13,801 research outputs found

    Essays on monetary policy and financial stability

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    Doutoramento em EconomiaBy focusing on the relationship between financial stability and monetary policy for the cases of Chile, Colombia, Japan, Portugal and the UK, this thesis aims to add to the existing literature on the fundamental issue of the relationship between financial stability and monetary policy, a traditional topic that gained importance in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. As the zero-lower bound loomed and the reach of traditional monetary policy narrowed, policy makers realised that alternative frameworks were needed and hence, macroprudential policy measures aimed at targeting the financial system as a whole were introduced. The second chapter looks at the relationship between monetary policy and financial stability, which has gained importance in recent years as Central Bank policy rates neared the zero-lower bound. We use an SVAR model to study the impact of monetary policy shocks on three proxies for financial stability as well as a proxy for economic growth. Monetary policy is represented by policy rates for the EMEs and shadow rates for the AEs in our chapter. Our main results show that monetary policy may be used to correct asset mispricing, to control fluctuations in the real business cycle and also to tame credit cycles in the majority of cases. Our results also show that for the majority of cases, in line with theory, local currencies appreciate following a positive monetary policy shock. Monetary policy intervention may indeed be successful in contributing to or achieving financial stability. However, the results show that monetary policy may not have the ability to maintain or re-establish financial stability in all cases. Alternative policy choices such as macroprudential policy tool frameworks which are aimed at targeting the financial system as a whole may be implemented as a means of fortifying the economy. The third chapter looks at the institutional setting of the countries in question, the independence of the Central Bank, the political environment and the impact of these factors on financial Abstract stability. I substantiate the literature review discussion with a brief empirical analysis of the effect of Central Bank Independence on credit growth using an existing database created by Romelli (2018). The empirical results show that there is a positive relationship between credit growth and the level of Central Bank Independence (CBI) due to the positive and statistically significant coefficient on the interaction term between growth in domestic credit to the private sector and the level of CBI. When considering domestic credit by deposit money banks and other financial institutions, the interaction term is positive and statistically significant for the case of the UK for the third regression equation. A number of robustness checks show that the coefficient is positive and statistically significant for a number of cases when implementing a variety of estimation methods. Fluctuations in credit growth are larger for higher levels of CBI and hence, in periods of financial instability or ultimately financial crises, CBI would be reined back in an effort to re-establish financial stability. Based on the empirical results, and in an effort to slow down surging credit supply and to maintain financial stability, policy makers and governmental authorities should attempt to decrease the level of CBI when the economy shows signs of overheating and credit supply continues to increase. The fourth chapter looks at the interaction between macroprudential policy and financial stability. The unexpected interconnectedness of the global economy and the economic blight that occurred as a result of this, recapitulated the need to implement an alternative policy framework aimed at targeting the financial system as a whole and hence, targeting the maintenance of financial stability. In this chapter, an index of domestic macroprudential policy tools is constructed and the effectiveness of these tools in controlling credit growth, managing GDP growth and stabilising inflation growth is studied using a dynamic panel data model for the period between 2000 and 2017. The empirical analysis includes two panels namely an EU panel of 27 countries and a Latin American panel of 7 countries, the chapter also looks at a case study of Japan, Portugal and the UK. Our main results find that a tighter macroprudential policy tool stance leads to a decrease in both credit growth and GDP growth while, a tighter macroprudential policy tool stance results in higher inflation in the majority of cases. Further, we find that capital openness plays a more important role in the case of Latin America, this may be due to the region’s dependence on foreign capital flows and exchange rate movements. Lastly, we find that, in times of higher perceived market volatility, GDP growth tends to be higher and inflation growth tends to be lower in the EU. In the other cases, higher levels of perceived market volatility result in higher inflation, higher credit growth and lower GDP Abstract growth. This is in line with expectations as an increase in perceived market volatility is met with an increased flow of assets into safer markets such as the EU. This thesis establishes a relationship between financial stability and monetary policy by studying the response of Chile, Colombia, Japan, Portugal and the UK in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. In short, the results of the work conducted in this thesis may be summarised as follows. Our results show that monetary policy contributes to the achievement of financial stability. Still, monetary policy alone is not sufficient and should be reinforced by less traditional policy choices such as macroprudential policy tools. Secondly, we find that the level of CBI should be reined in in times of surging credit supply in an effort to maintain financial stability. Finally, we conclude that macroprudential policy tools play an important role in the achievement of financial stability. These tools should complement traditional monetary policy frameworks and should be adapted for each region.info:eu-repo/semantics/publishedVersio

    Mathematical models to evaluate the impact of increasing serotype coverage in pneumococcal conjugate vaccines

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    Of over 100 serotypes of Streptococcus pneumoniae, only 7 were included in the first pneumo- coccal conjugate vaccine (PCV). While PCV reduced the disease incidence, in part because of a herd immunity effect, a replacement effect was observed whereby disease was increasingly caused by serotypes not included in the vaccine. Dynamic transmission models can account for these effects to describe post-vaccination scenarios, whereas economic evaluations can enable decision-makers to compare vaccines of increasing valency for implementation. This thesis has four aims. First, to explore the limitations and assumptions of published pneu- mococcal models and the implications for future vaccine formulation and policy. Second, to conduct a trend analysis assembling all the available evidence for serotype replacement in Europe, North America and Australia to characterise invasive pneumococcal disease (IPD) caused by vaccine-type (VT) and non-vaccine-types (NVT) serotypes. The motivation behind this is to assess the patterns of relative abundance in IPD cases pre- and post-vaccination, to examine country-level differences in relation to the vaccines employed over time since introduction, and to assess the growth of the replacement serotypes in comparison with the serotypes targeted by the vaccine. The third aim is to use a Bayesian framework to estimate serotype-specific invasiveness, i.e. the rate of invasive disease given carriage. This is useful for dynamic transmission modelling, as transmission is through carriage but a majority of serotype-specific pneumococcal data lies in active disease surveillance. This is also helpful to address whether serotype replacement reflects serotypes that are more invasive or whether serotypes in a specific location are equally more invasive than in other locations. Finally, the last aim of this thesis is to estimate the epidemiological and economic impact of increas- ing serotype coverage in PCVs using a dynamic transmission model. Together, the results highlight that though there are key parameter uncertainties that merit further exploration, divergence in serotype replacement and inconsistencies in invasiveness on a country-level may make a universal PCV suboptimal.Open Acces

    A Comparative Study on Students’ Learning Expectations of Entrepreneurship Education in the UK and China

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    Entrepreneurship education has become a critical subject in academic research and educational policy design, occupying a central role in contemporary education globally. However, a review of the literature indicates that research on entrepreneurship education is still in a relatively early stage. Little is known about how entrepreneurship education learning is affected by the environmental context to date. Therefore, combining the institutional context and focusing on students’ learning expectations as a novel perspective, the main aim of the thesis is to address the knowledge gap by developing an original conceptual framework to advance understanding of the dynamic learning process of entrepreneurship education through the lens of self-determination theory, thereby providing a basis for advancing understanding of entrepreneurship education. The author adopted an epistemological positivism philosophy and a deductive approach. This study gathered 247 valid questionnaires from the UK (84) and China (163). It requested students to recall their learning expectations before attending their entrepreneurship courses and to assess their perceptions of learning outcomes after taking the entrepreneurship courses. It was found that entrepreneurship education policy is an antecedent that influences students' learning expectations, which is represented in the difference in student autonomy. British students in active learning under a voluntary education policy have higher autonomy than Chinese students in passive learning under a compulsory education policy, thus having higher learning expectations, leading to higher satisfaction. The positive relationship between autonomy and learning expectations is established, which adds a new dimension to self-determination theory. Furthermore, it is also revealed that the change in students’ entrepreneurial intentions before and after their entrepreneurship courses is explained by understanding the process of a business start-up (positive), hands-on business start-up opportunities (positive), students’ actual input (positive) and tutors’ academic qualification (negative). The thesis makes contributions to both theory and practice. The findings have far reaching implications for different parties, including policymakers, educators, practitioners and researchers. Understanding and shaping students' learning expectations is a critical first step in optimising entrepreneurship education teaching and learning. On the one hand, understanding students' learning expectations of entrepreneurship and entrepreneurship education can help the government with educational interventions and policy reform, as well as improving the quality and delivery of university-based entrepreneurship education. On the other hand, entrepreneurship education can assist students in establishing correct and realistic learning expectations and entrepreneurial conceptions, which will benefit their future entrepreneurial activities and/or employment. An important implication is that this study connects multiple stakeholders by bridging the national-level institutional context, organisational-level university entrepreneurship education, and individual level entrepreneurial learning to promote student autonomy based on an understanding of students' learning expectations. This can help develop graduates with their ability for autonomous learning and autonomous entrepreneurial behaviour. The results of this study help to remind students that it is them, the learners, their expectations and input that can make the difference between the success or failure of their study. This would not only apply to entrepreneurship education but also to other fields of study. One key message from this study is that education can be encouraged and supported but cannot be “forced”. Mandatory entrepreneurship education is not a quick fix for the lack of university students’ innovation and entrepreneurship. More resources must be invested in enhancing the enterprise culture, thus making entrepreneurship education desirable for students

    Addressing infrastructure challenges posed by the Harwich Formation through understanding its geological origins

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    Variable deposits known to make up the sequence of the Harwich Formation in London have been the subject of ongoing uncertainty within the engineering industry. Current stratigraphical subdivisions do not account for the systematic recognition of individual members in unexposed ground where recovered material is usually disturbed - fines are flushed out during the drilling process and loose materials are often lost or mixed with the surrounding layers. Most engineering problems associated with the Harwich Formation deposits are down to their unconsolidated nature and irregular cementation within layers. The consequent engineering hazards are commonly reflected in high permeability, raised groundwater pressures, ground settlements - when found near the surface and poor stability - when exposed during excavations or tunnelling operations. This frequently leads to sudden design changes or requires contingency measures during construction. All of these can result in damaged equipment, slow progress, and unforeseen costs. This research proposes a facies-based approach where the lithological facies assigned were identified based on reinterpretation of available borehole data from various ground investigations in London, supported by visual inspection of deposits in-situ and a selection of laboratory testing including Particle Size Distribution, Optical and Scanning Electron Microscopy and X-ray Diffraction analyses. Two ground models were developed as a result: 1st a 3D geological model (MOVE model) of the stratigraphy found within the study area that explores the influence of local structural processes controlling/affecting these sediments pre-, syn- and post- deposition and 2nd a sequence stratigraphic model (Dionisos Flow model) unveiling stratal geometries of facies at various stages of accretion. The models present a series of sediment distribution maps, localised 3D views and cross-sections that aim to provide a novel approach to assist the geotechnical industry in predicting the likely distribution of the Harwich Formation deposits, decreasing the engineering risks associated with this stratum.Open Acces

    Development of in-vitro in-silico technologies for modelling and analysis of haematological malignancies

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    Worldwide, haematological malignancies are responsible for roughly 6% of all the cancer-related deaths. Leukaemias are one of the most severe types of cancer, as only about 40% of the patients have an overall survival of 10 years or more. Myelodysplastic Syndrome (MDS), a pre-leukaemic condition, is a blood disorder characterized by the presence of dysplastic, irregular, immature cells, or blasts, in the peripheral blood (PB) and in the bone marrow (BM), as well as multi-lineage cytopenias. We have created a detailed, lineage-specific, high-fidelity in-silico erythroid model that incorporates known biological stimuli (cytokines and hormones) and a competing diseased haematopoietic population, correctly capturing crucial biological checkpoints (EPO-dependent CFU-E differentiation) and replicating the in-vivo erythroid differentiation dynamics. In parallel, we have also proposed a long-term, cytokine-free 3D cell culture system for primary MDS cells, which was firstly optimized using easily-accessible healthy controls. This system enabled long-term (24-day) maintenance in culture with high (>75%) cell viability, promoting spontaneous expansion of erythroid phenotypes (CD71+/CD235a+) without the addition of any exogenous cytokines. Lastly, we have proposed a novel in-vitro in-silico framework using GC-MS metabolomics for the metabolic profiling of BM and PB plasma, aiming not only to discretize between haematological conditions but also to sub-classify MDS patients, potentially based on candidate biomarkers. Unsupervised multivariate statistical analysis showed clear intra- and inter-disease separation of samples of 5 distinct haematological malignancies, demonstrating the potential of this approach for disease characterization. The work herein presented paves the way for the development of in-vitro in-silico technologies to better, characterize, diagnose, model and target haematological malignancies such as MDS and AML.Open Acces

    Optical coherence tomography methods using 2-D detector arrays

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    Optical coherence tomography (OCT) is a non-invasive, non-contact optical technique that allows cross-section imaging of biological tissues with high spatial resolution, high sensitivity and high dynamic range. Standard OCT uses a focused beam to illuminate a point on the target and detects the signal using a single photodetector. To acquire transverse information, transversal scanning of the illumination point is required. Alternatively, multiple OCT channels can be operated in parallel simultaneously; parallel OCT signals are recorded by a two-dimensional (2D) detector array. This approach is known as Parallel-detection OCT. In this thesis, methods, experiments and results using three parallel OCT techniques, including full -field (time-domain) OCT (FF-OCT), full-field swept-source OCT (FF-SS-OCT) and line-field Fourier-domain OCT (LF-FD-OCT), are presented. Several 2D digital cameras of different formats have been used and evaluated in the experiments of different methods. With the LF-FD-OCT method, photography equipment, such as flashtubes and commercial DSLR cameras have been equipped and tested for OCT imaging. The techniques used in FF-OCT and FF-SS-OCT are employed in a novel wavefront sensing technique, which combines OCT methods with a Shack-Hartmann wavefront sensor (SH-WFS). This combination technique is demonstrated capable of measuring depth-resolved wavefront aberrations, which has the potential to extend the applications of SH-WFS in wavefront-guided biomedical imaging techniques

    Regularized interior point methods for convex programming

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    Interior point methods (IPMs) constitute one of the most important classes of optimization methods, due to their unparalleled robustness, as well as their generality. It is well known that a very large class of convex optimization problems can be solved by means of IPMs, in a polynomial number of iterations. As a result, IPMs are being used to solve problems arising in a plethora of fields, ranging from physics, engineering, and mathematics, to the social sciences, to name just a few. Nevertheless, there remain certain numerical issues that have not yet been addressed. More specifically, the main drawback of IPMs is that the linear algebra task involved is inherently ill-conditioned. At every iteration of the method, one has to solve a (possibly large-scale) linear system of equations (also known as the Newton system), the conditioning of which deteriorates as the IPM converges to an optimal solution. If these linear systems are of very large dimension, prohibiting the use of direct factorization, then iterative schemes may have to be employed. Such schemes are significantly affected by the inherent ill-conditioning within IPMs. One common approach for improving the aforementioned numerical issues, is to employ regularized IPM variants. Such methods tend to be more robust and numerically stable in practice. Over the last two decades, the theory behind regularization has been significantly advanced. In particular, it is well known that regularized IPM variants can be interpreted as hybrid approaches combining IPMs with the proximal point method. However, it remained unknown whether regularized IPMs retain the polynomial complexity of their non-regularized counterparts. Furthermore, the very important issue of tuning the regularization parameters appropriately, which is also crucial in augmented Lagrangian methods, was not addressed. In this thesis, we focus on addressing the previous open questions, as well as on creating robust implementations that solve various convex optimization problems. We discuss in detail the effect of regularization, and derive two different regularization strategies; one based on the proximal method of multipliers, and another one based on a Bregman proximal point method. The latter tends to be more efficient, while the former is more robust and has better convergence guarantees. In addition, we discuss the use of iterative linear algebra within the presented algorithms, by proposing some general purpose preconditioning strategies (used to accelerate the iterative schemes) that take advantage of the regularized nature of the systems being solved. In Chapter 2 we present a dynamic non-diagonal regularization for IPMs. The non-diagonal aspect of this regularization is implicit, since all the off-diagonal elements of the regularization matrices are cancelled out by those elements present in the Newton system, which do not contribute important information in the computation of the Newton direction. Such a regularization, which can be interpreted as the application of a Bregman proximal point method, has multiple goals. The obvious one is to improve the spectral properties of the Newton system solved at each IPM iteration. On the other hand, the regularization matrices introduce sparsity to the aforementioned linear system, allowing for more efficient factorizations. We propose a rule for tuning the regularization dynamically based on the properties of the problem, such that sufficiently large eigenvalues of the non-regularized system are perturbed insignificantly. This alleviates the need of finding specific regularization values through experimentation, which is the most common approach in the literature. We provide perturbation bounds for the eigenvalues of the non-regularized system matrix, and then discuss the spectral properties of the regularized matrix. Finally, we demonstrate the efficiency of the method applied to solve standard small- and medium-scale linear and convex quadratic programming test problems. In Chapter 3 we combine an IPM with the proximal method of multipliers (PMM). The resulting algorithm (IP-PMM) is interpreted as a primal-dual regularized IPM, suitable for solving linearly constrained convex quadratic programming problems. We apply few iterations of the interior point method to each sub-problem of the proximal method of multipliers. Once a satisfactory solution of the PMM sub-problem is found, we update the PMM parameters, form a new IPM neighbourhood, and repeat this process. Given this framework, we prove polynomial complexity of the algorithm, under standard assumptions. To our knowledge, this is the first polynomial complexity result for a primal-dual regularized IPM. The algorithm is guided by the use of a single penalty parameter; that of the logarithmic barrier. In other words, we show that IP-PMM inherits the polynomial complexity of IPMs, as well as the strong convexity of the PMM sub-problems. The updates of the penalty parameter are controlled by IPM, and hence are well-tuned, and do not depend on the problem solved. Furthermore, we study the behavior of the method when it is applied to an infeasible problem, and identify a necessary condition for infeasibility. The latter is used to construct an infeasibility detection mechanism. Subsequently, we provide a robust implementation of the presented algorithm and test it over a set of small to large scale linear and convex quadratic programming problems, demonstrating the benefits of using regularization in IPMs as well as the reliability of the approach. In Chapter 4 we extend IP-PMM to the case of linear semi-definite programming (SDP) problems. In particular, we prove polynomial complexity of the algorithm, under mild assumptions, and without requiring exact computations for the Newton directions. We furthermore provide a necessary condition for lack of strong duality, which can be used as a basis for constructing detection mechanisms for identifying pathological cases within IP-PMM. In Chapter 5 we present general-purpose preconditioners for regularized Newton systems arising within regularized interior point methods. We discuss positive definite preconditioners, suitable for iterative schemes like the conjugate gradient (CG), or the minimal residual (MINRES) method. We study the spectral properties of the preconditioned systems, and discuss the use of each presented approach, depending on the properties of the problem under consideration. All preconditioning strategies are numerically tested on various medium- to large-scale problems coming from standard test sets, as well as problems arising from partial differential equation (PDE) optimization. In Chapter 6 we apply specialized regularized IPM variants to problems arising from portfolio optimization, machine learning, image processing, and statistics. Such problems are usually solved by specialized first-order approaches. The efficiency of the proposed regularized IPM variants is confirmed by comparing them against problem-specific state--of--the--art first-order alternatives given in the literature. Finally, in Chapter 7 we present some conclusions as well as open questions, and possible future research directions
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