Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Credit risk & forward price models

Doutoramento em Gestão

Factores económicos do risco sistemático nas carteiras de investimento: o caso português

Dissertação de mest. em Ciências Económicas e Empresariais, Unidade de Ciências Económicas e Empresariais, Univ. do Algarve, 1996

Determinants Of Firm Terminal Value: The Perspective Of North American And European Financial Analysts

Company valuation models attempt to estimate the value of a company in two stages: (1) comprising of a period of explicit analysis and (2) based on unlimited production period of cash flows obtained through a mathematical approach of perpetuity, which is the terminal value. In general, these models, whether they belong to the Dividend Discount Model (DDM), the Discount Cash Flow (DCF), or RIM (Residual Income Models) group, discount one attribute (dividends, free cash flow, or results) to a given discount rate. This discount rate, obtained in most cases by the CAPM (Capital asset pricing model) or APT (Arbitrage pricing theory) allows including in the analysis the cost of invested capital based on the risk taking of the attributes. However, one cannot ignore that the second stage of valuation that is usually 53-80% of the company value (Berkman et al., 1998) and is loaded with uncertainties. In this context, particular attention is needed to estimate the value of this portion of the company, under penalty of the assessment producing a high level of error. Mindful of this concern, this study sought to collect the perception of European and North American financial analysts on the key features of the company that they believe contribute most to its value. For this feat, we used a survey with closed answers. From the analysis of 123 valid responses using factor analysis, the authors conclude that there is great importance attached (1) to the life expectancy of the company, (2) to liquidity and operating performance, (3) to innovation and ability to allocate resources to R&D, and (4) to management capacity and capital structure, in determining the value of a company or business in long term. These results contribute to our belief that we can formulate a model for valuating companies and businesses where the results to be obtained in the evaluations are as close as possible to those found in the stock market

Pairs trading in the UK equity market: risk and return

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.

How long is the memory of the US stock market?

The Efficient Market Hypothesis (EMH), one of the most important hypothesis in financial economics, argues that return rates have no memory (correlation) which implies that agents cannot make abnormal profits in financial markets, due to the possibility of arbitrage operations. With return rates for the US stock market, we corroborate the fact that with a linear approach, return rates do not show evidence of correlation. However, linear approaches might not be complete or global, since return rates could suffer from nonlinearities. Using detrended cross-correlation analysis and its correlation coefficient, a methodology which analyzes long-range behavior between series, we show that the long-range correlation of return rates only ends in the 149th lag, which corresponds to about seven months. Does this result undermine the EMH?