Robust Volatility Estimation and Analysis of the Leverage Effect

Using volatility estimation as the underlying commonality this thesis traverses the statistical problem of robust estimation of scale, through to the financial problem of valuing call options over stock. We use a large simulation study of robust scale estimators to benchmark a nonparametric volatility estimation procedure, which not only uses techniques which are particularly suited to observed financial returns, but also addresses the problem of bias in any robust volatility estimation procedure. Existing option pricing models are discussed with careful study of the assumed volatility and elasticity of volatility with respect to stock price relationships for each of these models. An option pricing formula is derived which extends existing methods, and provides a closed form solution which can be readily computed. Preliminary analysis of real price data suggests this model is able to explain observed leverage phenomena

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Climate Change and Geographic Information in Real Estate Research

Der Kampf gegen den Klimawandel und die Sicherstellung einer nachhaltigen Wirtschaftsweise sind wohl zwei der größten Herausforderungen unserer Epoche. Die Vermeidung von Treibhausgasemissionen, die Anpassung des Immobilienbestands an zunehmende Naturgefahren, Maßnahmen gegen den Mangel an bezahlbarem Wohnraum oder auch das Management eines Einkaufszentrum vor dem Hintergrund neuer Wettbewerber und verändertem Kundenverhalten - all diese unterschiedlichen Herausforderungen verlangen auch nach ökonomischen Entscheidungen und setzen, um Erfolgreich bewältigt zu werden, in tiefgreifendes Verständnis der zu Grunde liegenden Strukturen und Prozesse voraus. Ziel dieser Dissertation war es daher, die genannten Fragestellungen aus einer wissenschaftlichen Perspektive heraus zu beleuchten, um eine verlässliche und handlungsweisende Informationsbasis zu schaffen. Als Geograph hat sich der Autor dabei schwerpunktmäßig mit der Analyse raumbezogener Daten beschäftigt. Daneben kommen eine Reihe aktueller statistischer Analysemethoden sowie insbesondere Geographische Informationssysteme (GIS) zum Einsatz. Die fünf enthaltenen Aufsätze demonstrieren, wie räumliche Informationen und geostatistische Methoden auf sehr unterschiedliche Fragestellungen und räumliche Maßstäbe angewandt werden können - von Strukturen und Kundenströmen innerhalb von Einkaufszentren über Wohnungspreise auf städtischer Ebene bis zur Erfassung von Naturrisiken in ganz Deutschland. Wenn neben Forschern auch die Akteure der Immobilienwirtschaft eine Anregung durch die vorgestellten Ergebnisse und Methoden erfahren, können wirtschaftliche Aktivitäten und Planungsentscheidungen hoffentlich in Zukunft etwas mehr an den Zielen eines ökonomisch, ökologisch und sozial nachhaltigen Wirtschaftens ausgerichtet werden

Essays on SMEs insolvency risk

In light of the new Basel Capital Accord, Small and medium size enterprises (SMEs) play a fundamental role in the economic performance of major economies. Several lending communities proposed to treat SMEs as retail clients to optimize capital requirements and profitability. In this context, it is becoming critically important to have a detailed understanding of its risk behavior for appropriate pricing of credit risk. Thus, this thesis presents four essays on SMEs insolvency risk starting from chapter 3 through chapter 6 that investigates different dimensions of their default risk. My first essay makes distinction among SMEs that report operating cash flow and those which do not while modeling their default risk. However, I do not report any significant improvement in model’s classification performance when operating cash flow information is made available. Similarly, my second essay considers domestic and international SMEs separately while modelling their default risk and report almost identical classifications performance of the models’ developed for both the groups. The third essay compares the default risk attributes of micro, small and medium-sized firms respectively with SMEs. Test results suggest significant difference in the default risk attributes of only micro firms and SMEs. On a different line, my fourth essay deals with the methodological issues that have been witnessed recently in the bankruptcy literature that use hazard models for making bankruptcy predictions. This essay highlights the critical issues and provides appropriate guidance for the correct use of hazard models in making bankruptcy predictions. Here, I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. Empirical results show that my default definition performs significantly better than its respective counterparts in identifying distressed firms with superior goodness of fit measures across all econometric specifications. Detailed abstract of respective essays are as follows.Evidence pertaining to SMEs financing strongly motivates me to believe that firms which are unable to generate sufficient operating cash flow (OCF) are more susceptible to bankruptcy. However, the role of OCF in bankruptcy of SMEs lacks empirical validation. Thus, my first essay (chapter 3) investigates the role of operating cash flow information as predictors in assessing the creditworthiness of SMEs. One-year distress prediction model developed using significant financial information of United Kingdom SMEs over a period of 2000 to 2009 confirm that the presence of operating cash flow information does not improve the prediction accuracy of the distress prediction model.My second essay (chapter 4) considers domestic and international small and medium-sized enterprises (SMEs) of the United Kingdom separately while modelling their default risk. To establish the empirical validation, separate one-year default prediction models are developed using dynamic logistic regression technique that encapsulates significant financial information over an analysis period of 2000 to 2009. Almost an identical set of explanatory variables affect the default probability of domestic and international SMEs, which contradicts the need for separate default risk models. However, the lower predictive accuracy measures of the model developed for international SMEs motivate me to compare the weights of regression coefficients of the models developed for domestic and international firms. Test results confirm that four out of the nine common predictors display significant statistical differences in their weights. However, these differences do not contribute to the discriminatory performance of the default prediction models, given that I report very little difference in each model’s classification performance.A huge diversity exists within the broad category of Small and medium size enterprises (SMEs). They differ widely in their capital structure, firm size, access to external finance, management style, numbers of employees etc. Thus, my third essay (chapter 5) contributes to the literature by acknowledging this diversity while modeling credit risk for them, using a relatively large UK database, covering the analysis period between 2000 and 2009. My analysis partially employs the definition provided by the European Union to distinguish between ‘micro’, ‘small’, and ‘medium’ sized firms. I use both financial and non-financial information to predict firms’ failure hazard. I estimate separate hazard models for each sub-category of SMEs, and compare their performance with a SMEs hazard model including all the three sub-categories. I test my hypotheses using discrete-time duration-dependent hazard rate modelling techniques, which controls for both macro-economic conditions and survival time. My test results strongly highlight the differences in the credit risk attributes of ‘micro’ firms and SMEs, while it does not support the need to consider ‘small’ and ‘medium’ firms’ category separately while modelling credit risk for them, as almost the same sets of explanatory variables affect the failure hazard of SMEs, ‘small’ and ‘medium’ firms.My fourth essay (chapter 6) considers all serious and neglected concerns while developing discrete and continuous time duration dependent hazard models for predicting failure of US SMEs. I compare theoretical and classification performance aspects of three popular hazard models, namely discrete hazard models with logit and clog-log links and the extended Cox model. I report that discrete hazard models are superior to extended Cox models in making default predictions. I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. My empirical results show that my default definition performs significantly better than the default definitions which are only based on legal consequence or firms’ financial health in identifying distressed firms. In addition, my default definition also shows superior goodness of fit measures across all econometric specifications

Advances in Reliability, Risk and Safety Analysis with Big Data: Proceedings of the 57th ESReDA Seminar: Hosted by the Technical University of Valencia, 23-24 October, 2019, Valencia, Spain

The publication presents 57th Seminar organized by ESReDA that took place at the Polytechnic University of Valencia/Universitat Politècnica de Valencia, Spain. The Seminar was jointly organized by ESReDA and CMT Motores Termicos, a research unit at the Polytechnic University of Valencia. In accordance with the theme proposed for the Seminar, communications were presented that made it possible to discuss and better understand the role of the latest big data, machine learning and artificial intelligence technologies in the development of reliability, risk and safety analyses for industrial systems. The world is moving fast towards wide applications of big data techniques and artificial intelligence is considered to be the future of our societies. Rapid development of 5G telecommunications infrastructure would only speed up deployment of big data analytic tools. However, despite the recent advances in the these fields, there is still a long way to go for integrated applications of big data, machine learning and artificial intelligence tools in business practice. We would like to express our gratitude to the authors and key note speakers in particular and to all those who shared with us these moments of discussion on subjects of great importance and topicality for the members of ESReDA. The editorial work for this volume was supported by the Joint Research Centre of the European Commission in the frame of JRC support to ESReDA activities.JRC.C.3-Energy Security, Distribution and Market

Non parametric Estimation of high-frequency Volatility and Correlation Dynamics

This thesis addresses the problem of quantitatively evaluating the temporal dynamics that characterized financial time series. In particular, we perform an accurate analysis of the Fourier estimator, a newly proposed nonparametric methodology to measure ex-post volatility and cross-volatilities as functions of time, when financial assets are observed at different highfrequency levels over the day. The estimator has the peculiar feature to employ the observed data in their original form, therefore exploiting all the available information in the sample. We first show how to considerably improve the numerical performance of the Fourier method making possible the analysis of large sets of data, as it is usually the case with high-frequency series. Secondly, we use Monte Carlo simulation methods to study the behavior of three driving parameters in the estimation procedure, when the effects of both irregular sampling and microstructure noise are taken into account. The estimator is showed to be particularly sensitive to one of these quantities, which is in turn used to control the contribution of the above effects. Integrated financial correlation is also analyzed within two distinct comparative studies that involve other multivariate measures. The analysis is then extended to consider the entire evolution of the underlying correlation process. Finally, we propose a new class of nonparametric spot volatility estimators, which is showed to include the Fourier method as a particular case. The full limit theory under infill asymptotics in the pure diffusive settings of the class is derived. Empirical evidence in support of our conclusions is also provided

Ultrasound Imaging

In this book, we present a dozen state of the art developments for ultrasound imaging, for example, hardware implementation, transducer, beamforming, signal processing, measurement of elasticity and diagnosis. The editors would like to thank all the chapter authors, who focused on the publication of this book