Master of Arts / Department of Economics / Lloyd B. Thomas / A negative-sloped Treasury curve is often cited in financial news articles and by Federal Reserve economists as a predictor of recessions. This report reviews previously published research examining the reliability of yield curves predicting recessions. Findings show that the yield curve inverts two or more quarters before recessions, with short-term interest rates rising above long-term interest rates. Probit regression has proven a reliable method for generating estimated probabilities of future recessions that, in turn, are useful for both monetary policy and asset allocation decision-making.

Since arbitrage-free is a desirable theoretical feature in a healthy financial market, many efforts have been made to construct arbitrage-free models for yield curves. However, little attention is paid to review if such restriction will improve yield forecast. We evaluate the importance of arbitrage-free restriction on dynamic Nelson-Siegel term structure when forecasting yield curves. We find that it doesn’t help. We also compare these two Nelson-Siegel dynamic models with a benchmark dynamic model and show that Nelson-Siegel structure improve forecasts for long-maturity yields. / text

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It has been twelve years since the last time the yield curve was inverted. Since 2017, the yield curve has been continuing to flatten and has almost entered an inverted state. The last five recessions have been preceded by the inversion of the yield curve. I examine momentum trading strategy’s ability to outperform during an inverted yield curve state. The yield curve can enter the momentum portfolio strategy through the portfolio’s formation and holding period. I document the increased performance of the momentum strategy’s total portfolio return in an inverted state. These results have implications on the timing a momentum trading strategy might be implemented.

Economists often use complex mathematical models to forecast the future path of the economy and the likelihood of recession. But more simple indicators such as interest rates, stock price indices, and monetary aggregates also contain some relevant information about future economic activity. In this thesis we revisit the usefulness of one such indicator, the yield curve or, more specifically, the spread between the interest rates on the ten-year Treasury note and the three-month Treasury bill. By using four different models we examine whether the yield spread has still some predicitve power for future real GDP growth in selected european countries. What is more, we are comparing the predictive power of the yield spread with different variables, both in- sample and out-of-sample. We decompose the yield spread into expectations effect and term premium effect in order to investigate which factor contributes more to predicting real GDP growth. Using modified definition of recession we conclude that that yield spread still contains some useful information for predicting future economic activity, although its predictive power deteriorates.

In this thesis we survey several interpolation methods that are used to construct the yield curves. We also review the bootstrapping and show that the bootstrap is closely connected to the interpolation in the case of bootstrapping yield curve. The most effort is dedicated, in this thesis, on the monotone convex method and on investigation of the difficulties to get accurate yield curves.

The yields of xenon and krypton from the neutron- induced fission of Am241 and Am242 have been measured with a mass spectrometer. This was accomplished by irradiating samples of Am241 for different lengths of time so that the effect of the growth of highly fissionable Am242 could be determined. These studies reveal that both the degree of fine structure in the mass yield curve and the fission-product charge distribution are dependent on the energy of the incident neutrons. This has not been previously observed for any fissioning nuclide. These studies also reveal effects of the 50-neutron shell and of the neutron-proton ratio of the fissioning nuclide on the mass yield curve. / Thesis / Doctor of Philosophy (PhD)

The scope of this study is to estimate the zero-coupon yield curve of tomorrow by using Vasicek yield curve model with the zero-coupon bond yield data of today. The raw data of this study is the yearly simple spot rates of the Turkish zero-coupon bonds with different maturities of each day from July 1, 1999 to March 17, 2004. We completed the missing data by using Nelson-Siegel yield curve model and we estimated tomorrow yield cuve with the discretized Vasicek yield curve model.

It always exists different methods/models to build a yield curve from a set of observed market rates even when the curve completely reproduces the price of the given instruments. To create an accurate and smooth interest rate curve has been a challenging all the time. The purpose of this thesis is to use the real market data to construct the yield curves by the bootstrapping method and the Smith Wilson model in order to observe and compare the performance ability between the models. Furthermore, the extended Nelson Siegel model is introduced without implementation. Instead of implementation I compare the ENS model and the traditional bootstrapping method from a more theoretical perspective in order to perceive the performance capabilities of them.

Exchange rate forecasting is hard issue for most of floating exchange rate economies. Studying exchange rate is very attractive matter since almost no model could beat random walk in short run yet. Relative yields and information in relative yield curves are contemporary topics in empirical literature and this study follows Chen and Tsang (2009) who model exchange rate changes with relative factors obtained from Nelson-Siegel (1987) yield curve model and find that relative factor model can forecast exchange rate change up to 2 years and perform better than random walk in short run. Analysis follows the methodology defined by Chen and Tsang (2009) and TL/USD, TL/EUR exchange rate changes are modeled by the relative factors namely relative level, relative slope and relative curvature. Basically, 162 weekly datasets from 09.01.2007 to 16.03.2010 are used and the relative factors for each week are estimated. Afterwards, regression analysis is made and results show that relative level and relative curvature factors are significant up to 4-6 weeks horizon but relative slope does not provide any valuable information for exchange rate prediction in Turkish financial market. Length of forecasting horizon of relative factor model is too short when compared to other exchange rate models. Since it is accepted that exchange rates follow random walk, we provided some tests to compare performance of the model. Similar to the literature, only short run performance of relative factor model is compared to random walk model and concluded that the relative factor model does not provide better forecasting performance in Turkish financial market