Download or read book Predicting the Yield Curve Using Forecast Combinations written by João Caldeira and published by . This book was released on 2013 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine the statistical accuracy and economic value of modelling and forecasting the term structure of interest rates using forecast combinations. We adopt five alternative methods to combine point forecasts from several univariate and multivariate autoregressive specifications, as well as from factor models for the yield curve such as the dynamic versions of the Nelson-Siegel and Svensson specifications. Moreover, we conduct a detailed performance evaluation based not only on statistical measures of forecast accuracy, but also an economic criteria like Sharpe ratios of optimal mean-variance fixed income portfolios constructed based upon forecasts from individual models and their alternative combinations. Our empirical application based on a large panel of Brazilian interest rate future contracts with different maturities shows that combined forecasts consistently outperform individual models in several instances, specially when economic criteria are taken into account.

Download or read book Yield Curve Modeling and Forecasting written by Francis X. Diebold and published by Princeton University Press. This book was released on 2013-01-15 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Forecast Combination for U S Recessions with Real Time Data written by Laurent L. Pauwels and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.

Download or read book Forecasting Financial Time Series Using Model Averaging written by Francesco Ravazzolo and published by Rozenberg Publishers. This book was released on 2007 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Believing in a single model may be dangerous, and addressing model uncertainty by averaging different models in making forecasts may be very beneficial. In this thesis we focus on forecasting financial time series using model averaging schemes as a way to produce optimal forecasts. We derive and discuss in simulation exercises and empirical applications model averaging techniques that can reproduce stylized facts of financial time series, such as low predictability and time-varying patterns. We emphasize that model averaging is not a "magic" methodology which solves a priori problems of poorly forecasting. Averaging techniques have an essential requirement: individual models have to fit data. In the first section we provide a general outline of the thesis and its contributions to previ ous research. In Chapter 2 we focus on the use of time varying model weight combinations. In Chapter 3, we extend the analysis in the previous chapter to a new Bayesian averaging scheme that models structural instability carefully. In Chapter 4 we focus on forecasting the term structure of U.S. interest rates. In Chapter 5 we attempt to shed more light on forecasting performance of stochastic day-ahead price models. We examine six stochastic price models to forecast day-ahead prices of the two most active power exchanges in the world: the Nordic Power Exchange and the Amsterdam Power Exchange. Three of these forecasting models include weather forecasts. To sum up, the research finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included.

Download or read book Modelling and forecasting stock return volatility and the term structure of interest rates written by Michiel de Pooter and published by Rozenberg Publishers. This book was released on 2007 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.

Download or read book Yield Curve Forecast Combinations Based on Bond Portfolio Performance written by João Caldeira and published by . This book was released on 2017 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose an economically motivated forecast combination strategy in which model weights are related to portfolio returns obtained by a given forecast model. An empirical application based on an optimal mean-variance bond portfolio problem is used to highlight the advantages of the proposed approach with respect to combination methods based on statistical measures of forecast accuracy. We compute average net excess returns, standard deviation, and the Sharpe ratio of bond portfolios obtained with 9 alternative yield curve specifications, as well as with 12 different forecast combination strategies. Return-based forecast combination schemes clearly outperformed approaches based on statistical measures of forecast accuracy in terms of economic criteria. Moreover, return-based approaches that dynamically selects only the model with highest weight each period and discard all other models delivered even better results, evidencing not only the advantages of trimming forecast combinations but also the ability of the proposed approach to detect best performing models. To analyze the robustness of our results, different levels of risk aversion and a different data set are considered.

Download or read book Forecasting the Yield Curve written by Christian Scheitlin and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis is to forecast the US Treasury yield curve. In order to do so, the yield curve will first be modeled by the Nelson-Siegel (1987) method with the Diebold and Li (2006) extension and then forecasted. The data used is provided by Gürkaynak, Sack, and Wright (2006). The large dataset consists of fitted yields of US Treasury bonds. The conclusion of this thesis is that there is evidence that the Diebold and Li (2006) method can be applied to the dataset used. The forecasting results show mostly the correct change in direction of the yield curve but lack accuracy. The forecasting ability is quite well considering that the model does not include any macro-economic factors which are proven to influence the yield curve largely according to the results by Diebold, Piazzesi, and Rudebusch (2005).

Download or read book Forecasting the Term Structure of Government Bond Yields written by Francis X. Diebold and published by . This book was released on 2003 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor the equilibrium approach, which focuses on time-series dynamics (primarily those of the instantaneous rate) but pays comparatively little attention to fitting the entire cross section at any given time and has been shown to forecast poorly. Instead, we use variations on the Nelson-Siegel exponential components framework to model the entire yield curve, period-by-period, as a three dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.

Download or read book Predicting Output Using the Entire Yield Curve written by Azamat Abdymomunov and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many studies find that yields for government bonds predict real economic activity. Most of these studies use the yield spread, defined as the difference between two yields of specific maturities, to predict output. In this paper, I propose a different approach that makes use of information contained in the entire term structure of U.S. Treasury yields to predict U.S. real GDP growth. My proposed dynamic yield curve model produces better out-of-sample forecasts of real GDP than those produced by the traditional yield spread model. The main source of this improvement is in the dynamic approach to constructing forecasts versus the direct forecasting approach used in the traditional yield spread model. Although the predictive power of the yield curve for output is concentrated in the yield spread, there is also a gain from using information in the curvature factor for the real GDP growth prediction.

Download or read book Updating the Yield Curve to Analyst s Views written by Leonardo M. Nogueira and published by . This book was released on 2008 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fixed income analysts deal constantly with the challenge of mapping their expectations about the macroeconomic environment into movements of the yield curve. This paper assumes that an analyst is able to provide a forecast of a few benchmark yields or combinations of yields. Then it derives a forecast of the entire yield curve that is consistent with the analyst's views, and computes the expected return of a bond portfolio in that scenario. We consider examples of forecasting the government bond yield curves of the United States, the Eurozone and the United Kingdom. More generally, the proposed model allows the analyst to express views on any set of correlated random variables (such as stocks, commodities, credit spreads, etc.) and to derive forecasts that are consistent with the views. The model builds on the theory of principal component analysis (PCA), can be easily extended to other markets and has no restrictions on the number of forecast variables or the number of views. A typical application is in scenario analysis, when the analyst could split the problem of forecasting the yield curve into two parts: one in which the expected developments of the macroeconomic environment are used to forecast movements of a few benchmark yields; and another part where the model derived in this paper is used to estimate the impact of the analyst's views on the entire yield curve of one country or of several countries.

Download or read book A Non Linear Forecast Combination Procedure for Binary Outcomes written by Kajal Lahiri and published by . This book was released on 2015 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators -- the ISM new order diffusion index and the yield curve spread -- to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up.

Download or read book Essays on Combination of Forecasts written by Huiyu Huang and published by . This book was released on 2007 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Anchoring the Yield Curve Using Survey Expectations written by Carlo Altavilla and published by . This book was released on 2013 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.

Download or read book Forecasting Yield Curves with Survey Information written by Jack Clark Francis and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Campbell and Shiller [1991], Cochrane and Piazzesi [2005], Diebold and Li [2006] and many others have shown that today's yield curve possesses significant information about the dynamics of future yields. Vector autoregression (VAR) models can forecast interest rates with different maturities, but these forecasts can contain arbitrage opportunities. To avoid arbitrage it is important to use affine term structure models. This paper investigates the expectations of professional economic forecasters for the purpose of out-of-sample forecasting. The results suggest that survey data from professional economic forecasters can generate significant improvements in interest rate forecasts up to one year ahead.

Download or read book Modeling and Forecasting the Yield Curve Under Model Uncertainty written by Francesco Donati and published by . This book was released on 2009 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Forecasting the Yield Curve in a Data rich Environment Using the Factor augmented Nelson Siegel Model written by and published by . This book was released on 2010 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Yield Curve and Real Activity written by Zuliu Hu and published by . This book was released on 2006 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The financial press frequently suggest that the shape of yield curve reflects information about the prospects of the economy. This paper attempts to formalize the link between the yield curve and the real economic activity. A closed-form formula for the term structure of interest rates is derived. It is shown that the term structure embodies the market`s expectation about changes in the macroeconomic fundamental--the growth in real aggregate output of the economy. The paper then documents the use of bond market data for predicting GDP growth in the G-7 industrial countries. The results suggest that a simple measure of the slope of the yield curve, namely the yield spread, serves as a good predictor of future economic growth. The out-of-sample forecasting performance of the yield spread compares favorably with that of the alternative stock price-based model and a univariate time series (ARMA) model. One practical implication is that it may be useful to add some measure of the term structure to the list of leading indicators.