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Author: Biancen Xie Publisher: 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.
Author: Biancen Xie Publisher: 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.
Author: Kyu Ho Kang Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 108
Book Description
In my dissertation, I focus on theoretical asset pricing models and the development of Bayesian econometric methods to estimate them, particularly in the area of bond pricing. The first essay theoretically and empirically examines structural changes in a dynamic term-structure model of zero-coupon bond yields. To do this, we develop a new arbitrage-free one latent and two macro-economics factor affine model to price default-free bonds when all model parameters are subject to change at unknown time points. The bonds in our set-up can be priced straightforwardly once the change-point model is formulated as a specific unidirectional Markov process. We consider five versions of our general model - with 0, 1, 2, 3 and 4 change-points - to a collection of 16 yields measured quarterly over the period 1972:I to 2007:IV. Our empirical approach to inference is fully Bayesian with priors set up to reflect the assumption of a positive term-premium. The use of Bayesian techniques is particularly relevant because the models are high-dimensional and non-linear, and because it is more straightforward to compare our different change-point models from the Bayesian perspective. Our estimation results indicate that the model with 3 change-points is most supported by the data and that the breaks occurred in 1980:II, 1985:IV and 1995:II. These dates correspond (in turn) to the time of a change in monetary policy, the onset of what is termed the great moderation, and the start of technology driven period of economic growth. We also utilize the Bayesian framework to derive the out-of-sample predictive densities of the term-structure. We find that the forecasting performance of the 3 change-point model is substantially better than that of the other models we examine. In the second essay, we develop and estimate a model of the term structure of interest rates within the context of a Dynamic Stochastic General Equilibrium model. The model features multiple monetary policy and volatility regimes. We estimate this model by Bayesian methods. Our estimation results reveal that U.S. monetary policy has become ``more active'' since 1995:Q2, that during this period, the average term premium has fallen, and that the price of regime shift risk is always significantly positive over time. These findings highlight the important role that general equilibrium modeling can play in understanding the complex dynamics of the term structure.
Author: Azamat Abdymomunov Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 109
Book Description
In my dissertation, I study relationships between macroeconomics and financial markets. In particular, I empirically investigate the links between key macroeconomic indicators, such as output, inflation, and the business cycle, and the pricing of financial assets. The dissertation comprises three essays. The first essay investigates how the entire term structure of interest rates is influenced by regime-shifts in monetary policy. To do so, we develop and estimate an arbitrage-free dynamic term-structure model which accounts for regime shifts in monetary policy, volatility, and the price of risk. Our results for U.S. data from 1985-2008 indicate that (i) the Fed's reaction to inflation has changed over time, switching between "more active" and "less active" monetary policy regimes, (ii) the yield curve in the "more active" regime was considerably more volatile than in the "less active" regime, and (iii) on average, the slope of the yield curve in the "more active" regime was steeper than in the "less active" regime. The steeper yield curve in the "more active" regime reflects higher term premia that result from the risk associated with a more volatile future short-term rate given a more sensitive response to inflation. The second essay examines the predictive power of the entire yield curve for aggregate output. 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 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. The third essay investigates time variation in CAPM betas for book-to-market and momentum portfolios across stock market volatility regimes. For our analysis, we jointly model market and portfolio returns using a two-state Markov-switching process, with beta and the market risk premium allowed to vary between "low" and "high" volatility regimes. Our empirical findings suggest strong time variation in betas across volatility regimes in most of the cases for which the unconditional CAPM can be rejected. Although the regime-switching conditional CAPM can still be rejected in many cases, the time-varying betas help explain portfolio returns much better than the unconditional CAPM, especially when market volatility is high.
Author: Zbynek Stork Publisher: LAP Lambert Academic Publishing ISBN: 9783659563881 Category : Languages : en Pages : 124
Book Description
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: Wolfgang Lemke Publisher: ISBN: 9783865583000 Category : Languages : en Pages : 49
Book Description
A joint model of macroeconomic and term structure dynamics is specified and estimated for the euro area. The model comprises a backward-looking Phillips curve, a dynamic IS equation, a monetary policy rule as well as a specification of the dynamics of trend growth and the natural real interest rate. Under the condition of no arbitrage, yields of all maturities are affine functions of the macroeconomic driving forces. With the exception of a shock to potential output growth, the response of short-term yields to macroeconomic shocks is generally stronger than that of long-term yields. Impulse responses of all bond yields are fairly persistent, which reflects the persistence of their macroeconomic driving forces. Across the whole maturity spectrum, about ninety percent of the variationin yields is explained jointly by monetary policy shocks and shocks to the natural real rate of interest; the relative contribution of the latter shock increases with time to maturity. Cost-push shocks explain at most eight percent, while shocks to the output gap play an even less important role.
Author: Marcelo Ferman Publisher: ISBN: Category : Languages : en Pages :
Book Description
This thesis contributes to the literature that analyses the term structure of interest rates from a macroeconomic perspective. Chapter 1 studies the transmission of monetary policy shocks to the US macroeconomy and term structure. Based on estimates of a Macro-Affine model, it shows that monetary policy shocks trigger relevant movements in bond premia, which in turn feed back into the macroeconomy. This channel of monetary transmission shows up importantly in the pre-Volcker period, but becomes irrelevant later. This chapter concludes with an analysis of the macroeconomic implications of shocks to expectations about future monetary policy actions. Chapter 2 proposes a regime-switching approach to explain why the U.S. nominal yield curve on average has been steeper since the mid-1980s than during the Great Inflation of the 1970s. It shows that, once the possibility of regime switches in the short-rate process is incorporated into investors' beliefs, the average slope of the yield curve generally will contain a new component called 'level risk'. Level risk estimates were found to be large and negative during the Great Inflation, but became moderate and positive afterwards. These findings are replicated in a Markov-Switching DSGE model, where the monetary policy rule shifts between an active and a passive regime with respect to inflation fluctuations. Chapter 3 develops a DSGE model in which banks use short-term deposits to provide firms with long-term credit. The demand for long-term credit arises because firms borrow in order to finance their capital stock which they only adjust at infrequent intervals. The model shows that maturity transformation in the banking sector in general attenuates the output response to a technological shock. Implications of long-term nominal contracts are also examined in a New Keynesian version of the model. In this case, maturity transformation reduces the real effects of a monetary policy shock.
Author: Biancen Xie
Author: Biancen Xie
Author: Vicente Jakas
Author: Bovorn Vichiansin
Author: Marco S. Matsumura
Author: Kyu Ho Kang
Author: Azamat Abdymomunov
Author: Zbynek Stork
Author: Wolfgang Lemke
Author: Marcelo Ferman
Author: David Jamieson Bolder