A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time.
A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time.
In a constrained optimization problem, the decisionmaker wants to select the “optimal” choice – the one most valuable to him or her – that also meets all of the constraints imposed by the problem.
In a constrained optimization problem, the decisionmaker wants to select the “optimal” choice – the one most valuable to him or her – that also meets all of the constraints imposed by the problem.
In 1958 the Canadian Political Science Association established a committee to look into ways and means of improving statistical research in the social sciences in Canada.
In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called "e;The fundamental limit theorems in probability"e; in which he set out what he considered to be "e;the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered .
Metaheuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics, and science in reasonable time frames, but finding exact solutions in these applications still poses a real challenge.
Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources.
Taxpayer compliance is a voluntary activity, and the degree to which the tax system works is affected by taxpayers' knowledge that it is their moral and legal responsibility to pay their taxes.
To derive rational and convincible solutions to practical decision making problems in complex and hierarchical human organizations, the decision making problems are formulated as relevant mathematical programming problems which are solved by developing optimization techniques so as to exploit characteristics or structural features of the formulated problems.
A classic treatise that defined the field of applied demand analysis, Consumer Demand in the United States: Prices, Income, and Consumption Behavior is now fully updated and expanded for a new generation.
This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space models.
Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds.
Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data.
Financial engineering has been proven to be a useful tool for risk management, but using the theory in practice requires a thorough understanding of the risks and ethical standards involved.
This book gives readers the solid and formal mathematical background to apply copulas to a range of mathematical areas, such as probability, real analysis, measure theory, and algebraic structures.
Designed for a one-semester course, Applied Statistics for Business and Economics offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world.
Filling the void between surveys of the field with relatively light mathematical content and books with a rigorous, formal approach to stochastic integration and probabilistic ideas, Stochastic Financial Models provides a sound introduction to mathematical finance.
When scientists formulate their theories, expectations, and hypotheses, they often use statements like: ``I expect mean A to be bigger than means B and C"e;; ``I expect that the relation between Y and both X1 and X2 is positive"e;; and ``I expect the relation between Y and X1 to be stronger than the relation between Y and X2"e;.
Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.
Teach Your Students How to Become Successful Working QuantsQuantitative Finance: A Simulation-Based Introduction Using Excel provides an introduction to financial mathematics for students in applied mathematics, financial engineering, actuarial science, and business administration.
The theory and applications of random dynamical systems (RDS) are at the cutting edge of research in mathematics and economics, particularly in modeling the long-run evolution of economic systems subject to exogenous random shocks.
Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students.
While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases.
Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk.
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations.
Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals.
Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures.
Explores the Origin of the Recent Banking Crisis and how to Preclude Future CrisesShedding new light on the recent worldwide banking debacle, The Banking Crisis Handbook presents possible remedies as to what should have been done prior, during, and after the crisis.
Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools.
Fascinating Insight into How the Financial System Works and How the Credit Crisis AroseClearly supplies details vital to understanding the crisis Unravelling the Credit Crunch provides a clearly written, comprehensive account of the current credit crisis that is easily understandable to non-specialists.
