic in many practical applications. To overcome this estimation problem, we propose a new efficient evaluation methodology by combining the generalized additive model and the sparse group lasso method.

This volume highlights recent applications of multiple-criteria decision-making (MCDM) models in the field of finance. Covering a wide range of MCDM approaches, including multiobjective optimization, goal programming, value-based models, outranking techniques, and fuzzy models, it provides researchers and practitioners with a set of MCDM methodologies and empirical results in areas such as portfolio management, investment appraisal, banking, and corporate finance, among others. The book addresses issues related to problem structuring and modeling, solution techniques, comparative analyses, as well as combinations of MCDM models with other analytical methodologies.