Tackling the cybersecurity challenge is a matter of survival for society at large. Cyber attacks are rapidly increasing in sophistication and magnitude-and in their destructive potential. New threats emerge regularly, the last few years having seen a ransomware boom and distributed denial-of-service attacks leveraging the Internet of Things. For organisations, the use of cybersecurity risk management is essential in order to manage these threats. Yet current frameworks have drawbacks which can lead to the suboptimal allocation of cybersecurity resources. Cyber insurance has been touted as part of the solution - based on the idea that insurers can incentivize companies to improve their cybersecurity by offering premium discounts - but cyber insurance levels remain limited. This is because companies have difficulty determining which cyber insurance products to purchase, and insurance companies struggle to accurately assess cyber risk and thus develop cyber insurance products. To deal with these challenges, this volume presents new models for cybersecurity risk management, partly based on the use of cyber insurance. It contains: A set of mathematical models for cybersecurity risk management, including (i) a model to assist companies in determining their optimal budget allocation between security products and cyber insurance and (ii) a model to assist insurers in designing cyber insurance products. The models use adversarial risk analysis to account for the behavior of threat actors (as well as the behavior of companies and insurers). To inform these models, we draw on psychological and behavioural economics studies of decision-making by individuals regarding cybersecurity and cyber insurance. We also draw on organizational decision-making studies involving cybersecurity and cyber insurance. Its theoretical and methodological findings will appeal to researchers across a wide range of cybersecurity-related disciplines including risk and decision analysis, analytics, technology management, actuarial sciences, behavioural sciences, and economics. The practical findings will help cybersecurity professionals and insurers enhance cybersecurity and cyber insurance, thus benefiting society as a whole. This book grew out of a two-year European Union-funded project under Horizons 2020, called CYBECO (Supporting Cyber Insurance from a Behavioral Choice Perspective).

Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. Web ResourceMore detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems. Although many books exist that address the theoretical or practical aspects of cost-effectiveness analysis, this book differentiates itself from the competition by detailing how to apply health economic evaluation techniques in a clinical trial context, from both academic and pharmaceutical/commercial perspectives. It also includes a special chapter for clinical trials in Cancer. Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement is not just about performing cost-effectiveness analyses. It also emphasizes the strategic importance of economic evaluation and offers guidance and advice on the complex factors at play before, during, and after an economic evaluation. Filled with detailed examples, the book bridges the gap between applications of economic evaluation in industry (mainly pharmaceutical) and what students may learn in university courses. It provides readers with access to SAS and STATA code. In addition, Windows-based software for sample size and value of information analysis is available free of charge-making it a valuable resource for students considering a career in this field or for those who simply wish to know more about applying economic evaluation techniques. The book includes coverage of trial design, case report form design, quality of life measures, sample sizes, submissions to regulatory authorities for reimbursement, Markov models, cohort models, and decision trees. Examples and case studies are provided at the end of each chapter. Presenting first-hand insights into how economic evaluations are performed from a drug development perspective, the book supplies readers with the foundation required to succeed in an environment where clinical trials and cost-effectiveness of new treatments are central. It also includes thought-provoking exercises for use in classroom and seminar discussions.

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

This book explores how econometric modelling can be used to provide valuable insight into international housing markets. Initially describing the role of econometrics modelling in real estate market research and how it has developed in recent years, the book goes on to compare and contrast the impact of various macroeconomic factors on developed and developing housing markets. Explaining the similarities and differences in the impact of financial crises on housing markets around the world, the author's econometric analysis of housing markets across the world provides a broad and nuanced perspective on the impact of both international financial markets and local macro economy on housing markets. With discussion of countries such as China, Germany, UK, US and South Africa, the lessons learned will be of interest to scholars of Real Estate economics around the world.

This book contains the most complete set of the Chinese national income and its components based on system of national accounts. It points out some fundamental issues concerning the estimation of China's national income and it is intended to the students of the field of China study around the world.

Pathwise estimation and inference for diffusion market models discusses contemporary techniques for inferring, from options and bond prices, the market participants' aggregate view on important financial parameters such as implied volatility, discount rate, future interest rate, and their uncertainty thereof. The focus is on the pathwise inference methods that are applicable to a sole path of the observed prices and do not require the observation of an ensemble of such paths. This book is pitched at the level of senior undergraduate students undertaking research at honors year, and postgraduate candidates undertaking Master's or PhD degree by research. From a research perspective, this book reaches out to academic researchers from backgrounds as diverse as mathematics and probability, econometrics and statistics, and computational mathematics and optimization whose interest lie in analysis and modelling of financial market data from a multi-disciplinary approach. Additionally, this book is also aimed at financial market practitioners participating in capital market facing businesses who seek to keep abreast with and draw inspiration from novel approaches in market data analysis. The first two chapters of the book contains introductory material on stochastic analysis and the classical diffusion stock market models. The remaining chapters discuss more special stock and bond market models and special methods of pathwise inference for market parameter for different models. The final chapter describes applications of numerical methods of inference of bond market parameters to forecasting of short rate. Nikolai Dokuchaev is an associate professor in Mathematics and Statistics at Curtin University. His research interests include mathematical and statistical finance, stochastic analysis, PDEs, control, and signal processing. Lin Yee Hin is a practitioner in the capital market facing industry. His research interests include econometrics, non-parametric regression, and scientific computing.

Originally published in 1951, this volume reprints the classic work written by one of the leading global econometricians.
Econometrics is structured as followed:
Part 1 explains the relationship of Econometrics to Economics and Statistics.
Part 2 outlines the process of formulating economic hypotheses mathematically and of subjecting them to a statistical test.
Part 3 deals with the various component equations of the economic system - the psychic reactions, technical relations and reactions of business life and describes the process of setting up an economic model of the system as a whole.
Part 4 llustrates the use of econometric methods for policy purposes.

Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.
*Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods
*Insights within this volume can be applied to economics, finance and marketing disciplines

The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and general methodological issues.
*Every volume contains contributions from leading researchers
*Each Handbook presents an accurate, self-contained survey of a particular topic
*The series provides comprehensive and accessible surveys

Practically all donor countries that give aid claim to do so on the basis on the recipient's good governance, but do these claims have a real impact on the allocation of aid? Are democratic, human rights-respecting, countries with low levels of corruption and military expenditures actually likely to receive more aid than other countries?
Using econometric analysis, the author examines the factors that really determine the patterns of aid giving. The author analyses such examples as:
* aggregate aid flows
* aid from multilateral organisations such as the EU and the UN
* aid from bilateral donors such as Germany, Japan, the US as well as Arab donors.
This concise, well argued and well researched book will be a great read for students, academics and policy-makers involved in development studies, economics and international relations.

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

Today, most money is credit money, created by commercial banks. While credit can finance innovation, excessive credit can lead to boom/bust cycles, such as the recent financial crisis. This highlights how the organization of our monetary system is crucial to stability. One way to achieve this is by separating the unit of account from the medium of exchange and in pre-modern Europe, such a separation existed. This new volume examines this idea of monetary separation and this history of monetary arrangements in the North and Baltic Seas region, from the Hanseatic League onwards. This book provides a theoretical analysis of four historical cases in the Baltic and North Seas region, with a view to examining evolution of monetary arrangements from a new monetary economics perspective. Since the objective exhange value of money (its purchasing power), reflects subjective individual valuations of commodities, the author assesses these historical cases by means of exchange rates. Using theories from new monetary economics , the book explores how the units of account and their media of exchange evolved as social conventions, and offers new insight into the separation between the two. Through this exploration, it puts forward that money is a social institution, a clearing device for the settlement of accounts, and so the value of money, or a separate unit of account, ultimately results from the size of its network of users. The History of Money and Monetary Arrangements offers a highly original new insight into monetary arrangments as an evolutionary process. It will be of great interest to an international audience of scholars and students, including those with an interest in economic history, evolutionary economics and new monetary economics.

Market Analysis for Real Estate is a comprehensive introduction to how real estate markets work and the analytical tools and techniques that can be used to identify and interpret market signals. The markets for space and varied property assets, including residential, office, retail, and industrial, are presented, analyzed, and integrated into a complete understanding of the role of real estate markets within the workings of contemporary urban economies. Unlike other books on market analysis, the economic and financial theory in this book is rigorous and well integrated with the specifics of the real estate market. Furthermore, it is thoroughly explained as it assumes no previous coursework in economics or finance on the part of the reader. The theoretical discussion is backed up with numerous real estate case study examples and problems, which are presented throughout the text to assist both student and teacher.

Including discussion questions, exercises, several web links, and online slides, this textbook is suitable for use on a variety of degree programs in real estate, finance, business, planning, and economics at undergraduate and MSc/MBA level. It is also a useful primer for professionals in these disciplines.

Table of Contents

PART A: INTRODUCTION 1. Market Analysis In Perspective 2. Real Estate Economics PART B: METROPOLITAN GROWTH ANALYSIS 3. Metropolitan Growth Patterns 4. Analyzing Metropolitan Economies PART C: ANALYZING RESIDENTIAL REAL ESTATE MARKETS 5. Residential Real Estate Markets 6. Macroeconomic Analysis of Residential Real Estate Markets: Accounting Techniques 7. Macroeconomic Analysis of Residential Real Estate Markets: The Basics of the Econometric Approach 8. Macroeconomic Analysis of Residential Real Estate Markets: Applying the Econometric Approach 9. Analyzing Residential Projects: A Micro Perspective 10. Analysis of Residential Real Estate Markets: An Example PART D: ANALYZING THE MARKET FOR RETAIL SPACE 11. Retail Markets and Retail Market Studies 12. Analyzing the Market for Retail Space 13. Analyzing the Market for Retail Space: Synthesis and Market Studies PART E: OFFICE MARKET ANALYSIS 14. The Market for Office Space 15. Office Market Analysis: A Macro Perspective 16. Micro Analysis of Office Markets 17. Office Market Analysis: Synthesis and Market Studies PART F: INDUSTRIAL MARKET ANALYSIS 18. Industrial Space Market PART G: DATA SOURCES 19. Data Sources for Real Estate Market Analysis

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

This concise textbook presents students with all they need for advancing in mathematical economics. Detailed yet student-friendly, Vohra's book contains chapters in, amongst others:

* Feasibility
* Convex Sets
* Linear and Non-linear Programming
* Lattices and Supermodularity.

Higher level undergraduates as well as postgraduate students in mathematical economics will find this book extremely useful in their development as economists.

This concise textbook presents students with all they need for advancing in mathematical economics. Detailed yet student-friendly, Vohra's book contains chapters in, amongst others:

* Feasibility
* Convex Sets
* Linear and Non-linear Programming
* Lattices and Supermodularity.

Higher level undergraduates as well as postgraduate students in mathematical economics will find this book extremely useful in their development as economists.

Quantitative Modeling of Derivative Securities demonstrates how to take the basic ideas of arbitrage theory and apply them - in a very concrete way - to the design and analysis of financial products. Based primarily (but not exclusively) on the analysis of derivatives, the book emphasizes relative-value and hedging ideas applied to different financial instruments. Using a "financial engineering approach," the theory is developed progressively, focusing on specific aspects of pricing and hedging and with problems that the technical analyst or trader has to consider in practice. More than just an introductory text, the reader who has mastered the contents of this one book will have breached the gap separating the novice from the technical and research literature.

Originally published in 1951, this volume reprints the classic work written by one of the leading global econometricians.
Econometrics is structured as followed:
Part 1 explains the relationship of Econometrics to Economics and Statistics.
Part 2 outlines the process of formulating economic hypotheses mathematically and of subjecting them to a statistical test.
Part 3 deals with the various component equations of the economic system - the psychic reactions, technical relations and reactions of business life and describes the process of setting up an economic model of the system as a whole.
Part 4 llustrates the use of econometric methods for policy purposes.

The book examines the development and the dynamics of the personal distribution of income in Germany, Great Britain, Sweden and the United States and some other OECD countries. Starting with the distribution of labour income, the issue is then expanded to include all monetary incomes of private households and to adjust for household size by an equivalence scale. Some authors analyse one country in detail by decomposing aggregate inequality measures, other authors focus on direct comparisons of some features of the income distribution in Germany with those in Great Britain or in the United States. The results suggest dominant influences of unemployment as well as of tax and transfer policies and different welfare regimes, respectively, but also show that our knowledge about distributional processes is still limited.

This new book will be welcomed by econometricians and students of econometrics everywhere. Introducing discrete time modelling techniques and bridging the gap between economics and econometric literature, this ambitious book is sure to be an invaluable resource for all those to whom the terms unit roots, cointegration and error correction forms, chaos theory and random walks are recognisable if not yet fully understood.