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Author : Michiel De Pooter
Publisher :
ISBN 13 :
Total Pages : 49 pages
Book Rating : 4.:/5 (728 download)
Download or read book Term Structure Forecasting Using Macro Factors and Forecast Combination written by Michiel De Pooter and published by . This book was released on 2010 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michiel de Pooter
Publisher : Rozenberg Publishers
ISBN 13 : 9051709153
Total Pages : 286 pages
Book Rating : 4.0/5 (517 download)
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.
Author : Michiel De Pooter
Publisher :
ISBN 13 :
Total Pages : 52 pages
Book Rating : 4.:/5 (129 download)
Download or read book Predicting the Term Structure of Interest Rates written by Michiel De Pooter and published by . This book was released on 2010 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: We assess the relevance of parameter uncertainty, model uncertainty, and macroeconomic information for forecasting the term structure of interest rates. We study parameter uncertainty by comparing Bayesian inference with frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. We incorporate macroeconomic information in yield curve models by extracting common factors from a large panel of macro series. Our results show that accounting for parameter uncertainty does not improve the forecast performance of individual models. The predictive accuracy of single models varies over time considerably and we demonstrate that mitigating model uncertainty by combining forecasts leads to substantial gains in predictability. Combining forecasts using a weighting method that is based on relative historical performance results in highly accurate forecasts. The gains in terms of forecast performance are substantial, especially for longer maturities, and are consistent over time. In addition, we find that adding macroeconomic factors generally is beneficial for improving out-of-sample forecasts.
Author : Francesco Ravazzolo
Publisher : Rozenberg Publishers
ISBN 13 : 9051709145
Total Pages : 198 pages
Book Rating : 4.0/5 (517 download)
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.
Author : Hwagyun Kim
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (13 download)
Download or read book Term Structure Dynamics with Macro Factors Using High Frequency Data written by Hwagyun Kim and published by . This book was released on 2017 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper empirically studies the role of macro factors in explaining and predicting daily bond yields. In general, macro-finance models use low-frequency data to match with macroeconomic variables available only at low frequencies. To deal with this, we construct and estimate a tractable no-arbitrage affine model with both conventional latent factors and macro factors by imposing cross-equation restrictions on the daily yields of bonds with different maturities, credit risks, and inflation indexation. The estimation results using both the US and UK data show that the estimated macro factors significantly predict actual inflation and the output gap. In addition, our daily macro term structure model forecasts better than no-arbitrage models with only latent factors as well as other statistical models.
Author : Biancen Xie
Publisher :
ISBN 13 :
Total Pages : 111 pages
Book Rating : 4.:/5 (11 download)
Download or read book Essays on Macro-finance Affine Term Structure Models written by Biancen Xie and published by . This book was released on 2019 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: In my dissertation, I focus on theoretical affine term structure models and the development of Bayesian econometric methods to estimate them.In the first Chapter, we address the question of which unspanned macroeconomic factors are the best in the class of macro-finance Gaussian affine term structure models. To answer this question, we extend Joslin, Priebsch, and Singleton (2014) in two dimensions. First, following Ang and Piazzesi (2003) and Chib and Ergashev (2009), three latent factors, instead of the first three principal components of the yield curve, are used to represent the level, slope and curvature of the yield curve. Second we postulate a grand affine model that includes all the macro-variables in contention. Specific models are then derived from this grand model by letting each of the macro-variables play the role of a relevant macro factor (i.e. by affecting the time-varying market price of factor risks), or the role of an irrelevant macro factor (having no effect on the market price of factor risks). The Bayesian marginal likelihoods of the resulting models are computed by an efficient Markov chain Monte Carlo algorithm and the method of Chib (1995) and Chib and Jeliazkov (2001). Given eight common macro factors, our comparison of 28=256 affine models shows that the most relevant macro factors for the U.S. yield curve are the federal funds rate, industrial production, total capacity utilization, and housing sales. We also show that the best supported model substantially improves out-of-sample yield curve forecasting and the understanding of term-premium.The second Chapter considers the question of which unspanned macro factors can improve prediction in arbitrage-free affine term structure models and convert return forecasts into economic gains. To achieve this, we develop a Bayesian framework for incorporating different combinations of macro variables within an affine term structure framework. Then each specific model within the framework is evaluated statistically and economically. For the statistical evaluation, we examine its out-of-sample yield density forecasting. The economic value of each model is compared in terms of the bond portfolio choice of a Bayesian risk- averse investor. We consider two main kinds of macro factors: representative macro factors in Chib et al. (2019) and principal component macro factors in Ludvigson and Ng (2009b). Our empirical results show that regardless of macro dataset we use(either Chib et al. (2019) or Ludvigson and Ng (2009b)), macro factor in real economic activity, financial sector and price index will help generate notable gains in out-of-sample forecast. Such gains in predictive accuracy translate into higher portfolio returns after accounting for estimation error and model uncertainty. In contrast, incorporating redundant macro variables into the affine term structure models can even decrease utility and prediction accuracy for investors. In addition, given the data sample we consider in the Chapter, we also find that principle component factors can perform relatively better than representative macro factors in terms of certainty equivalence return (CER).The third Chapter compares the posterior sampling performance of No-U-Turn sam- pler(NUTS) algorithm and tailored randomized-blocking Metropolis-Hastings (TaRB-MH) for macro-finance affine Term structure models. We conduct empirical experiments on 3 affine term structure models with the U.S. yield curve data. For each experiment, we examine the sampling efficiency of model parameters, factors, term premium, predictive yields,etc. Our emprical results indicate that the TaRB-MH substantially outperforms the NUTS methodin terms of the convergence and efficiency in posterior sampling. Furthermore, we show that NUTS' inefficiency in simulating the affine term structure models will be robust given different initial values for the algorithm.
Author : Emanuel Kopp
Publisher : International Monetary Fund
ISBN 13 : 1484363671
Total Pages : 22 pages
Book Rating : 4.4/5 (843 download)
Download or read book A Macroeconomic Approach to the Term Premium written by Emanuel Kopp and published by International Monetary Fund. This book was released on 2018-06-15 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, term premia have been very low and sometimes even negative. Now, with the United States economy growing above potential, inflationary pressures are on the rise. Term premia are very sensitive to the expected future path of growth, inflation, and monetary policy, and an inflation surprise could require monetary policy to tighten faster than anticipated, inducing to a sudden decompression of term and other risk premia, thus tightening financial conditions. This paper proposes a semi-structural dynamic term structure model augmented with macroeconomic factors to include cyclical dynamics with a focus on medium- to long-run forecasts. Our results clearly show that a macroeconomic approach is warranted: While term premium estimates are in line with those from other studies, we provide (i) plausible, stable estimates of expected long-term interest rates and (ii) forecasts of short- and long-term interest rates as well as cyclical macroeconomic variables that are stunningly close to those generated from large-scale macroeconomic models.
Author : Ha-Il Park
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (75 download)
Download or read book Term Structure Dynamics with Macroeconomic Factors written by Ha-Il Park and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Affine term structure models (ATSMs) are known to have a trade-off in predicting future Treasury yields and fitting the time-varying volatility of interest rates. First, I empirically study the role of macroeconomic variables in simultaneously achieving these two goals under affine models. To this end, I incorporate a liquidity demand theory via a measure of the velocity of money into affine models. I find that this considerably reduces the statistical tension between matching the first and second moments of interest rates. In terms of forecasting yields, the models with the velocity of money outperform among the ATSMs examined, including those with inflation and real activity. My result is robust across maturities, forecasting horizons, risk price specifications, and the number of latent factors. Next, I incorporate latent macro factors and the spread factor between the short-term Treasury yield and the federal funds rate into an affine term structure model by imposing cross-equation restrictions from no-arbitrage using daily data. In doing so, I identify the highfrequency monetary policy rule that describes the central bank's reaction to expected inflation and real activity at daily frequency. I find that my affine model with macro factors and the spread factor shows better forecasting performance.
Author : Francis X. Diebold
Publisher : Princeton University Press
ISBN 13 : 0691146802
Total Pages : 223 pages
Book Rating : 4.6/5 (911 download)
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.
Author : Graham Elliott
Publisher : Elsevier
ISBN 13 : 0444627405
Total Pages : 667 pages
Book Rating : 4.4/5 (446 download)
Download or read book Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics
Author : Hans Dewachter
Publisher :
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4.:/5 (129 download)
Download or read book Macro Factors and the Term Structure of Interest Rates written by Hans Dewachter and published by . This book was released on 2012 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents an essentially affine model of the term structure of interest rates making use of macroeconomic factors and their long-run expectations. The model extends the approach pioneered by Kozicki and Tinsley (2001) by modeling consistently long-run inflation expectations simultaneously with the term structure. Application to the U.S. economy shows the importance of long-run inflation expectations in the modeling of long-term bond yields. The paper also provides a macroeconomic interpretation for the latent factors found in standard finance models of the yield curve: the quot;levelquot; factor represents the long-run inflation expectation of agents; the quot;slopequot; factor captures business cycle conditions; and the quot;curvaturequot; factor expresses a clear independent monetary policy factor.
Author : Evangelos Salachas
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)
Download or read book The Term Structure of Interest Rates and Macro Economy written by Evangelos Salachas and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we extract the factors that shape the yield curve and we relate them with macroeconomy. We examine whether the term structure can predict future economic activity by applying a range of econometric approaches both in pre- and post- crisis periods. Furthermore, we assess the strength of the yield curve forecasting power on economic activity for Eurozone. In addition, we analyze the effect of increased market risk in the term structure and economic activity whereas we evaluate the impact of monetary policy in the term structure. We find that the forecasting performance of term structure deteriorates in the post-crisis period and that credit spreads forecast better Eurozone industrial production. Also, as we find, one significant explanation for the change in predictability during pre- and post- crisis periods is due to the effect of market risk on the term structure during the post-crisis period. Finally, we argue that monetary policy determines significantly the term structure either by conventional or unconventional measures.
Author : Jiahan Li
Publisher :
ISBN 13 :
Total Pages : 43 pages
Book Rating : 4.:/5 (13 download)
Download or read book Forecasting Macroeconomic Time Series written by Jiahan Li and published by . This book was released on 2014 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information out of a large number of predictors. So far dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities in forecasting twenty important macroeconomic variables. These alternative models could handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show analytically and empirically that combing forecasts from LASSO-based models and those from dynamic factor models could further reduce the mean square forecast error (MSFE). Our three main findings can be summarized as follows. First, for most of the variables under investigation, all LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at the economically meaningful block levels, new methods greatly enhance model interpretabilities. Third, once forecasts from a LASSO-based approach and those from a dynamic factor model are combined by forecasts combination techniques, the combined forecasts are significantly better than dynamic factor model forecasts and the naïve random walk benchmark.
Author : Zbynek Stork
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783659563881
Total Pages : 124 pages
Book Rating : 4.5/5 (638 download)
Download or read book Term Structure of Interest Rates written by Zbynek Stork and published by LAP Lambert Academic Publishing. This book was released on 2014-07-08 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Macro-finance modelling is an increasingly popular topic. Various approaches have been developing rapidly, usually using econometric techniques. This book focuses on structural approach to an analysis of average yield curve and its dynamics using macroeconomic factors. An underlying model is based on basic Dynamic Stochastic General Equilibrium (DSGE) approach. Log-linearized solution of the model is the key for derivation of yield curve and its main determinants - pricing kernel, price of risk and affine term structure of interest rates - based on no-arbitrage assumption. The book presents a consistent derivation of a structural macro-finance model, with a reasonable computational burden that allows for time varying term premia. A simple VAR model, widely used in macro-finance literature, serves as a benchmark. The two models are briefly compared and analysis shows their ability to fit an average yield curve observed from the data. It also presents a possible importance of this issue for monetary and fiscal institutions. The book should help shed some light on the use of DSGE framework within macro-finance modelling and should be useful for students and researchers in this field.
Author : Chamadanai Marknual
Publisher :
ISBN 13 :
Total Pages : 260 pages
Book Rating : 4.:/5 (941 download)
Download or read book Term Structure Modeling, Forecasting and Implications for Monetary Policy written by Chamadanai Marknual and published by . This book was released on 2015 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the macro-finance-fiscal term structure model to incorporate fiscal instability variables and the term spread to understand the impact of the sovereign debt crisis on the evolution of the yield curve. My findings reveal financial instability increases the term spread associated with the expectation of higher sovereign default risk and consequently signals economic agents to reduce their spending, and thus worsens economic activity. Secondly, I also investigate whether the dynamic factor model with nonparametric factor loadings is more accurate relative to other term structure models by employing the dynamic semi-parametric factor model (DSFM). The empirical results indicate that a better in-sample fit is provided by the dynamic semiparametric factor model. However, the overall forecasting results are not encouraging. The dynamic semiparametric factor model provides accurate results in forecasting a persistent trend while the dynamic Nelson-Siegel model is more suitable to fit more volatile series. Thirdly,I use a Sheen-Trueck-Wang business conditions index for term structure modeling and forecasting. I find the cross-sectional yield provides guidance to anchor the yield in the next period. The prediction performance of the model is enhancedby using the index since it includes information on frequently released or more recent available data. The index is significantly related to the slope factor, which suggests the forward-looking information from the index inuences the adjustmentthe in the yield slope. Lastly, I examine the effectiveness of the US quantitative easing (QE) policy with a Bayesian structural vector auto regressive (B-SVAR)model with sign restrictions. I find the transmission mechanism of the Federal Reserve asset purchase effectively expands output and avert deflation through a compression in the yield spread.
Author : Mr.Lars E. O. Svensson
Publisher : International Monetary Fund
ISBN 13 : 1451853750
Total Pages : 76 pages
Book Rating : 4.4/5 (518 download)
Download or read book Estimating and Interpreting Forward Interest Rates written by Mr.Lars E. O. Svensson and published by International Monetary Fund. This book was released on 1994-09-01 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of forward interest rates as a monetary policy indicator is demonstrated, using Sweden 1992-1994 as an example. The forward rates are interpreted as indicating market expectations of the time-path of future interest rates, future inflation rates, and future currency depreciation rates. They separate market expectations for the short-, medium-, and long-term more easily than the standard yield curve. Forward rates are estimated with an extended and more flexible version of Nelson and Siegel’s functional form.
Author : Kihwan Kim
Publisher :
ISBN 13 :
Total Pages : 231 pages
Book Rating : 4.:/5 (973 download)
Download or read book Essays on Forecasting Macroeconomic Variables Using Mixed Frequency Data written by Kihwan Kim and published by . This book was released on 2016 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigate the forecasting performance of mixed frequency factor models with mixed frequency dataset. In the first chapter, I consider the mixed fre- quency factor approach used in ADS (2009) to construct their co-incident activity index, and ask the question of whether a class of mixed frequency indexes is useful for predicting the future values of quarterly U.S. real GDP growth and monthly industrial production, unemployment and inflation. My forecasting assessment of the mixed frequency factor model is performed in conjunction with standard prediction models such as autoregression, multivariate distributed lag models, and diffu- sion index models of the variety examined by Stock and Watson (2002a). The main findings of the study are as follows. First, prediction models using only mixed frequency indexes show their best performance at short-term GDP forecasting horizons, and are particularly good during recessions. Second, prediction models using both mixed frequency indexes and diffusion indexes forecast monthly variables more accurately than models using single frequency type indexes. Third, model combi- nations perform relatively poorly in real GDP forecasting contexts, although they perform better when applied to the prediction of monthly variables. Fourth, survey information can be conveniently exploited with higher frequency variables such as daily and weekly variables, and mixed frequency indexes using such survey information are sometimes useful in forecasting lower frequency variables. In the second chapter, I evaluate the predictive performance of hybrid models for forecasting four economic variables. The hybrid approach takes into account the notion that simple autoregression and sophisticated factor models' predictive abilities may change according to the state of the econ- omy. I find that hybrid prediction models produce better forecasts than standard models and than combination models, in most cases, using the same menu of models discussed above. For example, in one-quarter ahead GDP forecasts, the best hybrid model reduces the mean squared forecast error of the best model combinations and the linear models by 14 and 11 percent, on average, respectively. More specifically, the mean squared forecast error of autoregression is reduced by approximately 35 percent. In 12-month ahead predictions of inflation, the best hybrid model improves the best model combinations and the linear models by 11 percent and 16 percent, on average, respectively. This number again increases, in this case to 36 percent, when comparing only with autoregression. One reason for these findings is that hybrid prediction models more effectively utilize survey information.
