Author: Sebastian FuxPublisher:
ISBN:
Category :
Languages : un
Pages : 0
Book Description
Ph. D.-serie
Author: Sebastian FuxEssays on Return Predictability and Term Structure Modelling
Author: Sebastian FuxPublisher:
ISBN: 9788793155190
Category :
Languages : en
Pages : 159
Book Description
Essays on Return Predictability and Term Structure Modelling
Author: Sebastian FuxPublisher:
ISBN: 9788793155183
Category :
Languages : en
Pages : 159
Book Description
Ph. D.-serie
Author: Sebastian FuxEssays in Term Structure Modelling and Bond Returns Forecasting
Author: Mikhail PranovichEssays on Return Predictability in Financial Markets
Author: Chan R. MangPublisher:
ISBN:
Category :
Languages : en
Pages : 149
Book Description
My thesis examines return predictability in government bond markets and currency markets. In Chapter 1, I take the term structure model in Cochrane and Piazzesi (2008) and construct currency market prices. The implied currency market prices are then counterfactually volatile and predictable, at least with respect to commonly used predictor variables. Getting the model closer to currency market data means reducing bond risk compensation but doing so nearly eliminates predictability in bond markets. One way to generate sensible time-variation in bond and currency risk-premia allows the volatility of returns to be time-varying. In Chapter 2, I test if alternative forecast rules perform better than the return-forecasting factor of Cochrane and Piazzesi (2008). I compare forecasts assuming all historical data is available to recursively made ones that are revised with the arrival of news. Differences in the two forecast rules systematically move with realized bond risk-premia and forecast mean yield curve levels and short-term interest rates one year ahead not just for the U.S., but also for government bond markets of other industrialized economies. I show that lower long-term rates relative to short-rates in 2004-2005 is consistent with an expected a decline of interest rates by market participants. In Chapter 3, I show that the cross-sectional average spread in the return-forecasting factor of Cochrane and Piazzesi (2005, 2008) can forecast currency risk-premia. However, the return-forecasting factor spread consistent with real-time data does not forecast currency risk-premia. I also find that both currency risk-premia and exchange rate changes have a predictable component that is detected by the information gap, what I call the hidden FX market factor, between forecasts that take as given the full sample of data and those consistent with real-time availability. Controlling for large and transitory exchange rate changes using this information gap make interest rate differentials between the average foreign country and the U.S. positively correlated with dollar appreciation rates, delivering the right sign predicted by uncovered interest parity.
Option Markets, Return Predictability and Term Structure
Author: Yanhui ZhaoPublisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
Book Description
This dissertation consists of three essays on eliciting information about underlying assets from the equity options markets, and improving our understanding of the term structure cost of equity. In the first essay, we find that high standard deviations of the volatility premium, of implied volatility innovations, and of the volatility term structure spread in equity options predict low underlying returns. This return predictability is not explained by the levels of these three variables, or by volatility of volatility, other known firm characteristics, or common risk factor models. We find support for interpreting the standard deviations of these option-based measures as forward-looking proxies of heterogeneous beliefs. In the second essay, we find that stocks with high risk-neutral skewness (RNS) exhibit abnormal performance driven by rebounds following poor performance. This behavior connects it to momentum crashes caused by reversal in past losers. In periods of post-recession rebounds and high market volatility when momentum crashes occur, a zero-investment high-low RNS portfolio has significant positive abnormal returns. The momentum anomaly is strongest (weakest) in stocks with the lowest (highest) RNS, consistent with the positive relationship of RNS to momentum crashes. These results hold controlling for trading frictions, other firm characteristics, and risk factors. We generalize our findings to all stocks by constructing an RNS factor-mimicking portfolio SKEW and find that a WML strategy that avoids high SKEW beta stocks has superior performance to the baseline and risk-managed WML strategies. In the third essay, we estimate the cost of equity capital term structure for the insurance industry as a whole, and several insurance sectors such as life/health and property/casualty. We use a vector autoregressive process to jointly model the dynamics of expected cash flows, beta, and the market risk premium. We obtain a closed form solution for the discount rate appropriate for each maturity. Our empirical analysis shows that for the insurance industry, the cost of equity based on our term structure model is on average nearly 299.6 basis points higher compared to the single period CAPM. This means that these insurers might overly invest if they rely on the single period CAPM.
Essays in Term Structure Modelling
Author: Jes TaulbjergPublisher:
ISBN: 9788789695693
Category :
Languages : en
Pages :
Book Description
The Term Structure of the Risk-return Tradeoff
Author: John Y. CampbellPublisher:
ISBN:
Category : Investments
Languages : en
Pages : 45
Book Description
Recent research in empirical finance has documented that expected excess returns on bonds and stocks, real interest rates, and risk shift over time in predictable ways. Furthermore, these shifts tend to persist over long periods of time. In this paper we propose an empirical model that is able to capture these complex dynamics, yet is simple to apply in practice, and we explore its implications for asset allocation. Changes in investment opportunities can alter the risk-return tradeoff of bonds, stocks, and cash across investment horizons, thus creating a term structure of the risk-return tradeoff.'' We show how to extract this term structure from our parsimonious model of return dynamics, and illustrate our approach using data from the U.S. stock and bond markets. We find that asset return predictability has important effects on the variance and correlation structure of returns on stocks, bonds and T-bills across investment horizons
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.