Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented. Provides step-by-step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models. Accompanied by a CD-ROM containing links to the software as well as the datasets used in the examples. Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.
The essential guide to financial instruments, logically presented Fundamentals of Financial Instruments deals with the global financial markets and the instruments in which they trade. While most books on finance tend to be heavily mathematical, this book emphasizes the concepts in a logical, sequential fashion, introducing mathematical concepts only at the relevant times. As a result, the reader gains conceptual clarity reinforced by just the right level of technical detail to ensure a comprehensive exposure to the skills needed in the financial world. Establishes a strong foundation for understanding global markets Acts as an invaluable resource for those considering a career in the financial markets Offers an accessible yet in-depth treatise on modern financial instruments Presents a logical navigational path for a typical student of finance who is attempting to come to terms with the intricacies of the subject Covering the fundamentals of various types of assets in a single volume, Fundamentals of Financial Instruments is a compact yet comprehensive one-stop reference for students and professionals in finance and economics.
This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
Discover the secrets to applying simple econometric techniques to improve forecasting Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest. Presents the economic and financial variables that offer unique insights into economic performance Highlights the econometric techniques that can be used to characterize variables Explores the application of SAS software, complete with simple explanations of SAS-code and output Identifies key econometric issues with practical solutions to those problems Presenting the «ten commandments» for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.
Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
This book introduces the application of microeconometric methods for modelling various aspects of economic activity for small to large size enterprises, using methods that are based on both time-series and cross-section approaches. The information obtained from using these estimated models can then be used to inform business decisions that improve the efficiency of operations and planning. Basic models used in the modelling of the business (single-equation and multiple-equation systems) are introduced whilst a wide range of economic activity including major aspects of financial management, demand for labour, administrative staff and labour productivity are also explored. Microeconometrics in Business Management: Introduces econometric methods which can be used in the modelling of economic activity and forecasting, to help improve the efficiency of business operations and planning. Describes econometric entities through multiple-equation and single-equation microeconometric models. Explores the process of building and adapting basic microeconometric tools. Presents numerous micromodels based on time-series data and statistical cross-sectional sequences, which can be used in any enterprise. Features numerous real –world applications along with examples drawn from the authors own experience. Is supported by a companion website featuring practice problems and statistical data to aid students to construct and estimate micro models. Features end of chapter exercises with examples present in free software GRETL. This book serves as a valuable resource for students, business management practitioners and researchers in econometric micro-model construction and various decision-making processes.
Utilise Excel 2013 capabilities to build effective financial models Using Excel for Business Analysis, Revised Edition provides practical guidance for anyone looking to build financial models. Whether for business proposals, opportunity evaluation, financial reports, or any other business finance application, this book shows you how to design, create, and test your model, then present your results effectively using Excel 2013. The book opens with a general guide to financial modelling, with each subsequent chapter building skill upon skill until you have a real, working model of your own. Financial tools, features, and functions are covered in detail from a practical perspective, and put in context with application to real-world examples. Each chapter focuses on a different aspect of Excel modelling, including step-by-step instructions that walk you through each feature, and the companion website provides live model worksheets that give you the real hands-on practice you need to start doing your job faster, more efficiently, and with fewer errors. Financial modelling is an invaluable business tool, and Excel 2013 is capable of supporting the most common and useful models most businesses need. This book shows you how to dig deeper into Excel's functionality to craft effective financial models and provide important information that informs good decision-making. Learn financial modelling techniques and best practice Master the formulas and functions that bring your model to life Apply stress testing and sensitivity analysis with advanced conditionals Present your results effectively, whether graphically, orally, or written A deceptively powerful application, Excel supports many hundreds of tools, features, and functions; Using Excel for Business Analysis eliminates the irrelevant to focus on those that are most useful to business finance users, with detailed guidance toward utilisation and best practice.
This practical book shows how to deal with the complicated area of accounting of financial instruments. Containing a huge number of sophisticated worked examples, the book treats this complex subject in a way that gives clear guidance on the subject. In an introductory, controversial overview of the subject, the book highlights the mistakes that both auditing firms and the accounting standard setters are making, and demonstrates the contribution the International Financial Reporting Standards have made to the current credit crisis.
Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.
Introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Enables the reader to replicate the results in the book using R code. Is accompanied by a supporting website featuring examples and case studies in R. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Financial modelling Theory, Implementation and Practice with Matlab Source Jörg Kienitz and Daniel Wetterau Financial Modelling – Theory, Implementation and Practice with MATLAB Source is a unique combination of quantitative techniques, the application to financial problems and programming using Matlab. The book enables the reader to model, design and implement a wide range of financial models for derivatives pricing and asset allocation, providing practitioners with complete financial modelling workflow, from model choice, deriving prices and Greeks using (semi-) analytic and simulation techniques, and calibration even for exotic options. The book is split into three parts. The first part considers financial markets in general and looks at the complex models needed to handle observed structures, reviewing models based on diffusions including stochastic-local volatility models and (pure) jump processes. It shows the possible risk-neutral densities, implied volatility surfaces, option pricing and typical paths for a variety of models including SABR, Heston, Bates, Bates-Hull-White, Displaced-Heston, or stochastic volatility versions of Variance Gamma, respectively Normal Inverse Gaussian models and finally, multi-dimensional models. The stochastic-local-volatility Libor market model with time-dependent parameters is considered and as an application how to price and risk-manage CMS spread products is demonstrated. The second part of the book deals with numerical methods which enables the reader to use the models of the first part for pricing and risk management, covering methods based on direct integration and Fourier transforms, and detailing the implementation of the COS, CONV, Carr-Madan method or Fourier-Space-Time Stepping. This is applied to pricing of European, Bermudan and exotic options as well as the calculation of the Greeks. The Monte Carlo simulation technique is outlined and bridge sampling is discussed in a Gaussian setting and for Lévy processes. Computation of Greeks is covered using likelihood ratio methods and adjoint techniques. A chapter on state-of-the-art optimization algorithms rounds up the toolkit for applying advanced mathematical models to financial problems and the last chapter in this section of the book also serves as an introduction to model risk. The third part is devoted to the usage of Matlab, introducing the software package by describing the basic functions applied for financial engineering. The programming is approached from an object-oriented perspective with examples to propose a framework for calibration, hedging and the adjoint method for calculating Greeks in a Libor market model. Source code used for producing the results and analysing the models is provided on the author's dedicated website, http://www.mathworks.de/matlabcentral/fileexchange/authors/246981.
The financial services industry is undergoing significant change, which has added challenges for institutions assessing their operations and internal controls for regulatory considerations. This 2016 edition of this industry standard resource offers clear and practical guidance of audit and accounting issues such as transfers and servicing, troubled debt restructurings, financing receivables and the allowance for loan losses, and fair value accounting. It also provides direction for institutions assessing their operations and internal controls for regulatory considerations. New and existing regulatory reporting matters are also covered, including updates resulting from the Dodd-Frank Wall Street Reform and Consumer Protection Act and BASEL III implementation. Key Benefits Include: • Coverage of regulatory updates from key industry regulators (FDIC, OCC, Federal Reserve and NCUA) • Coverage of Basel III capital rulings that implement both the Basel III capital framework issued by the Basel Committee on Banking Supervision and certain requirements imposed by the Dodd-Frank Act • Illustrative auditors’ reports • Appendix which highlights FASB ASU No. 2014-09, Revenue from Contracts with Customers (Topic 606) • Appendix which highlights an overview of statements on quality control • A new appendix which highlights FASB ASU No. 2016-02, Leases • A new appendix which highlights FASB’s project on accounting for financial instruments, including an overview of FASB ASU No. 2016-01, Financial Instruments—Overall (Subtopic 825-10): Recognition and Measurement of Financial Assets and Financial Liabilities, and FASB ASU No. 2016-13, Financial Instruments-Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments
Nonlinearity, Complexity and Randomness in Economics presents a variety of papers by leading economists, scientists, and philosophers who focus on different aspects of nonlinearity, complexity and randomness, and their implications for economics. A theme of the book is that economics should be based on algorithmic, computable mathematical foundations. Features an interdisciplinary collection of papers by economists, scientists, and philosophers Presents new approaches to macroeconomic modelling, agent-based modelling, financial markets, and emergent complexity Reveals how economics today must be based on algorithmic, computable mathematical foundations
This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Quantitative Finance is expanding rapidly. One of the aspects of the recent financial crisis is that, given the complexity of financial products, the demand for people with high numeracy skills is likely to grow and this means more recognition will be given to Quantitative Finance in existing and new course structures worldwide. Evidence has suggested that many holders of complex financial securities before the financial crisis did not have in-house experts or rely on a third-party in order to assess the risk exposure of their investments. Therefore, this experience shows the need for better understanding of risk associate with complex financial securities in the future. The Mathematics of Derivative Securities with Applications in MATLAB provides readers with an introduction to probability theory, stochastic calculus and stochastic processes, followed by discussion on the application of that knowledge to solve complex financial problems such as pricing and hedging exotic options, pricing American derivatives, pricing and hedging under stochastic volatility and an introduction to interest rates modelling. The book begins with an overview of MATLAB and the various components that will be used alongside it throughout the textbook. Following this, the first part of the book is an in depth introduction to Probability theory, Stochastic Processes and Ito Calculus and Ito Integral. This is essential to fully understand some of the mathematical concepts used in the following part of the book. The second part focuses on financial engineering and guides the reader through the fundamental theorem of asset pricing using the Black and Scholes Economy and Formula, Options Pricing through European and American style options, summaries of Exotic Options, Stochastic Volatility Models and Interest rate Modelling. Topics covered in this part are explained using MATLAB codes showing how the theoretical models are used practically. Authored from an academic’s perspective, the book discusses complex analytical issues and intricate financial instruments in a way that it is accessible to postgraduate students with or without a previous background in probability theory and finance. It is written to be the ideal primary reference book or a perfect companion to other related works. The book uses clear and detailed mathematical explanation accompanied by examples involving real case scenarios throughout and provides MATLAB codes for a variety of topics.