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Essentials of Time Series for Financial Applications

2018-05-29 Business & Economics
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Release : 2018-05-29
Genre : Business & Economics
Kind : eBook
Book Rating : 100/5 ( reviews)

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Book Synopsis Essentials of Time Series for Financial Applications by : Massimo Guidolin

Download or read book Essentials of Time Series for Financial Applications written by Massimo Guidolin. This book was released on 2018-05-29. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. - Provides practical, hands-on examples in time-series econometrics - Presents a more application-oriented, less technical book on financial econometrics - Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction - Features examples worked out in EViews (9 or higher)

Introduction to Modern Time Series Analysis

2012-10-09 Business & Economics
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Release : 2012-10-09
Genre : Business & Economics
Kind : eBook
Book Rating : 350/5 ( reviews)

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Book Synopsis Introduction to Modern Time Series Analysis by : Gebhard Kirchgässner

Download or read book Introduction to Modern Time Series Analysis written by Gebhard Kirchgässner. This book was released on 2012-10-09. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.

Data Science for Economics and Finance

2021 Application software
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Release : 2021
Genre : Application software
Kind : eBook
Book Rating : 916/5 ( reviews)

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Book Synopsis Data Science for Economics and Finance by : Sergio Consoli

Download or read book Data Science for Economics and Finance written by Sergio Consoli. This book was released on 2021. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Applications of Time Series in Finance and Macroeconomics

2011
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Release : 2011
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Applications of Time Series in Finance and Macroeconomics by : Raul Ibarra Ramirez

Download or read book Applications of Time Series in Finance and Macroeconomics written by Raul Ibarra Ramirez. This book was released on 2011. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains three applications of time series in finance and macroeconomics. The first essay compares the cumulative returns for stocks and bonds at investment horizons from one to ten years by using a test for spatial dominance. Spatial dominance is a variation of stochastic dominance for nonstationary variables. The results suggest that for investment horizons of one year, bonds spatially dominate stocks. In contrast, for investment horizons longer than five years, stocks spatially dominate bonds. This result is consistent with the advice given by practitioners to long term investors of allocating a higher proportion of stocks in their portfolio decisions. The second essay presents a method that allows testing of whether or not an asset stochastically dominates the other when the time horizon is uncertain. In this setup, the expected utility depends on the distribution of the value of the asset as well as the distribution of the time horizon, which together form the weighted spatial distribution. The testing procedure is based on the Kolmogorov Smirnov distance between the empirical weighted spatial distributions. An empirical application is presented assuming that the event of exit time follows an independent Poisson process with constant intensity. The last essay applies a dynamic factor model to generate out-of-sample forecasts for the inflation rate in Mexico. Factor models are useful to summarize the information contained in large datasets. We evaluate the role of using a wide range of macroeconomic variables to forecast inflation, with particular interest on the importance of using the consumer price index disaggregated data. The data set contains 54 macroeconomic series and 243 consumer price subcomponents from 1988 to 2008. The results indicate that factor models outperform the benchmark autoregressive model at horizons of one, two, four and six quarters. It is also found that using disaggregated price data improves forecasting performance.

Introduction to Modern Time Series Analysis

2008-08-27 Business & Economics
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Release : 2008-08-27
Genre : Business & Economics
Kind : eBook
Book Rating : 351/5 ( reviews)

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Book Synopsis Introduction to Modern Time Series Analysis by : Gebhard Kirchgässner

Download or read book Introduction to Modern Time Series Analysis written by Gebhard Kirchgässner. This book was released on 2008-08-27. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.

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