An Introduction to Excel VBA Programming: with Applications in Finance and Insurance

Excel Visual Basic for Applications (VBA) can be used to automate operations in Excel and is one of the most frequently used software programs for manipulating data and building models in banks and insurance companies. An Introduction to Excel VBA Programming: with Applications in Finance and Insurance introduces readers to the basic fundamentals of VBA Programming while demonstrating applications of VBA to solve real-world problems in finance and insurance. Assuming no prior programming experience and with reproducible examples using code and data, this text is suitable for advanced undergraduate students, graduate students, actuaries, and financial analysts who wish to learn VBA. Features: <ul>
<li>Presents the theory behind the algorithms in detail</li> <li>Includes more than 100 exercises with selected solutions</li> <li>Provides VBA code in Excel files and data to reproduce the results in the book</li> <li>Offers a solutions manual for qualified instructors</li> </ul>

Gan, Guojun

Reference

Chapman and Hall/CRC

Data Clustering in C++: An Object-Oriented Approach (Chapman & Hall/Crc Data Mining and Knowledge Discovery)

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. 
 Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered.
 This book is divided into three parts--
 <ul>
 <li>Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns</li>
 <li>A C++ Data Clustering Framework: The development of data clustering base classes</li>
 <li>Data Clustering Algorithms: The implementation of several popular data clustering algorithms </li>
 
</ul>
 A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

Data Clustering in C++: An Object-Oriented Approach (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Business & Money

Education & Reference

Metamodeling for Variable Annuities (Chapman and Hall/CRC Financial Mathematics Series)

This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students, graduate students, and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end, the book will not only describe the theory of these mathematical approaches, but also present the implementations.

Valdez, Emiliano A.

Insurance

Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.

Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability, Series Number 20)

Computers & Technology

Wu, Jianhong

Ma, Chaoqun

Computer Science

Society for Industrial and Applied Mathematics

This book covers part of the learning outcomes of the Fundamentals of Actuarial Mathematics (FAM) exam and the Advanced Short-Term Actuarial Mathematics (ASTAM) exam administered by the Society of Actuaries. It can be used by students and practitioners who prepare for actuarial exams.

Actuarial Loss Models (Chapman & Hall/CRC Series in Actuarial Science)

An introduction to the mathematical theory and financial models developed and used on Wall Street Providing both a theoretical and practical approach to the underlying mathematical theory behind financial models, Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach presents important concepts and results in measure theory, probability theory, stochastic processes, and stochastic calculus. Measure theory is indispensable to the rigorous development of probability theory and is also necessary to properly address martingale measures, the change of numeraire theory, and LIBOR market models. In addition, probability theory is presented to facilitate the development of stochastic processes, including martingales and Brownian motions, while stochastic processes and stochastic calculus are discussed to model asset prices and develop derivative pricing models. The authors promote a problem-solving approach when applying mathematics in real-world situations, and readers are encouraged to address theorems and problems with mathematical rigor. In addition, Measure, Probability, and Mathematical Finance features: <ul>
<li>A comprehensive list of concepts and theorems from measure theory, probability theory, stochastic processes, and stochastic calculus</li> <li>Over 500 problems with hints and select solutions to reinforce basic concepts and important theorems</li> <li>Classic derivative pricing models in mathematical finance that have been developed and published since the seminal work of Black and Scholes</li> </ul>
Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach is an ideal textbook for introductory quantitative courses in business, economics, and mathematical finance at the upper-undergraduate and graduate levels. The book is also a useful reference for readers who need to build their mathematical skills in order to better understand the mathematical theory of derivative pricing models.

Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach

Xie, Hong

Finance

wiley

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