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Asian Development Outlook: 2021
International Comparison Program for As

2021 International Comparison Program for Asia and the Pacific

Purchasing Power Parities and Real Expenditures-A Summary Report

Derivatives: Mathematical Foundations for Finance Students is written for students of finance and actuarial science who want to understand derivatives not just as formulas but as ideas that make sense. The goal is to build the mathematical foundations up step by step - starting from the familiar territory of first-year undergraduate probability and statistics and moving all the way to stochastic calculus and the principles of risk-neutral pricing, which form the heart of modern financial mathematics.This text will enable the reader to:¿ understand models of asset price behavior and apply stochastic calculus ¿ use stochastic simulations in R to investigate processes and price options ¿ price options with the binomial model ¿ derive solutions to the Black-Scholes equation using risk-neutral pricing ¿ price interest-rate derivatives using Black's model, short-rate models and the Heath-Jarrow-Morton (HJM) framework ¿ tackle quantitative finance exam questions.It incorporates a range of learning features to aid student understanding, including boxed examples, end-of-chapter summaries, selected questions from Society of Actuaries (US) Quantitative Finance examinations and further reading suggestions. The book is also supported by a suite of digital learning resources, including PowerPoint slides, multiple choice questions, instructor manual/advice document for lecturers and a test bank.This book will appeal to both undergraduate and postgraduate students who wish to understand the principles of stochastic calculus and option pricing.

Mathematical Foundations for Finance Students

Derivatives

Unlock the full potential of quantitative trading with Prescriptions for Quant Traders Using R-a practical, hands-on guide for turning data into trading decisions.This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. Organized into ten structured parts, it delivers step-by-step "prescriptions" using R-script-driven tasks that solve real-world trading problems. Each prescription is designed to be immediately actionable, so readers can move from concept to implementation without guesswork.What makes this book different is its clarity, utility, and its link to online video walk-throughs of R scripts. The text flows seamlessly without interruptions, yet a comprehensive bibliography ensures depth and rigor. Every chapter blends explanation with executable code, enabling traders to:<ul> <li> Design systematic trading strategies in R - and understand why they work.</li> <li> Apply robust statistical and econometric methods to financial data for better predictions.</li> <li> Evaluate risk and performance metrics to refine strategies with confidence.</li> <li> Automate workflows so insights move faster from model to market.</li> </ul>Whether you are a retail trader aiming to compete with professionals, a financial analyst seeking sharper models, or a data scientist expanding into trading, this book bridges the gap between theory and practice.Readers will find special value in the prescription-based approach. Just as a doctor prescribes remedies, these scripts deliver targeted solutions to trading challenges-from portfolio optimization and volatility forecasting to Bayesian inference and machine learning integration.In a marketplace where retail participation continues to grow, Prescriptions for Quant Traders Using R equips you with the knowledge, tools, and confidence to approach trading systematically. The result: deeper insights, stronger discipline, and strategies that stand the test of real-world markets.Whether you're coding your first model or refining a sophisticated strategy, this book provides the prescriptions you need to succeed.

Videos & Scripts

Prescriptions for Quant Traders Using R

World Scientific Lecture Notes In Economics And Policy

This book attempts to understand the operation and properties of 'market economies'. We do this by carefully examining, refining and in some cases extending the major theorems that Microeconomics has established in response to the Walrasian conjecture. We critically evaluate theorems and results, that assert the existence, uniqueness, stability, optimality and empirical relevance of Walrasian equilibrium states. As a result of our work, we attempt to comment on the wisdom of recommending that socio-economic systems operate as a collection of deregulated competitive markets.

Lectures In General Equilibrium Microeconomics

A paradigm-shifting textbook that teaches graduate students in economics how to use economic models to interpret data. Offering a new pedagogical paradigm, this textbook delivers a ready-made economics course covering the general concepts that unify different approaches to bringing economic models to data. Jesse Shapiro presents core ideas in quantitative economics as an abstract, cohesive whole and introduces canonical models that can be used across a wide range of applications. This course-in-a-book exposes students to stylized applications of these models that illuminate their foundations as well as recent research articles demonstrating the models’ broad applicability. <ul><li>Provides a coherent, generalized approach to making model-based inferences in empirical economics</li><li>Introduces central concepts and canonical models in quantitative economics</li><li>Features applications of modern research topics</li><li>Suits self-paced learning and classroom use alike</li></ul>

Introduction to Quantitative Economics

An award-winning economic journalist on why the US dollar is positioned to maintain global primacy—and what that means for America and the world &nbsp; “Magisterial.”—Edward Chancellor, Reuters &nbsp; Prophecies that the dollar will lose its status as the world’s dominant currency have echoed for decades—and are increasing in volume. Cryptocurrency enthusiasts claim that Bitcoin or other blockchain-based monetary units will replace the dollar. Foreign policy hawks warn that China’s renminbi poses a lethal threat to the greenback. And sound money zealots predict that mounting US debt and inflation will surely erode the dollar’s value to the point of irrelevancy. &nbsp; Contra the doomsayers, Paul Blustein shows that the dollar’s standing atop the world’s currency pyramid is impregnable, barring catastrophic policy missteps by the US government. Recounting how the United States has wielded the dollar to impose devastating sanctions against adversaries, Blustein explains that although targets such as Russia have found ways to limit the damage, Washington’s financial weaponry will retain potency long into the future. His message, however, is that America must not be complacent about the dollar; the great power that its supremacy confers comes with commensurate responsibility.

The Past and Future of the World's Dominant Currency

King Dollar

Econometric Society Monographs

A Framework for Economists with Policy Aspirations

Minimalist Market Design

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

FOUNDATIONS OF FINANCIAL DATA SCIENCE: METHODS, MODELS, AND APPLICATIONS

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

Handbook of U.S. Labor Statistics

The Handbook of U.S. Labor Statistics is recognized as an authoritative resource on the U.S. labor force. It continues and enhances the Bureau of Labor Statistics' (BLS) discontinued publication, Labor Statistics. It allows the user to understand recent developments as well as to compare today's economy with that of the past. In addition, it includes the most current employment projections through 2035. The Handbook is a comprehensive reference providing an abundance of information on a variety of topics. In addition to providing statistics on employment, unemployment, and prices, it includes information on topics such as: ·Earnings ·Productivity ·Consumer expenditures ·Occupational safety and health ·Union membership ·Working poor ·Recent trends in the labor force ·And much more! In addition to over 215 tables that present practical data, the Handbook provides: ·Introductory material for each chapter that contains highlights of salient data and figures that call attention to noteworthy trends in the data ·Notes and definitions, which contain concise descriptions of the data sources, concepts, definitions, and methodology from which the data are derived ·References to more comprehensive reports which provide additional data and more extensive descriptions of estimation methods, sampling, and reliability measures

Employment, Earnings, Prices, Productivity, and Other Labor Data

Handbook of U.S. Labor Statistics 2027

2021 International Comparison Program for Asia and the Pacific

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