Yield Curve Modeling and Forecasting

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Release : 2013-01-15
Genre : Business & Economics
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Book Rating : 802/5 ( reviews)

Yield Curve Modeling and Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Yield Curve Modeling and Forecasting write by Francis X. Diebold. This book was released on 2013-01-15. Yield Curve Modeling and Forecasting available in PDF, EPUB and Kindle. Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

Yield Curve Modeling and Forecasting Using Semiparametric Factor Dynamics

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Release : 2012
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Yield Curve Modeling and Forecasting Using Semiparametric Factor Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Yield Curve Modeling and Forecasting Using Semiparametric Factor Dynamics write by Wolfgang Karl Härdle. This book was released on 2012. Yield Curve Modeling and Forecasting Using Semiparametric Factor Dynamics available in PDF, EPUB and Kindle.

Yield Curve Dynamics

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Release : 1997
Genre : Business & Economics
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Book Rating : 061/5 ( reviews)

Yield Curve Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Yield Curve Dynamics write by Ronald J. Ryan. This book was released on 1997. Yield Curve Dynamics available in PDF, EPUB and Kindle. � Invaluable to financial professionals � Breakthrough that examines both theory and practical solutions Examines both the advanced theory and practice of these techniques. Topics include: single- and multi-factor models; applying yield-curve modeling to risk management; forecasting short-term interest rates; unique yield-curve volatility; and trading strategies.

Yield Curve Modeling

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Release : 2005-06-23
Genre : Business & Economics
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Book Rating : 743/5 ( reviews)

Yield Curve Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Yield Curve Modeling write by Y. Stander. This book was released on 2005-06-23. Yield Curve Modeling available in PDF, EPUB and Kindle. This book will give the reader insight into how to model yield curves in our incomplete and imperfect financial markets. An extensive list of yield curve models are shown and discussed. Using actual market instruments, these models are then applied and the different yield curves are compared. It is assumed that the reader has a basic understanding of the financial instruments available in the market. Various issues that have to be taken into account in practice are discussed, like daycount conventions, business-day rules, the credit quality of the instrument and liquidity to name but a few. It is also shown how yield curves can be used to estimate credit spreads and country risk premiums. Creating a yield curve model has some implications in risk management. Specifically - the model, operational, liquidity and basis risks are discussed.

Modeling and Forecasting the Yield Curve Under Model Uncertainty

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Release : 2009
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Modeling and Forecasting the Yield Curve Under Model Uncertainty - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Modeling and Forecasting the Yield Curve Under Model Uncertainty write by Francesco Donati. This book was released on 2009. Modeling and Forecasting the Yield Curve Under Model Uncertainty available in PDF, EPUB and Kindle. We propose a methodology that permits to investigate and forecast the behavior of a variable and its determinants in real time, both in the time and in the frequency domain, starting from a model designed in the time domain, which makes the presentation and evaluation of the results straightforward. This paper applies the methodology to the yield curve. We extract all the shocks affecting the forward rates and the yields and we divide them into three disjoint classes: 1) long-run shocks giving rise to possibly permanent effects, 2) medium-run forces and 3) short-run forces giving rise to transitory effects. These forces drive the low-, medium- and high-frequency component, respectively, composing the time series of the variables used in the model. We explicitly model and estimate such cause-and-effect relationships. The analysis of the shocks and the frequency components provides a timely and comprehensive overview of the nature of the movements in the yields. Furthermore, using the forecast of the frequency components to forecast the yields enhances forecast accuracy, also at long prediction horizons. To perform the frequency decompositions, to identify the forces governing the evolution of the model variables, and to perform the out-of-sample forecasts we use a dynamic filter whose embedded feedback control corrects for model uncertainty.