This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.
Models and Strategies for Exchange Rate ForecastingMichael R. RosenbergGetting an accurate exchange rate is critical for any company doing business in today's global economy. Exchange Rate Determination--written by the number one-ranked foreign exchange team in the world--examines the methods used to accurately and profitably forecast foreign exchange rates. This hands-on guidebook uses extensive charts and tables to examine currency option markets, productivity trends and exchange rates; technical analysis methods to improve currency forecasting accuracy; and more.
This text explains the methods and aspects of exchange rate forecasting, including purchasing power, parity, interest rate differentials and technical analysis. Guidelines for reducing risk with forecasting strategies are included, as are techniques for co
Forecasting exchange rates is a variable that preoccupies economists, businesses and governments, being more critical to more people than any other variable. In Exchange Rate Forecasting the author sets out to provide a concise survey of the techniques of forecasting - bringing together the various forecasting methods and applying them to the exchange rate in a highly accessible and readable manner. Highly practical in approach, the book provides an understanding of the techniques of forecasting with an emphasis on its applications and use in business decision-making, such as hedging, speculation, investment, financing and capital budgeting. In addition, the author also considers recent developments in the field, notably neural networks and chaos, again, with easy-to-understand explanations of these "rocket science" areas. The practical approach to forecasting is also reflected in the number of examples that pepper the text, whilst descriptions of some of the software packages that are used in practice to generate forecasts are also provided.
This text summarizes the important new thinking on financial market forecasting and on the statistical modeling of non-stationary series in a clear and readable manner. The emphasis throughout is on real-life examples using data from a wide variety of countries and sources.
Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. Neural Networks in Business Forecasting provides researchers and practitioners with some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
The recent financial crisis has troubled the US, Europe, and beyond, and is indicative of the integrated world in which we live. Today, transactions take place with the use of foreign currencies, and their values affect the nations' economies and their citizens' welfare. Exchange Rates and International Financial Economics provides readers with the historic, theoretical, and practical knowledge of these relative prices among currencies. While much of the previous work on the topic has been simply descriptive or theoretical, Kallianiotis gives a unique and intimate understanding of international exchange rates and their place in an increasingly globalized world.
The medium-term predictability of exchange rate movements is examined using three models of fundamentals: purchasing power parity, the monetary model, and uncovered interest parity. While the first two approaches yield favorable in-sample results, these largely reflect finite-sample estimation biases. Adjusting for these biases, there is little evidence of predictability, consistent with the lack of systematic improvement in out-of-sample forecasting performance relative to a random walk. Uncovered interest parity fares better at long horizons, but reflects information already embodied in market prices; in this sense, it may not be useful as an indicator of exchange rate misalignment. While more elaborate models of fundamentals might have better medium-term forecasting properties, careful attention must be paid to finite-sample biases in assessing predictability.
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).
A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike
