Author: Michiel De PooterPublisher:
ISBN:
Category :
Languages : en
Pages : 49
Book Description
Term Structure Forecasting Using Macro Factors and Forecast Combination
Author: Michiel De PooterModelling and forecasting stock return volatility and the term structure of interest rates
Author: Michiel de PooterPublisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286
Book Description
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.
Predicting the Term Structure of Interest Rates
Author: Michiel De PooterPublisher:
ISBN:
Category :
Languages : en
Pages : 52
Book Description
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.
Forecasting Financial Time Series Using Model Averaging
Author: Francesco RavazzoloPublisher: Rozenberg Publishers
ISBN: 9051709145
Category :
Languages : en
Pages : 198
Book Description
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.
Term Structure Dynamics with Macro Factors Using High Frequency Data
Author: Hwagyun KimPublisher:
ISBN:
Category :
Languages : en
Pages : 28
Book Description
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.
Essays on Macro-finance Affine Term Structure Models
Author: Biancen XiePublisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 111
Book Description
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.
Term Structure Dynamics with Macroeconomic Factors
Author: Ha-Il ParkPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
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.
Handbook of Economic Forecasting
Author: Graham ElliottPublisher: Elsevier
ISBN: 0444627405
Category : Business & Economics
Languages : en
Pages : 667
Book Description
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
Yield Curve Modeling and Forecasting
Author: Francis X. DieboldPublisher: Princeton University Press
ISBN: 0691146802
Category : Business & Economics
Languages : en
Pages : 223
Book Description
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.
The Term Structure of Interest Rates and Macro Economy
Author: Evangelos SalachasPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
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.