Testing Affine Term Structure Models in Case of Transaction Costs

In this paper we empirically analyze the impact of transaction costs on the performance of affine interest rate models. We test the implied (no arbitrage) Euler restrictions, and we calculate the specification error bound of Hansen and Jagannathan to measure the extent to which a model is misspecified. Using data on T-bill and bond returns we find, under the assumption of frictionless markets, strong evidence of misspecification of one- and two-factor affine interest rate models. This is in line with earlier research. However, we show that the pricing errors of these models are reduced considerably, if relatively small transaction costs are taken into account. The average transaction costs for T-bills, due to the bid-ask spread, are around 1.5 basis points. At this size of transaction costs and for monthly holding periods, the misspecification of one- and two-factor affine interest rate models becomes statistically insignificant and economically very small. For quarterly holding periods, higher transaction costs of around 3 basis points are required to avoid misspecification.

COVARIANCE MATRIX CONSTRUCTION AND ESTIMATION: CRITICAL ANALYSES AND EMPIRICAL CASES FOR PORTFOLIO APPLICATIONS

The thesis contributes to the financial econometrics literature by improving the estimation of the covariance matrix among financial time series. To such aim, existing econometrics tools have been investigated and improved, while new ones have been introduced in the field. The main goal is to improve portfolio construction for financial hedging, asset allocation and interest rates risk management. The empirical applicability of the proposed innovations has been tested trough several case studies, involving real and simulated datasets. The thesis is organised in three main chapters, each of those dealing with a specific financial challenge where the covariance matrix plays a central role. Chapter 2 tackles on the problem of hedging portfolios composed by energy commodities. Here, the underlying multivariate volatility among spot and futures securities is modelled with multivariate GARCH models. Under this specific framework, we propose two novel approaches to construct the covariance matrix among commodities, and hence the resulting long-short hedging portfolios. On the one hand, we propose to calculate the hedge ratio of each portfolio constituent to combine them later on in a unique hedged position. On the other hand, we propose to directly hedge the spot portfolio, incorporating in such way investor\u2019s risk and return preferences. Trough a comprehensive numerical case study, we assess the sensitivity of both approaches to volatility and correlation misspecification. Moreover, we empirically show how the two approaches should be implemented to hedge a crude oil portfolio. Chapter 3 focuses on the covariance matrix estimation when the underlying data show non\u2013Normality and High\u2013Dimensionality. To this extent, we introduce a novel estimator for the covariance matrix and its inverse \u2013 the Minimum Regularised Covariance Determinant estimator (MRCD) \u2013 from chemistry and criminology into our field. The aim is twofold: first, we improve the estimation of the Global Minimum Variance Portfolio by exploiting the MRCD closed form solution for the covariance matrix inverse. Trough an extensive Monte Carlo simulation study we check the effectiveness of the proposed approach in comparison to the sample estimator. Furthermore, we take on an empirical case study featuring five real investment universes characterised by different stylised facts and dimensions. Both simulation and empirical analysis clearly demonstrate the out\u2013of\u2013sample performance improvement while using the MRCD. Second, we turn our attention on modelling the relationships among interest rates, comparing five covariance matrix estimators. Here, we extract the principal components driving the yield curve volatility to give important insights on fixed income portfolio construction and risk management. An empirical application involving the US term structure illustrates the inferiority of the sample covariance matrix to deal with interest rates. In chapter 4, we improve the shrinkage estimator for four risk-based portfolios. In particular, we focus on the target matrix, investigating six different estimators. By the mean of an extensive numerical example, we check the sensitivity of each risk-based portfolio to volatility and correlation misspecification in the target matrix. Furthermore, trough a comprehensive Monte Carlo experiment, we offer a comparative study of the target estimators, testing their ability in reproducing the true portfolio weights. Controlling for the dataset dimensionality and the shrinkage intensity, we find out that the Identity and Variance Identity target estimators are the best targets towards which to shrink, always holding good statistical properties

New return anomalies and new-Keynesian ICAPM

AbstractI propose a new multi-factor asset pricing model with new-Keynesian factors to explain stock return anomalies from 1972Q1 to 2009Q2. This new model explains the average returns across testing portfolios formed on financial distress, momentum, and standardized unexpected earnings with misspecification-robust statistics. Test portfolios formed on net stock issues and total accruals are also partly explained by new-Keynesian factors. Two monetary policy factors play an important role in explaining these new anomalies. The credit aspect of these new anomalies suggests an economic rationale for the model through capital market imperfections and the credit channel of monetary policy mechanism

Linear Factor Models and the Estimation of Expected Returns

Linear factor models of asset pricing imply a linear relationship between expected returns of assets and exposures to one or more sources of risk. We show that exploiting this linear relationship leads to statistical gains of up to 31% in variances when estimating expected returns on individual assets over historical averages. When the factors are weakly correlated with assets, i.e. β’s are small, and the interest is in estimating expected excess returns, that is risk premiums, on individual assets rather than the prices of risk, the Generalized Method of Moment estimators of risk premiums does lead to reliable inference, i.e. limiting variances suffer from neither lack of identification nor unboundedness. If the factor model is misspecified in the sense of an omitted factor, we show that factor model-based estimates may be inconsistent. However, we show that adding an alpha to the model capturing mispricing only leads to consistent estimators in case of traded factors. Moreover, our simulation experiment documents that using the more precise estimates of expected returns based on factor{models rather than the historical averages translates into significant improvements in the out-of-sample performances of the optimal portfolios

Gold as an inflation hedge?

This paper attempts to reconcile an apparent contradiction between short-run and long-run movements in the price of gold. The theoretical model suggests a set of conditions under which the price of gold rises over time at the general rate of inflation and hence be an effective hedge against inflation. The model also demonstrates that short-run changes in the gold lease rate, the real interest rate, convenience yield, default risk, the covariance of gold returns with other assets and the dollar/world exchange rate can disturb this equilibrium relationship and generate short-run price volatility. Using monthly gold price data (1976-1999), and cointegration regression techniques, an empirical analysis confirms the central hypotheses of the theoretical model