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 timevarying 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 realtime 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.

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.

The epidemiology of foot-and-mouth disease at the wildlife-livestock interface in northern Tanzania

Foot-and-mouth disease (FMD), a disease of cloven hooved animals caused by FMD virus (FMDV), is one of the most economically devastating diseases of livestock worldwide. The global burden of disease is borne largely by livestock-keepers in areas of Africa and Asia where the disease is endemic and where many people rely on livestock for their livelihoods and food-security. Yet, there are many gaps in our knowledge of the drivers of FMDV circulation in these settings. In East Africa, FMD epidemiology is complicated by the circulation of multiple FMDV serotypes (distinct antigenic variants) and by the presence of large populations of susceptible wildlife and domestic livestock. The African buffalo (Syncerus caffer) is the only wildlife species with consistent evidence of high levels of FMDV infection, and East Africa contains the largest population of this species globally. To inform FMD control in this region, key questions relate to heterogeneities in FMD prevalence and impacts in different livestock management systems and to the role of wildlife as a potential source of FMDV for livestock. To develop FMD control strategies and make best use of vaccine control options, serotype-specific patterns of circulation need to be characterised. In this study, the impacts and epidemiology of FMD were investigated across a range of traditional livestock-keeping systems in northern Tanzania, including pastoralist, agro-pastoralist and rural smallholder systems. Data were generated through field studies and laboratory analyses between 2010 and 2015. The study involved analysis of existing household survey data and generated serological data from cross-sectional livestock and buffalo samples and longitudinal cattle samples. Serological analyses included non-structural protein ELISAs, serotype-specific solid-phase competitive ELISAs, with optimisation to detect East African FMDV variants, and virus neutralisation testing. Risk factors for FMDV infection and outbreaks were investigated through analysis of cross-sectional serological data in conjunction with a case-control outbreak analysis. A novel Bayesian modeling approach was developed to infer serotype-specific infection history from serological data, and combined with virus isolation data from FMD outbreaks to characterise temporal and spatial patterns of serotype-specific infection. A high seroprevalence of FMD was detected in both northern Tanzanian livestock (69%, [66.5 - 71.4%] in cattle and 48.5%, [45.7-51.3%] in small ruminants) and in buffalo (80.9%, [74.7-86.1%]). Four different serotypes of FMDV (A, O, SAT1 and SAT2) were isolated from livestock. Up to three outbreaks per year were reported by households and active surveillance highlighted up to four serial outbreaks in the same herds within three years. Agro-pastoral and pastoral livestock keepers reported more frequent FMD outbreaks compared to smallholders. Households in all three management systems reported that FMD outbreaks caused significant impacts on milk production and sales, and on animals’ draught power, hence on crop production, with implications for food security and livelihoods. Risk factor analyses showed that older livestock were more likely to be seropositive for FMD (Odds Ratio [OR] 1.4 [1.4-1.5] per extra year) and that cattle (OR 3.3 [2.7-4.0]) were more likely than sheep and goats to be seropositive. Livestock managed by agro-pastoralists (OR 8.1 [2.8-23.6]) or pastoralists (OR 7.1 [2.9-17.6]) were more likely to be seropositive compared to those managed by smallholders. Larger herds (OR: 1.02 [1.01-1.03] per extra bovine) and those that recently acquired new livestock (OR: 5.57 [1.01 – 30.91]) had increased odds of suffering an FMD outbreak. Measures of potential contact with buffalo or with other FMD susceptible wildlife did not increase the likelihood of FMD in livestock in either the cross-sectional serological analysis or case-control outbreak analysis. The Bayesian model was validated to correctly infer from ELISA data the most recent serotype to infect cattle. Consistent with the lack of risk factors related to wildlife contact, temporal and spatial patterns of exposure to specific FMDV serotypes were not tightly linked in cattle and buffalo. In cattle, four serial waves of different FMDV serotypes that swept through southern Kenyan and northern Tanzanian livestock populations over a four-year period dominated infection patterns. In contrast, only two serotypes (SAT1 and SAT2) dominated in buffalo populations. Key conclusions are that FMD has a substantial impact in traditional livestock systems in East Africa. Wildlife does not currently appear to act as an important source of FMDV for East African livestock, and control efforts in the region should initially focus on livestock management and vaccination strategies. A novel modeling approach greatly facilitated the interpretation of serological data and may be a potent epidemiological tool in the African setting. There was a clear temporal pattern of FMDV antigenic dominance across northern Tanzania and southern Kenya. Longer-term research to investigate whether serotype-specific FMDV sweeps are truly predictable, and to shed light on FMD post-infection immunity in animals exposed to serial FMD infections is warranted.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.