Credit Risk in a Network Economy

We develop a structural model of credit risk in a network economy. In particular, we are able to account for complex counterparty relationships,where one company may be indirectly affected by the credit risk of another company in the network. In this re-spect,we generalize Jarrow and Yu (2001)and Collin-Dufresne,Goldstein and Hugonnier (2003),but do so in the rich context of a structural form model. We provide closed form formulae for the price of risky debt and equity,which depend upon the lending/borrowing relationships in the economy. Our model applies to completely general lender/borrower relationships,including looping relationships. Our formulae can apply to cases where not only ?nancial ?ows but also operations are dependent across ?rms. In order to achieve these results,we use queueing theory. This paper thus represents one of the ?rst applications of queueing theory to ?nance.Credit Risk; Capital Structure; Queueing Networks

Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture

Currently, multi-core system is a predominant architecture in the computational word. This gives new possibilities to speedup statistical and numerical simulations, but it also introduce many challenges we need to deal with. In order to improve the performance metrics, we need to consider different key points as: core communications, data locality, dependencies, memory size, etc. This paper describes a series of optimization steps done on the SYMBOL model meant to enhance its performance and scalability. SYMBOL is a micro-funded statistical tool which analyses the consequences of bank failures, taking into account the available safety nets, such as deposit guarantee schemes or resolution funds. However, this tool, in its original version, has some computational weakness, because its execution time grows considerably, when we request to run with large input data (e.g. large banking systems) or if we wish to scale up the value of the stopping criterium, i.e. the number of default scenarios to be considered. Our intention is to develop a tool (extendable to other model having similar characteristics) where a set of serial (e.g. deleting redundancies, loop enrolling, etc.) and parallel strategies (e.g. OpenMP, and GPU programming) come together to obtain shorter execution time and scalability. The tool uses automatic configuration to make the best use of available resources on the basis of the characteristics of the input datasets. Experimental results, done varying the size of the input dataset and the stopping criterium, show a considerable improvement one can obtain by using the new tool, with execution time reduction up to 96 % of with respect to the original serial versionJRC.G.1-Financial and Economic Analysi

Multivariate financial econometrics: with applications to volatility modelling, option pricing and asset allocation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Static and Dynamic Modelling of Credit Default Risk: Tails, Moments, and Calibration

Credit risk modelling can take many different approaches. Each method has its strengths and weaknesses and studying a variety of them can help find new ways of performing credit risk analysis. We present here three different models, each classified either as static or dynamic, and structural or reduced-form. The static structural model from Lucas et al. (2000) helps us derive a moment behaviour theorem within the dynamic structural setting of Bush et al. (2011). For comparison, we also present the dynamic reduced-form model of Giesecke et al. (2012). A calibration exercise of the dynamic structural model is implemented and we study its performance through changing financial environment. This highlights the horse race between simplicity and efficiency of a model that still needs to be adequately addressed, as the results from the calibration show the difficulty of capturing the key financial environment’s aspects

Recent Advances in Technology, Strategy and Application of Sustainable Energy Systems

The global COVID-19 pandemic has had strong impacts on national and international freight, construction and tourism industry, supply chains, and has resulted in a rapid decline in the demand for traditional energy sources. In fact, research has outlined that urban areas depend on global supply chains for their day-to-day basic functions, including energy supplies, food and safe access to potable water. The disruption of global supply chains can leave many urban areas in a very vulnerable position, in which their citizens may struggle to obtain their basic supplies, as the COVID-19 crisis has recently shown. Therefore, solutions aiming to enhance local food, water and energy production systems, even in urban environments, have to be pursued. The COVID-19 crisis has also highlighted in the scientific community the problem of people’s exposure to outdoor and indoor pollution, confirmed as a key element for the increase both in the transmission and severity of the contagion, on top of involving health risks on their own. In this context, most nations are going to adopt new preferential policies to stimulate the development of relevant sustainable energy industries, based on the electrification of the systems supplied by renewable energy sources as confirmed by the International Energy Agency (IEA). Thus, while there is ongoing research focusing on a COVID 19 vaccine, there is also a need for researchers to work cooperatively on novel strategies for world economic recovery incorporating renewable energy policy, technology and management. In this framework, the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference provides a good platform for researchers and other experts to exchange their academic thoughts, promoting the development and improvements on the renewable energy technologies as well as their role in systems and in the transition towards sustainable energy systems. The 14th SDEWES Conference was held in Dubrovnik, Croatia. It brought together around 570 researchers from 55 countries in the field of sustainable development. The present Special Issue of Energies, specifically dedicated to the 14th SDEWES Conference, focuses on four main fields: energy policy for sustainable development, biomass energy application, building energy saving, and power plant and electric systems

Information Propagation in Financial Markets

This dissertation consists of three essays which examine information flows through financial markets and across firms, and investigates the factors affecting the process of information dissemination. The first essay examines whether the announcement of a credit rating change for a given firm contains information pertinent to the valuations of intra-industry peer firms. I identify an information spillover effect on peer firms surrounding credit rating downgrades. Further, I find that the post-announcement spillover effects are indicative of an overreaction in the market’s response to the downgrade announcement. Peer firms exhibit predictability in their post-announcement returns as a function of their relative transparency. The second essay explores the relation between instances of credit rating initiations and stock market liquidity. Traditional finance literature holds the view that liquidity is impaired as a function of information asymmetry. Additionally, that credit ratings have been shown to reduce information asymmetry. This study uses instances of new credit ratings to examine the change in stock market liquidity surrounding the announcement of the new rating. My results suggest that rating initiations improve in the liquidity of the newly rated firm’s equity and that managers exploit this price support through seasoned equity offerings. The third essay investigates information flows through the Social networks of board members. I find that the degree to which a CEO and her directors overlap in Social communities affects the governance of the firm and that these effects are conditional upon the adverse reputation costs faced by the board. For firms whose boards face relatively lower (higher) potential adverse reputation costs to bad behavior, clustering is associated with poorer (better) governance and greater (lesser) expropriation by managers

Three essays on the global financial crisis of 2007-2008

JEL Classification Codes: G01, E5The present Doctoral Thesis is comprised of three empirical essays addressing the Global Financial Crisis of 2007 – 2008. The said essays analyse distinct, but interconnected issues pertaining to this fundamental research topic. In the first paper, a careful examination is conducted in order to ascertain whether the ‘Subprime’ Crisis in the U.S.A. might have been duly forecasted using publicly available data. This hypothesis is confirmed by using two distinctive methodologies applied to a set of financial indicators, which independently confirm the predictability of the said financial shock. The second paper addresses the degree of heterogeneity of banking responses in forty two countries in the wake of the Global Financial Crisis. By employing a novel methodology – the Heterogeneous Regime-Switching Model (HRSM) –, representative country banking institutions worldwide are deemed to have had quite distinctive and heterogeneous responses to the onset of the global systemic event under scrutiny, and these responses may be grouped according to certain clusters. The third paper addresses the impact of the Global Financial Crisis upon the Euro Area and corresponding sovereign debt schedules. An updated academic survey is first conducted regarding the topic of the impact of excessive Member States’ sovereign debt in the wake of the systemic breakdown. The survey is followed by an empirical study using quadratic econometric specifications demonstrating that the over-accumulation of sovereign debt in the Euro Area is strongly associated with the diminishment of output growth in the latter Area, and that sound sovereign debt thresholds in key Euro Area Member States have been unwisely breached.A presente Tese Doutoral abarca três ensaios académicos empíricos que analisam a Crise Financeira Global de 2007 – 2008. Estes ensaios analisam questões científicas distintas, mas interligadas entre si, relativamente a este fundamental tópico de investigação. O primeiro ensaio investiga cuidadosamente a hipótese segundo a qual a Crise ‘Subprime’ nos E.U.A. poderia ter sido adequadamente prevista por recurso a dados publicamente disponíveis. Esta hipótese é verificada por recurso a duas metodologias alternativas, devidamente aplicadas a um conjunto de indicadores estritamente financeiros, sendo confirmada a previsibilidade do dito choque financeiro. O segundo ensaio investiga, no seguimento da Crise Financeira Global, o grau de heterogeneidade dos comportamentos de instituições financeiras em quarenta e dois países. Utilizando uma inovadora metodologia – intitulada Heterogeneous Regime-Switching Model (HRSM) –, constata-se que os distintos sistemas financeiros nacionais reagiram de forma diferenciada e heterogénea ao choque financeiro global sob estudo, sendo estas respostas passíveis de serem agrupadas em certas categorias. O terceiro ensaio investiga o impacto económico da Crise Financeira Global sobre a Zona Euro e, em particular, sobre a dinâmica da respectiva dívida soberana. Primeiramente, é elaborado um survey académico actualizado relativamente ao tópico do excessivo endividamento público e respectivo impacto sobre o produto económico, prestando-se particular atenção ao contexto do choque sistémico sob análise. Este survey académico é seguido de uma aplicação empírica envolvendo especificações econométricas quadráticas que atestam que a excessiva acumulação de dívida soberana na Zona Euro está associada a um processo de decrescimento económico da dita Zona, e que níveis óptimos associados a rácios de dívida pública em determinados Estados Membros foram imprudentemente ultrapassados

Making heads or tails of systemic risk measures

This paper shows that the CoVaR,$\Delta$-CoVaR,CoES,$\Delta$-CoES and MES systemic risk measures can be represented in terms of the univariate risk measure evaluated at a quantile determined by the copula. The result is applied to derive empirically relevant properties of these measures concerning their sensitivity to power-law tails, outliers and their properties under aggregation. Furthermore, a novel empirical estimator for the CoES is proposed. The power-law result is applied to derive a novel empirical estimator for the power-law coefficient which depends on $\Delta\text{-CoVaR}/\Delta\text{-CoES}$. To show empirical performance simulations and an application of the methods to a large dataset of financial institutions are used. This paper finds that the MES is not suitable for measuring extreme risks. Also, the ES-based measures are more sensitive to power-law tails and large losses. This makes these measures more useful for measuring network risk but less so for systemic risk. The robustness analysis also shows that all $\Delta$ measures can underestimate due to the occurrence of intermediate losses. Lastly, it is found that the power-law tail coefficient estimator can be used as an early-warning indicator of systemic risk.Comment: Revised version of the $\Delta$-CoES paper, now with a better estimator and clear theoretical results. Main body: 22 pages. Appendix contains: proofs, explanation of the data cleaning/pre-processing procedure, supplementary figures, tables and details of the software/computer setu

Topics in financial market risk modelling

PhD ThesisThe growth of the financial risk management industry has been motivated by the increased volatility of financial markets combined with the rapid innovation of derivatives. Since the 1970s, several financial crises have occurred globally with devastating consequences for financial and non-financial institutions and for the real economy. The most recent US subprime crisis led to enormous losses for financial and non-financial institutions and to a recession in many countries including the US and UK. A common lesson from these crises is that advanced financial risk management systems are required. Financial risk management is a continuous process of identifying, modeling, forecasting and monitoring risk exposures arising from financial investments. The Value at Risk (VaR) methodology has served as one of the most important tools used in this process. This quantitative tool, which was first invented by JPMorgan in its Risk-Metrics system in 1995, has undergone a considerable revolution and development during the last 15 years. It has now become one of the most prominent tools employed by financial institutions, regulators, asset managers and nonfinancial corporations for risk measurement. My PhD research undertakes a comprehensive and practical study of market risk modeling in modern finance using the VaR methodology. Two newly developed risk models are proposed in this research, which are derived by integrating volatility modeling and the quantile regression technique. Compared to the existing risk models, these two new models place more emphasis on dynamic risk adjustment. The empirical results on both real and simulated data shows that under certain circumstances, the risk prediction generated from these models is more accurate and efficient in capturing time varying risk evolution than traditional risk measures. Academically, the aim of this research is to make some improvements and extensions of the existing market risk modeling techniques. In practice, the purpose of this research is to support risk managers developing a dynamic market risk measurement system, which will function well for different market states and asset categories. The system can be used by financial institutions and non-financial institutions for either passive risk measurement or active risk control

Stochastic volatility models in financial econometrics: an application to South Africa

Thesis (Ph.D.)--University of the Witwatersrand, Faculty of Commerce, Law & Management, School of Economic and Business Sciences, 2015.The dissertation carries out a study to understand asset price behaviour in South Africa. This is investigated through the application of stochastic volatility models to trace the characteristics of high frequency financial data; daily temperature, exchange rates, interest rates, stock and house prices. Innovation in the derivatives market has seen the introduction of weather derivatives as a risk mitigation tool against adverse weather movements. Chapter Two applies three different time series models of temperature to estimate payoffs to determine which method offers the best hedging strategy in four South African cities. Results from the study suggest that the seasonality GARCH method of estimating payoffs for temperature based weather derivatives offers superior performance compared to the Cumulative Cooling Degree Days (CDD) and the historical method. This suggests that the seasonality GARCH method can be applied in these cities to hedge against adverse temperature movements. In Chapter three we consider the estimation methodology for jump diffusion models and GARCH models. Chapter four investigates volatility on exchange rate data. Use is made of the british pound/south african rand, euro/south african rand and u.s dollar/ south african rand exchange rates. The research introduces a jump diffusion model to trace the behaviour of exchange rate data. Estimation results are able to match the summary statistics in mean, variance, skewness and kurtosis. Results from the model can also explain the volatility smile for short and medium term maturities. A fat tailed GARCH model is introduced to capture the persistence in volatility on exchange rate data. Results from this chapter have an implication for pricing currency options to offer leverage to organisations affected by exchange rate risk. Chapter five extends the analysis to study the behaviour of short term interest rates, making use of the 90 Day Treasury bill (T-Bill) rate. The chapter considers a variant application of the Chan et al. (1992) model for short term interest rates wherein a jump diffusion model is introduced. The results match the summary statistics equivalent suggesting the capability of the model specification. Splitting the estimation period suggests that the jump size is highest post inflation target though with a smaller intensity. However, the 90 Day T-Bill shows higher volatility after inflation targeting though with a lesser intensity. These findings have a bearing on valuation of short term interest derivatives and also investigating multi factor models of interest rates. In chapter six four vi sectors (banking, mining, media and leisure) are considered to explore movements in stock prices. A jump diffusion model is applied to get estimation results. The results confirm related studies that stock prices have incidents of volatility which can be captured by a jump diffusion model. The results also shed light on the importance of portfolio diversification considering the different results across the sectors investigated. The implication also lies in understanding market efficiency. Chapter seven applies the jump diffusion model on house prices to understand more on the drivers of volatility on house prices. The interesting results on this chapter can be summarised as follows; the four different house segments have almost similar jump sizes though the small house price segment has highest intensity. This can point to expectations and volatility from participants in this segment at a higher level than for other segments over different regimes over the study period. The estimated higher moments were not normalised as had happened for the three previous chapters after introducing the jump diffusion model. Results from this chapter have an application to valuing mortgage premium across different house price segments. It is recommended that rigorous research on asset prices using various approaches be considered as it goes a long way in informing policy makers and investors to mitigate risk in an environment of volatile asset prices. With the growing interest in weather derivatives world-wide, there is a need to educate farmers, government entities, potential counter-parties and other organisations affected by weather related risk on the importance of weather derivatives so that a foundation is laid for trading in this special type of insurance