%{search_type} search results

• Part 1 Actuarial mathematics: the relationship between finance and actuarial science
• actuarial applications of generalized linear models. Part 2 Credit: consumer credit and statistics
• methodologies for classifying applications for credit
• credit scoring and quality management
• consumer credit and business cycles. Part 3 Financial markets: probability in finance - an introduction
• introduction to financial economics
• American options
• notes on term structure models
• default risk
• nonparametric methods and option pricing
• stochastic volatility
• market time and asset price movements theory and estimation.
• (source: Nielsen Book Data)

The chapters in this book describe various aspects of the application of statistical methods in finance. It will interest and attract statisticians to this area, illustrate some of the many ways that statistical tools are used in financial applications, and give some indication of problems which are still outstanding. The statisticians will be stimulated to learn more about the kinds of models and techniques outlined in the book - both the domain of finance and the science of statistics will benefit from increased awareness by statisticians of the problems, models, and techniques applied in financial applications. For this reason, extensive references are given. The level of technical detail varies between the chapters. Some present broad non-technical overviews of an area, while others describe the mathematical niceties. This illustrates both the range of possibilities available in the area for statisticians, while simultaneously giving a flavour of the different kinds of mathematical and statistical skills required. Whether you favour data analysis or mathematical manipulation, if you are a statistician there are problems in finance which are appropriate to your skills.
(source: Nielsen Book Data)
Describes aspects of the application of statistical methods in finance, and aims to attract statisticians to this area by illustrating some of the many ways in which statistical tools are used in financial applications. Statisticians will be stimulated to learn more about the kinds of models and techniques outlined in this book.
(source: Nielsen Book Data)

This volume comprises a reference work for teaching at graduate level and research in empirical finance. It covers a wide range of statistical and probabilistic methods applied to a variety of financial methods.
(source: Nielsen Book Data)

• 1. Review of Probability
• 2. What is a Random Process
• 3. Examples of Markovian Processes
• 4. Spectral Measurement and Correlation
• 5. Thermal Noise
• 6. Shot Noise
• 7. The Fluctuation-Dissipation Theorem
• 8. Generalized Fokker-Planck Equation of Markov Process
• 9. Langevin Process
• 10. Langevin Treatment of the Fokker-Planck Process
• 11. The Rotating Wave Van Del Pol Oscillator (RWVP)
• 12. Noise in Homogeneous Semiconductors
• 13. Random Walk of Light in Turbid Media
• 14. Analytical Solution of the Elastic Boltzmann Transport Equation
• 15. Signal Extraction in the Presence of Smoothing and Noise
• 16. Stochastic Methods to Investment Decision
• 17. Spectral Analysis of Economic Time Series.
• (source: Nielsen Book Data)

This respected high-level text is aimed at students and professionals working on random processes in various areas, including physics and finance. The first author, Melvin Lax (1922-2002) was a distinguished Professor of Physics at City College of New York and a member of the U. S. National Academy of Sciences, and is widely known for his contributions to our understanding of random processes in physics. Most chapters of this book are outcomes of the class notes which Lax taught at the City University of New York from 1985 to 2001. The material is unique as it presents the theoretical framework of Lax's treatment of random processes, from basic probability theory to Fokker-Planck and Langevin Processes, and includes diverse applications, such as explanations of very narrow laser width, analytical solutions of the elastic Boltzmann transport equation, and a critical viewpoint of mathematics currently used in the world of finance.
(source: Nielsen Book Data)

with an autobiography from Ragnar NorbergThe Risk and Stochastics Conference, held at the Royal Statistical Society in April 2015, brought together academics from the worlds of actuarial science, stochastic calculus, finance and statistics to celebrate the achievements of Professor Ragnar Norberg as he turned 70. After the conference, Ragnar Norberg suddenly fell very ill and passed away; this book honours his life and work.This collection of articles is written by speakers of the conference, themselves respected academics who have influenced and been influenced by the life and work of Professor Norberg. His professional and academic achievements are celebrated here, most significantly the instrumental work he put into setting up the world-renowned Risk and Stochastics Enterprise at the London School of Economics (LSE). Subjects covered include discussion of risk measurements, ruin constraint, supporting stable pensions, filtration in discrete time, Riesz means and Beurling moving averages and orthonormal polynomial expansions. Also featured are notes from contributors giving account of their personal relations with Professor Norberg, as well as an autobiographical chapter from the man himself.Aimed at graduate level students and researchers interested in the life and work of Ragnar Norberg, this book provides a unique opportunity to reflect on and understand key findings and ground-breaking research in modern actuarial and financial mathematics and their interface, while giving intimate insights into the life of a leading academic mind.
(source: Nielsen Book Data)

• Preface to the Fith Edition
• Part I Option Pricing
• Derivatives
• Introduction to Option Management
• Basic Concepts of Probability Theory
• Stochastic Processes in Discrete Time
• Stochastic Integrals and Differential Equations
• Black-Scholes Option Pricing Model
• Binomial Model for European Options
• American Options
• Exotic Options
• Interest Rates and Interest Rate Derivatives
• Part II Statistical Models of Financial Time Series
• Introduction: Definitions and Concepts
• ARIMA Time Series Models
• Time Series with Stochastic Volatility
• Long Memory Time Series
• Non-Parametric and Flexible Time Series Estimators
• Part III Selected Financial Applications
• Value at Risk and Backtesting
• Copulae and Value at Risk
• Statistics of Extreme Risks
• Neural Networks and Deep Learning
• Volatility Risk of Option Portfolios
• Nonparametric Estimators for the Probability of Default
• Credit Risk Management and Credit Derivatives
• Financial econometrics of Crypto-currencies
• A Technical Appendix
• Index
• Symbols and Notations.

Now in its fifth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods for evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to specific problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic. All numerical calculations are transparent and reproducible using quantlets. For this new edition the book has been updated and extensively revised and now includes several new aspects such as neural networks, deep learning, and crypto-currencies. Both R and Matlab code, together with the data, can be downloaded from the books product page and the Quantlet platform. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allow readers to reproduce the tables, pictures and calculations inside this Springer book. "This book provides an excellent introduction to the tools from probability and statistics necessary to analyze financial data. Clearly written and accessible, it will be very useful to students and practitioners alike." Yacine Ait-Sahalia, Otto Hack 1903 Professor of Finance and Economics, Princeton University

• Returns.
• Random Walk Hypothesis.
• Portfolios.
• Efficient Portfolio Theory.
• Estimation.
• Capital Asset Pricing Model.
• The Market Model.
• The Single-Index Model.
• Factor Models.
• (source: Nielsen Book Data)

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.
(source: Nielsen Book Data)

• Part I Option Pricing: Derivatives.- Introduction to Option Management.- Basic Concepts of Probability Theory.- Stochastic Processes in Discrete Time.- Stochastic Integrals and Differential Equations.- Black-Scholes Option Pricing Model.- Binomial Model for European Options.- American Options.- Exotic Options.- Interest Rates and Interest Rate Derivatives.- Part II Statistical Models of Financial Time Series: Introduction - Definitions and Concepts.- ARIMA Time Series Models.- Time Series with Stochastic Volatility.- Long Memory Time Series.- Non-Parametric and Flexible Time Series Estimators.- Part III Selected Financial Applications: Copulae and Value at Risk.- Statistics of Extreme Risks.- Neural Networks.- Volatility Risk of Option Portfolios.- Nonparametric Estimators for the Probability of Default.- Credit Risk Management and Credit Derivatives.- Appendix: Integration Theory.- Portfolio Strategies.
• (source: Nielsen Book Data)

Now in its fourth edition, this book offers a detailed yet concise introduction to the growing field of statistical applications in finance. The reader will learn the basic methods of evaluating option contracts, analyzing financial time series, selecting portfolios and managing risks based on realistic assumptions about market behavior. The focus is both on the fundamentals of mathematical finance and financial time series analysis, and on applications to given problems concerning financial markets, thus making the book the ideal basis for lectures, seminars and crash courses on the topic. For this new edition the book has been updated and extensively revised and now includes several new aspects, e.g. new chapters on long memory models, copulae and CDO valuation. Practical exercises with solutions have also been added. Both R and Matlab Code, together with the data, can be downloaded from the book's product page and www.quantlet.de.
(source: Nielsen Book Data)

• Preface ix Acknowledgments xi CHAPTER 1 Setting the Stage 1 Why Is This Book Different? 2 Road Map of the Book 3 References 5 CHAPTER 2 Building Zero Curves 7 Market Instruments 8 Linear Interpolation 16 Cubic Splining 25 Appendix: Finding Swap Rates Using a Floating Coupon Bond Approach 41 References 43 CHAPTER 3 Valuing Vanilla Options 45 Black-Scholes Formulae 47 Adaptations of the Black-Scholes Formulae 53 Limitations of the Black-Scholes Formulae 70 Application in Currency Risk Management 74 Appendix 78 References 80 CHAPTER 4 Simulations 81 Uniform Number Generation 82 Non-Uniform Number Generation 86 Applications of Simulations 93 Variance Reduction Techniques 100 References 104 CHAPTER 5 Valuing Exotic Options 107 Valuing Path-Independent, European-Style Options on a Single Variable 108 Valuing Path-Dependent, European-Style Options on a Single Variable 114 Valuing Path-Independent, European-Style Options on Two Variables 135 Valuing Path-Dependent, European-Style Options on Multiple Variables 152 References 157 CHAPTER 6 Estimating Model Parameters 159 Calibration of Parameters in the Black-Scholes Model 161 Using Implied Black-Scholes Volatility Surface and Zero Rate Term Structure to Value Options 169 Using Volatility Surface 178 Calibration of Interest Rate Option Model Parameters 190 Statistical Estimation 196 References 203 CHAPTER 7 The Effectiveness of Hedging Strategies 205 Delta Hedging 206 Assumptions Underlying Delta Hedging 216 Beyond Delta Hedging 223 Testing Hedging Strategies 230 Analysis Associated with the Hedging of a European-Style Vanilla Put Option 235 References 244 CHAPTER 8 Valuing Variable Annuity Guarantees 245 Basic GMDB 246 Death Benefit Riders 261 Other Details Associated with GMDB Products 269 Improving Modeling Assumptions 273 Living Benefit Riders 276 References 279 CHAPTER 9 Real Options 281
• Surrendering a GMAB Rider 282 Adding Servers in a Queue 300 References 314 CHAPTER 10 Parting Thoughts 315 About the Author 317 About the Website 319 Index 321.
• (source: Nielsen Book Data)

Learn how quantitative models can help fight client problems head-on Before financial problems can be solved, they need to be fully understood. Since in-depth quantitative modeling techniques are a powerful tool to understanding the drivers associated with financial problems, one would need a solid grasp of these techniques before being able to unlock their full potential of the methods used. In The Mathematics of Financial Models, the author presents real world solutions to the everyday problems facing financial professionals. With interactive tools such as spreadsheets for valuation, pricing, and modeling, this resource combines highly mathematical quantitative analysis with useful, practical methodologies to create an essential guide for investment and risk-management professionals facing modeling issues in insurance, derivatives valuation, and pension benefits, among others. In addition to this, this resource also provides the relevant tools like matrices, calculus, statistics and numerical analysis that are used to build the quantitative methods used. Financial analysts, investment professionals, risk-management professionals, and graduate students will find applicable information throughout the book, and gain from the self-study exercises and the refresher course on key mathematical topics. Equipped with tips and information, The Mathematics of Financial Models * Provides practical methodologies based on mathematical quantitative analysis to help analysts, investment and risk-management professionals better navigate client issues * Contains interactive tools that demonstrate the power of analysis and modeling * Helps financial professionals become more familiar with the challenges across a range of industries * Includes a mathematics refresher course and plenty of exercises to get readers up to speed The Mathematics of Financial Models is an in-depth guide that helps readers break through common client financial problems and emerge with clearer strategies for solving issues in the future.
(source: Nielsen Book Data)

• Derivatives
• Introduction to option management
• Basic concepts of probability theory
• Stochastic processes in discrete time
• Stochastic integrals and differential equations
• Black-Scholes option pricing model
• Binomial model for European options
• American options
• Exotic options
• Interest rates and interest rate derivatives
• Introduction : definitions and concepts
• ARIMA time series models
• Time series with stochastic volatility
• Long memory time series
• Non-parametric and flexible time series estimators
• Value at risk and backtesting
• Copulae and value at risk
• Statistics of extreme risks
• Neural networks
• Volatility risk of option portfolios
• Non-parametric estimators for the probability of default
• Credit risk management.

Provides a concise introduction to the basic methods of evaluating option contracts, analysing financial time series, selecting portfolios and managing risks making realistic assumptions of the market behviour. Focuses on both the fundamentals of mathematical finance and financial time series analysis and on applicaiton to given problems of financial markets -- Back cover.

• Arbitrage theory
• Preferences
• Optimality and equilibrium
• Monetary measures of risk
• Dynamic arbitrage theory
• American contingent claims
• Superhedging
• Efficient hedging
• Hedging under constraints
• Minimizing the hedging error
• Dynamic risk measures.

This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in discrete time has two immediate benefits. First, the probabilistic machinery is simpler, and one can discuss right away some of the key problems in the theory of pricing and hedging of financial derivatives. Second, the paradigm of a complete financial market, where all derivatives admit a perfect hedge, becomes the exception rather than the rule. Thus, the need to confront the intrinsic risks arising from market incomleteness appears at a very early stage. The first part of the book contains a study of a simple one-period model, which also serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of financial risk. In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk. This third revised and extended edition now contains more than one hundred exercises. It also includes new material on risk measures and the related issue of model uncertainty, in particular a new chapter on dynamic risk measures and new sections on robust utility maximization and on efficient hedging with convex risk measures.
(source: Nielsen Book Data)

• Linear regression models.- Multivariate analysis and likelihood inference.- Basic investment models and their statistical analysis.- Parametric models and bayesian methods.- Time series modeling forecasting.- Dynamic models of asset return and their volatilities.- Nonparametric regression and substantive-empirical modeling.- Option pricing and market data.- Advanced multivariate and time series methods in financial econometrics.- Interest rate markets.- Statistical trading strategies.- Statistical methods in risk management.- Appendix A.- Appendix B.- Appendix C.- References.- Index.
• (source: Nielsen Book Data)

This book presents statistical methods and models of importance to quantitative finance and links finance theory to market practice via statistical modelling and decision making. Part I provides basic background in statistics, which includes linear regression and extensions to generalized linear models and nonlinear regression, multivariate analysis, likelihood inference and Bayesian methods, and time series analysis. It also describes applications of these methods to portfolio theory and dynamic models of asset returns and their volatilities.Part II presents advanced topics in quantitative finance and introduces a substantive-empirical modelling approach to address the discrepancy between finance theory and market data. It describes applications to option pricing, interest rate markets, statistical trading strategies, and risk management. Nonparametric regression, advanced multivariate and time series methods in financial econometrics, and statistical models for high-frequency transactions data are also introduced in this connection.The book has been developed as a textbook for courses on statistical modelling in quantitative finance in master's level financial mathematics (or engineering) and computational (or mathematical) finance programs. It is also designed for self-study by quantitative analysts in the financial industry who want to learn more about the background and details of the statistical methods used by the industry. It can also be used as a reference for graduate statistics and econometrics courses on regression, multivariate analysis, likelihood and Bayesian inference, nonparametrics, and time series, providing concrete examples and data from financial markets to illustrate the statistical methods.
(source: Nielsen Book Data)

• Option Pricing.- Statistical Model of Financial Time Series.- Selected Financial Applications.
• (source: Nielsen Book Data)

Presents the necessary statistical and mathematical background for Financial Engineers and introduces the main ideas in mathematical finance and financial statistics. This work covers topics such as: option valuation, financial time series analysis, value-at-risk, copulas, and statistics of the extremes.
(source: Nielsen Book Data)

• Preface to the Second Edition. From the Preface to the First Edition. PART I. BACKGROUND.
• 1. Motivation.
• 2. Financial Theory. PART II. NUMERICAL METHODS.
• 3. Basics of Numerical Analysis.
• 4. Numerical Integration: Deterministic and Monte Carlo Methods.
• 5. Finite Difference Methods for Partial Differential Equations.
• 6. Convex Optimization. PART III. PRICING EQUITY OPTIONS.
• 7. Option Pricing by Binomial and Trinomial Lattices.
• 8. Option Pricing by Monte Carlo Methods.
• 9. Option Pricing by Finite Difference Methods. PART IV. ADVANCED OPTMIZATION MODELS AND METHODS.
• 10. Dynamic Programming.
• 11. Linear Stochastic Programming Models with Recourse.
• 12. Non-Convex Optimization. PART V. APPENDICES. Appendix A. Introduction to MATLAB Programming. Appendix B. Refresher on Probability theory and Statistics. Appendix C. Introduction to AMPL. Index.
• (source: Nielsen Book Data)

A state-of-the-art introduction to the powerful mathematical and statistical tools used in the field of finance The use of mathematical models and numerical techniques is a practice employed by a growing number of applied mathematicians working on applications in finance. Reflecting this development, Numerical Methods in Finance and Economics: A MATLAB?-Based Introduction, Second Edition bridges the gap between financial theory and computational practice while showing readers how to utilize MATLAB?--the powerful numerical computing environment--for financial applications. The author provides an essential foundation in finance and numerical analysis in addition to background material for students from both engineering and economics perspectives. A wide range of topics is covered, including standard numerical analysis methods, Monte Carlo methods to simulate systems affected by significant uncertainty, and optimization methods to find an optimal set of decisions. Among this book's most outstanding features is the integration of MATLAB?, which helps students and practitioners solve relevant problems in finance, such as portfolio management and derivatives pricing. This tutorial is useful in connecting theory with practice in the application of classical numerical methods and advanced methods, while illustrating underlying algorithmic concepts in concrete terms. Newly featured in the Second Edition: In-depth treatment of Monte Carlo methods with due attention paid to variance reduction strategies New appendix on AMPL in order to better illustrate the optimization models in Chapters 11 and 12 New chapter on binomial and trinomial lattices Additional treatment of partial differential equations with two space dimensions Expanded treatment within the chapter on financial theory to provide a more thorough background for engineers not familiar with finance New coverage of advanced optimization methods and applications later in the text Numerical Methods in Finance and Economics: A MATLAB?-Based Introduction, Second Edition presents basic treatments and more specialized literature, and it also uses algebraic languages, such as AMPL, to connect the pencil-and-paper statement of an optimization model with its solution by a software library. Offering computational practice in both financial engineering and economics fields, this book equips practitioners with the necessary techniques to measure and manage risk.
(source: Nielsen Book Data)

This book is an introduction to financial mathematics. The first part of the book studies a simple one-period model which serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of risk. In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Such models are typically incomplete: They involve intrinsic risks which cannot be hedged away completely. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk. In addition to many corrections and improvements, this second edition contains several new sections, including a systematic discussion of law-invariant risk measures and of the connections between American options, superhedging, and dynamic risk measures.
(source: Nielsen Book Data)