Statistical Tools for Finance and Insurance

Statistical Tools for Finance and Insurance presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance. Written by practitioners and leading academics in the field this book offers a unique combination of topics from which every market analyst and risk manager will benefit. Features of the book: Offers insight into new methods and the applicability of the stochastic technology; Provides the tools, instruments and (online) algorithms for recent techniques in quantitative finance and modern treatments in insurance calculations; Covers topics such as heavy tailed distributions, implied trinomial trees, support vector machines, valuation of mortgage-backed securities, pricing of CAT bonds, simulation of risk processes, and ruin probability approximation; Presents extensive examples; The downloadable electronic edition of the book offers interactive tools.

[1]  R. R. Bahadur Rates of Convergence of Estimates and Test Statistics , 1967 .

[2]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[3]  M. Raghavachari On a Theorem of Bahadur on the Rate of Convergence of Test Statistics , 1970 .

[4]  O. Barndorff-Nielsen Information And Exponential Families , 1970 .

[5]  R. Shepherd Theory of cost and production functions , 1970 .

[6]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[7]  J. F. C. Kingman,et al.  Information and Exponential Families in Statistical Theory , 1980 .

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  G. Simons,et al.  On the theory of elliptically contoured distributions , 1981 .

[10]  Jerald F. Lawless,et al.  Statistical Models and Methods for Lifetime Data. , 1983 .

[11]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[12]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  D. W. Coit,et al.  Practical reliability data and analysis , 1986 .

[14]  P. Perron,et al.  Trends and random walks in macroeconomic time series : Further evidence from a new approach , 1988 .

[15]  R. R. Bahadur Some Limit Theorems in Statistics , 1987 .

[16]  A. Gallant,et al.  Nonlinear Statistical Models , 1988 .

[17]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[18]  S. Kotz,et al.  Symmetric Multivariate and Related Distributions , 1989 .

[19]  J. Hull Options, Futures, and Other Derivatives , 1989 .

[20]  Frantisek Rublík On optimality of the LR tests in the sense of exact slopes. I. General case , 1989, Kybernetika.

[21]  P. Perron,et al.  The Great Crash, The Oil Price Shock And The Unit Root Hypothesis , 1989 .

[22]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[23]  Dennis W. Jansen,et al.  The Demand for Money in the United States: Evidence from Cointegration Tests , 1991 .

[24]  Marjorie G. Hahn,et al.  Sums, trimmed sums and extremes , 1991 .

[25]  Monetary Dynamics: An Application of Cointegration and Error-Correction Modeling , 1991 .

[26]  Olivier Gaudoin,et al.  Statistical analysis of the geometric de-eutrophication software-reliability model , 1992 .

[27]  The demand for the components of broad money: error-correction and generalized asset adjustment sytems , 1993 .

[28]  Cointegration, real exchange rate and modelling the demand for broad money in Japan , 1993 .

[29]  D. Orden,et al.  Financial Deregulation and the Dynamics of Money, Prices, and Output in New Zealand and Australia , 1993 .

[30]  S. Price,et al.  The demand for Indonesian narrow money: long-run equilibrium, error correction and forward-looking behaviour , 1994 .

[31]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[32]  A. Arize A re-examination of the demand for money in small developing economies , 1994 .

[33]  Ali M. Kutan,et al.  Economic Reforms and Long-Run Money Demand in China: Implications for Monetary Policy , 1994 .

[34]  Frederic S. Mishkin Symposium on the Monetary Transmission Mechanism , 1995 .

[35]  B. Bernanke,et al.  Inside the Black Box: The Credit Channel of Monetary Policy Transmission , 1995 .

[36]  Y. Nikitin,et al.  Asymptotic Efficiency of Nonparametric Tests: Contents , 1995 .

[37]  Léopold Simar,et al.  A Note on the Convergence of Nonparametric DEA Efficiency Measures , 1996 .

[38]  Ryuzo Miyao Does a Cointegrating M2 Demand Relation Really Exist in Japan , 1996 .

[39]  Gaston H. Gonnet,et al.  On the LambertW function , 1996, Adv. Comput. Math..

[40]  A. Ledford,et al.  Statistics for near independence in multivariate extreme values , 1996 .

[41]  A. Pázman The density of the parameter estimators when the observations are distributed exponentially , 1996 .

[42]  Dimiter Driankov,et al.  Fuzzy Model Identification , 1997, Springer Berlin Heidelberg.

[43]  C. Klüppelberg,et al.  Modelling Extremal Events , 1997 .

[44]  R. Dekle Financial Liberalization and Money Demand in ASEAN Countries: Implications for Monetary Policy , 1997, SSRN Electronic Journal.

[45]  H. Joe Multivariate models and dependence concepts , 1998 .

[46]  R. Nelsen An Introduction to Copulas , 1998 .

[47]  Subramanian S Sriram Demand for M2 in an Emerging-Market Economy: An Error-Correction Model for Malaysia , 1999, SSRN Electronic Journal.

[48]  David W. Coit,et al.  Analysis of grouped data from field-failure reporting systems , 1999 .

[49]  E. Mammen,et al.  On estimation of monotone and concave frontier functions , 1999 .

[50]  Claus Brand,et al.  A money demand system for euro area M3 , 2000, Social Science Research Network.

[51]  D. Coit,et al.  Gamma distribution parameter estimation for field reliability data with missing failure times , 2000 .

[52]  B. Park,et al.  THE FDH ESTIMATOR FOR PRODUCTIVITY EFFICIENCY SCORES , 2000, Econometric Theory.

[53]  G. Samorodnitsky,et al.  Multivariate extremes, aggregation and risk estimation , 2000 .

[54]  XploRe® - Application Guide , 2000 .

[55]  M. Bahmani‐Oskooee,et al.  How stable is M2 money demand function in Japan , 2001 .

[56]  P. Embrechts,et al.  Risk Management: Correlation and Dependence in Risk Management: Properties and Pitfalls , 2002 .

[57]  N. Bingham,et al.  Semi-parametric modelling in finance: theoretical foundations , 2002 .

[58]  J. Lawless Statistical Models and Methods for Lifetime Data , 2002 .

[59]  Rüdiger Kiesel,et al.  Sensitivity analysis of credit portfolio models , 2002 .

[60]  Gerhard Stahl,et al.  Applied Quantitative Finance , 2002 .

[61]  Rafael Schmidt,et al.  Credit Risk Modelling and Estimation via Elliptical Copulae , 2003 .

[62]  Karel Komorád Implied Trinomial Trees and Their Implementation with XploRe , 2003, Comput. Stat..

[63]  M. Stehlík Distributions of exact tests in the exponential family , 2003 .

[64]  P. Embrechts,et al.  Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management , 2003 .

[65]  L. de Haan,et al.  Bivariate tail estimation: dependence in asymptotic independence , 2004 .

[66]  Markus Junker,et al.  Measurement of Aggregate Risk with Copulas , 2005 .