Dissertations / Theses on the topic 'Financial econometric'

Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises
Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises

There are numerous researches done in the last couple decades dedicated to the observation of impact of merges and acquisitions on the performance of the company. The topic is considered to be up-to-date, as still there is no common approach to evaluating of benefits mergers are about to bring to a new established entity. In this thesis the issue of post-merger financial performance is investigated on an example of three biggest energy companies in Europe: RWE, E.ON and Vattenfall. The aim of the thesis is to find out whether financial performance of chosen companies improves after the merger occurs. This target is elaborated with a help of the analysis of commonly used financial ratios in corporate finance and construction of two regression models, which explain the interrelations between basic indicator of the company's growth (net income), the fact of the merger and determined financial ratios. As an outcome of the research, a few findings were obtained, such as worsening of financial performance three to five years after the merger, with continuing improvement in further years, quite stable financial indicators before the merger, positive interconnection between the fact of the merger and the net income. Such outcomes might be considered as significant, though further research and elaboration of the topic can be performed in the future.

The goal of this dissertation is to use statistical tools to analyze specific financial risks that have played dominant roles in the US financial crisis of 2008-2009. The first risk relates to the level of aggregate stress in the financial markets. I estimate the impact of financial stress on economic activity and monetary policy using structural VAR analysis. The second set of risks concerns the US housing market. There are in fact two prominent risks associated with a US mortgage, as borrowers can both prepay or default on a mortgage. I test the existence of unobservable heterogeneity in the borrower's decision to default or prepay on his mortgage by estimating a multinomial logit model with borrower-specific random coefficients.

The goal of this dissertation is to use statistical tools to analyze specific financial risks that have played dominant roles in the US financial crisis of 2008-2009. The first risk relates to the level of aggregate stress in the financial markets. I estimate the impact of financial stress on economic activity and monetary policy using structural VAR analysis. The second set of risks concerns the US housing market. There are in fact two prominent risks associated with a US mortgage, as borrowers can both prepay or default on a mortgage. I test the existence of unobservable heterogeneity in the borrower's decision to default or prepay on his mortgage by estimating a multinomial logit model with borrower-specific random coefficients.

Thesis (Ph. D.)--University of California, Riverside, 2008. Thesis (Ph. D.)--University of California, Riverside, 2009.
Includes abstract. Title from first page of PDF file (viewed February 3, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 135-137). Includes bibliographical references (leaves ). Also issued in print.

Das moderne Finanzsystem ist komplex, dynamisch, hochdimensional und oftmals nicht stationär. All diese Faktoren stellen große Herausforderungen beim Messen des zugrundeliegenden Finanzrisikos dar, das speziell für Marktteilnehmer von oberster Priorität ist. Hochdimensionalität, die aus der ansteigenden Vielfalt an Finanzprodukten entsteht, ist ein wichtiges Thema für Ökonometriker. Ein Standardansatz, um mit hoher Dimensionalität umzugehen, ist es, Schlüsselvariablen auszuwählen und kleine Koeffizientenen auf null zu setzen, wie etwa Lasso. In der Finanzmarktanalyse kann eine solche geringe Annahme helfen, die führenden Risikofaktoren aus dem extrem großen Portfolio, das letztendlich das robuste Maß für finanzielles Risiko darstellt, hervorzuheben. In dieser Arbeit nutzen wir penalisierte Verfahren, um die ökonometrischen Maße für das finanzielle Risiko in hoher Dimension zu schätzen, sowohl mit nieder-, als auch hochfrequenten Daten. Mit Fokus auf dem Finanzmarkt, können wir das Risikonetzwerk des ganzen Systems konstruieren, das die Identifizierung individualspezifischen Risikos erlaubt.
Modern financial system is complex, dynamic, high-dimensional and often possibly non-stationary. All these factors pose great challenges in measuring the underlying financial risk, which is of top priority especially for market participants. High-dimensionality, which arises from the increasing variety of the financial products, is an important issue among econometricians. A standard approach dealing with high dimensionality is to select key variables and set small coefficient to zero, such as lasso. In financial market analysis, such sparsity assumption can help highlight the leading risk factors from the extremely large portfolio, which constitutes the robust measure for financial risk in the end. In this paper we use penalized techniques to estimate the econometric measures of financial risk in high dimensional, with both low-frequency and high-frequency data. With focus on financial market, we could construct the risk network of the whole system which allows for identification of individual-specific risk.

Since the Global Financial Crisis, the literature of financial networks analysis has been trying to investigate the changes in the financial networks structure, that led to the instability of the financial system. The Global Financial Crisis followed by the Great Recession costed taxpayers an unprecedented $14 trillion (Alessandri and Haldane, 2009), austerity and downturns in GDP. The dynamics of the financial networks transferred the collapse of a US housing market bubble into a large meltdown of the financial systems globally. The study of systemic risk and macro-prudential policy has come to the forefront to model and manage the negative externalities of monetary, fiscal and financial sector activities that can lead to system wide instabilities and failure. The dimensions of crisis propagation have been modelled as those that can spread cross-sectionally in domino like failures with global scope, or build up over time, as in asset bubbles. The cross sectional propagation of shocks that occur due to non-payment of debt or other financial obligations with the failure of a financial intermediary or a sovereign leading to the failure of other economic entities, is called financial contagion. Cross sectional analysis of financial contagion can be done using statistical methods or by network analysis. The latter gives a structural model of the interconnections in terms of financial obligations. This dissertation uses both approaches to model financial contagion. The applications include the study of systemic risk in Eurozone Sovereign crisis, the US CDS market and the global banking network. This is organized in three self-contained chapters Our contribution to the literature begins with the study of the dynamics of the market of the Credit Default Swap (CDS) contracts for selected Eurozone sovereigns and the UK. The EWMA correlation analysis and the Granger-causality test demonstrate that there was contagion effect since correlations and cross-county interdependencies increased after August 2007. Furthermore, the IRF analysis shows that among PIIGS, the CDS spreads of Spain and Ireland have the biggest impact on the European CDS spreads, whereas the UK is found not be a source of sovereign contagion to the Eurozone. Next we perform the empirical reconstruction of the US CDS network based on the real-world data obtained from the FDIC Call Reports, and study the propagation of contagion, assuming different network structures. The financial network shows a highly tiered core-periphery structure. We find that network topology matters for the stability of the financial system. The “too interconnected to fail” phenomenon is discussed and shown to be the result of highly tiered network with central core of so called super-spreaders. In this type of network the contagion is found to be short, without multiple waves, but with very high losses brought by the core of the network. Finally we study a global banking network (GBN) model based on the Markose (2012) eigen-pair approach and propose a systemic risk indices (SRI) which provide early warning signals for systemic instability and also the rank order of the systemic importance and vulnerability of the banking systems. The empirical model is based on BIS Consolidated Banking Statistics for the exposures of 19 national banking systems to the same number of debtor countries and the data obtained from Bankscope for the equity capital of these 19 national banking systems. The SRI is based on the ratio of the netted cross-border exposures of the national banking systems to their respective equity capital. The eigen-pair method stipulates that if the maximum eigenvalue of the network exceeds the capital threshold, there is cause for concern of a contagion. This is compared with the loss multiplier SRI proposed by Castrén and Rancan (2012). The latter is found to have no early warning capabilities and peaks well after the onset of the crisis in 2009 while the eigen-pair SRI gives ample warning by late 2006 that the cross border liabilities was unsustainable in respect of the equity capital of the national banking systems. We contribute to the literature by highlighting the efficacy of the network approach to systemic stability analysis of GBNs. In particular we develop an eigen-pair approach for GBNs and prove its usefulness in an early warning context.

In the modern economy, the role of financial markets is very important: thanks to them, the numerous financial assets that allow the global economic system to function are exchanged and allocated to the use deemed most efficient. However, it is well known that phenomena, known as speculative bubbles, often occur on the markets, which bring prices to values that do not correspond at all to the real value of the asset, generating problems not only for the efficient functioning of the markets themselves but also for the economy. This work addresses this issue at both a theoretical and an econometric and practical level. It is structured as follows: in the first chapter we will deal with the theoretical aspect of financial bubbles, focusing on the academic debate regarding their definition, origins, classification, and possible effects. Well-known historical examples of this phenomenon will be presented in the second chapter, to then move on to the econometric treatment of the topic: in the third chapter we will show some tests designed to detect speculative bubbles and to define a start and an end date. Finally, in the fourth chapter they will be applied to some time series to verify the results. In particular, we will consider three cases to date the phase of exuberance of 1929 and verify the possible presence of a bubble in the global stock market and especially in the technology sector, a widespread and much discussed idea. Furthermore, thanks to these analyses it will be possible to examine the differences between the results produced by the tests and procedures used.

L'objet de cette thèse est de mener une étude empirique sur le redressement des entreprises en difficultés en France. Dans un premier temps, nous utilisons une base de données originale construite à partir de dossiers de redressement ouverts au tribunal de commerce de Paris pour identifier les déterminants de l'issue de la procédure de réorganisation (continuation versus cession). Les résultats empiriques indiquent que la rentabilité de l'entreprise augmente la probabilité de continuation alors que la taille des actifs, le montant des créances privilégiées par rapport au montant des actifs et la proportion des actifs tangibles augmentent la probabilité d'une cession. Nous trouvons également que certaines causes de défaut influencent significativement l'issue de la procédure. Dans un deuxième temps, nous examinons la performance des entreprises réorganisées et ses déterminants. Premièrement, nous nous intéressons à l'exécution des plans de redressement. Nous montrons que la probabilité de succès d'un plan augmente avec l'âge de l'entreprise, la concentration des créances bancaires, le pourcentage du premier versement et la rentabilité du secteur d'activités. Deuxièmement, nous utilisons des mesures comptables pour évaluer la performance. En particulier, les résultats montrent que la taille des actifs de l'entreprise et sa rentabilité durant l'année de confirmation augmentent la probabilité qu'elle reste active pendant au moins quatre ans après la confirmation. Troisièmement, nous examinons la survie des entreprises réorganisées. Le modèle Cox estimé avec des variables dépendantes montre que la rentabilité de l'entreprise, sa liquidité et la rentabilité du secteur on un impact positif sur la survie alors que l'endettement a un effet de seuil négatif
This thesis provides an empirical analysis of the reorganization of bankrupt firms in the French context. On the one hand, we use an original data set from the commercial Court of Paris to study the particularity of the French bankruptcy law which consists in providing bankrupt firms with two forms of reorganization (continuation versus sale). Empirical results indicate that the probability of confirming a continuation plan increases with the firm's profitability and the fraction of intangible assets while it decreases with the size of the firm and the amount of secured debt relative to assets. Moreover, some causes of default have a significant impact on the reorganization form. On the other hand, we investigate the performance of the reorganized firms according to three criteria. First, we examine the consummation of the reorganization plans. We find that the age of the firm, the percentage of the plan's first payout, the relative size of banking claims, and the firms' industry profitability increase the probability of plans' consummation. Second, we assess accounting measures of performance prior to filing and following confirmation. In particular, logistic results show that larger firms with higher profitability and operating in profitable industries at the confirmation year are most likely to continue their operations for at least four years following confirmation. Third, we investigate the future prospects of reorganized firms using survival analysis techniques. The estimation of time-varying Cox model indicates that company's profitability, liquidity, and the industry profitability have positive effect on survival while leverage has a negative threshold effect

This thesis is a collection of three essays on financial econometrics with a common background in ultra-high frequency modeling of market activity. In the first essay, we propose an accurate and fast-to-estimate forecasting model for discrete valued time series with long memory and seasonality.1 The modelling is achieved with an autoregressive conditional Poisson process that features seasonality and heterogeneous autoregressive components (whence the acronym SHARP: Seasonal Heterogeneous AutoRegressive Poisson). Motivated by the prominent role of the bid-ask spread as a transaction cost for trading, we apply the SHARP model to forecast the bid-ask spreads of a large sample of NYSE equity stocks. [...]

This thesis is an empirical study of the volatility and correlation in financial markets, and consists of two parts: The first part is on econometric modeling of the volatility and correlation (Chapter 1). The second part is on the pricing implication of the correlation and volatility as risk factors (Chapter 2 and 3). The thesis begins with proposing a regime-switching multivariate GARCH model of volatilities and correlation. We incorporate the Markov-switching mechanism into the Constant Conditional Correlation model (CCC). The proposed model allows us to capture the different dynamics in both the volatilities and correlations in different regimes. It is particularly useful in examining the contemporaneous relationship between the unobservable volatility and correlation processes. We apply our model to the stock market index paired with two bond market indexes. Then in the second chapter, we estimate the risk premium for the average correlation in the cross-section of the US stock market. The average correlation is the cross-sectional average of the correlations between each pair of stocks in the stock market. We find there is a negative and statistically significant risk premium for the average correlation controlling for other factors such as Fama-French's SMB and HML as well as the liquidity factor and momentum factor. The third chapter focuses on the risk-return relation using a set of variance-related risk measures. We combine two lines of literature both of which find significant forecasting power of some risk measures for stock market returns: variance risk premium literature and studies on average variance-correlation decomposition. We illustrate how these two approaches can be related to each other, and empirically evaluate the relative importance of two approaches.

Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.

In the three chapters of my dissertation, I analyze the individual behaviors including health (vaccination and preventive care) and consumer (financial literacy) behaviors and the corresponding interventions by nonlinear econometric modeling. In the first chapter, I suggest an appropriate econometric model that investigates the effect of paid sick leave on workers' decision to receive the seasonal flu vaccination. For this investigation, I apply a Bayesian non-linear structural regression model with one-outcome and two-endogenous equations. The results of my estimation indicate that having paid sick leave affects workers' vaccination decisions differently based on their income levels. Low-income workers are willing to be vaccinated because they perceive the high cost of claiming paid sick leave. However, high-income workers are willing to be vaccinated because paid sick leave reduces the cost of vaccination for seasonal flu. In the second chapter, I suggest new econometric regression models that investigate the effect of "Don't Know" or "Refuse" (DK/RF) responses on parameter identification. I estimate the effect of group characteristics and financial education on the level of young respondents' objective financial knowledge and find the actual effects and biases by my suggested models. This study examines six questions about personal finance and selects covariates in the 2015 National Financial Capability Study (NFCS). Because these questions include DK/RF responses, a simple regression model that does not consider DK/RF responses could lead to misleading conclusions, such as gender/income difference and educational effectiveness in schools. In the last chapter, I investigate the effect of three health-related interventions including a doctor's recommendation, information about human papillomavirus (HPV), and HPV vaccination, on the misuse of cervical cancer screening including too-early screening, unnecessary HPV test, annual Pap test, and no Pap smear that are not recommended for women younger than 30 years. I examine the National Health Interview Survey conducted in 2015 and applies binary and multinomial logistic regression models. From the estimation result, I observe that doctor's recommendation plays a significant role in increasing the probability of receiving cervical cancer screening while it induces the too-early screening, unnecessary HPV testing, and overuse of Pap smears.
Ph. D.

This thesis consists of three self-contained chapters. Chapter I. The first chapter develops a dynamic general equilibrium model which includes financial intermediation and endogenous financial crises. Consistent with the data, financial crises occur out of prolonged (credit) boom periods and are initiated by a moderate adverse shock. The mechanism which gives rise to boom-bust episodes around financial crises is based on an interaction between the maturity mismatch of the financial sector and an agency problem which results in procyclical lending. I show how to model these features in a tractable way, giving a realistic representation of the financial sector's balance sheet and its lending behavior. The chapter provides empirical evidence on the behavior of the U.S. financial sector's market leverage which is (i) acyclical, (ii) rose mildly prior to the Great Recession, and (iii) increased sharply during the crisis; the model is consistent with these empirical facts. It also predicts and replicates the Great Recession, when confronted with a historical series of structural shocks. Finally, the framework is extended to include price rigidities, nominal debt contracts, and monetary policy. Within this version, I analyze the impact of monetary policy on financial stability and show that a U-shaped pattern of the policy target rate is most likely to increase financial instability. Chapter II. The second chapter models the economy as a time varying vector autoregression, consisting of economic and financial variables. The interest lies in the time varying response of these variables to a monetary policy shock. Monetary policy shocks are identified as the surprise component in policy announcements extracted from price changes in Federal Funds futures around such announcements. These monetary policy surprises enter the model as an exogenous variable. The framework is used to obtain evidence on the time varying response of stock prices to the monetary policy surprises. Stock prices always persistently decrease following a monetary tightening and more strongly than fundamentals imply - with an increase in risk-premia accounting for the difference. However, the response of stock prices varies over time. They decrease less during a boom and a perceived bubble period than during a recession. The findings suggest that so-called "leaning against the wind policies" may be ineffective since stock prices are less responsive during periods when such policies would disinflate asset bubbles using contractionary monetary policy. Chapter III. The third chapter augments a monetary dynamic general equilibrium model with a bubble as considered in [Miao_Wang_2015]. A bubble may exist in firms' stock market values and firms borrow against their inflated stock market values. Within this framework, I analyze the relation between monetary policy and the bubble. I find that contractionary monetary policy decreases the bubble which tightens borrowing constraints and amplifies the reaction of investment and output. These results are in contrast to the ones in Gali (2014) who considers a bubble of the classic rational type and finds that contractionary monetary policy can increase bubbles.

This thesis focuses on the effects of development assistance (ODA), private foreign commercial capital (PFX), domestic savings (SAV), the openness of the economy and producer prices on agricultural output, and on export and domestic shares of agricultural output in sub-Saharan Africa (SSA). This study uses panel data spanning 27 countries and the period 1970 to 1993.
The production function is a Cobb-Douglas type. Static export and domestic share equations are derived from a specification of the agricultural gross domestic product function. Transformed auto-regressive distributed-lag versions of the static share models are used to investigate long-run dynamics, persistence and implementation lags in the share response model.
Agricultural output is affected as follows. ODA, PFX and SAV have small positive or negative impact depending on agricultural region or economic policy environment. The impact of openness of the economy is negative in all agricultural regions, however, there is evidence of positive effect of openness within improved policy environment. None of these effects are statistically significant.
Export share is affected as follows. ODA, PFX and SAV have small positive impact in some agricultural regions and policy environments, both in the short-run and in the long-run. PFX is not significant anywhere. ODA is significant only when countries are grouped by policy environment in the short-run. SAV is significant in the short-run only in some regions, and significant in the long-run only in others. Openness has positive impact in the short-run. This is significant in many regions. Its long-run impact is mostly positive but not significant anywhere. The impact of producer price is mostly positive but not significant.
Efforts to encourage economic activities in rural communities such as improvements in domestic terms of trade in favor of agriculture, together with the provision of infrastructure are likely to stimulate output. Strategies to diversify and process agricultural exports in the face of falling agricultural commodity prices should be pursued.

The thesis "Four Essays on the Econometric Modelling of Volatility and Durations" consists of four research papers in the area of financial econometrics on topics of the modelling of financial market volatility and the econometrics of ultra-high-frequency data. The aim of the thesis is to develop new econometric methods for modelling and hypothesis testing in these areas. The second chapter introduces a new model, the time-varying GARCH (TV-GARCH) model, in which volatility has a smooth time-varying structure of either additive or multiplicative type. To characterize smooth changes in the (un)conditional variance we assume that the parameters vary smoothly over time according to the logistic transition function. A data-based modelling technique is used for specifying the parametric structure of the TV-GARCH models. This is done by testing a sequence of hypotheses by Lagrange multiplier tests presented in the chapter. Misspecification tests are also provided for evaluating the adequacy of the estimated model. The third chapter addresses the issue of modelling deterministic changes in the unconditional variance over a long return series. The modelling strategy is illustrated with an application to the daily returns of the Dow Jones Industrial Average (DJIA) index from 1920 until 2003. The empirical results sustain the hypothesis that the assumption of constancy of the unconditional variance is not adequate over long return series and indicate that deterministic changes in the unconditional variance may be associated with macroeconomic factors. In the fourth chapter we propose an extension of the univariate multiplicative TV-GARCH model to the multivariate Conditional Correlation GARCH (CC-GARCH) framework. The variance equations are parameterized such that they combine the long-run and the short-run dynamic behaviour of the volatilities. In this framework, the long-run behaviour is described by the individual unconditional variances, and it is allowed to vary smoothly over time according to the logistic transition function. The effects of modelling the nonstationary variance component are examined empirically in several CC-GARCH models using pairs of seven daily stock return series from the S&P 500 index. The results show that the magnitude of such effect varies across different stock series and depends on the structure of the conditional correlation matrix. An important feature of financial durations is the evidence of a strong diurnal variation over the trading day. In the fifth chapter we propose a new parameterization for describing the diurnal pattern of trading activity. The parametric structure of the diurnal component allows the duration process to change smoothly over the time-of-day according to the logistic transition function. The empirical results suggest that the diurnal variation may not always have the inverted U-shaped pattern for the trade durations as documented in earlier studies.

In recent years, the economies of Eastern Europe have experienced a complete breakdown of their political and economic structures in the transition process. These changes have led to the emergence of a financial sector, the infrastructure of which has had to be established practically from scratch. In Poland, the most important non-bank financial sector, the Warsaw Stock Exchange (WSE), has played an essential role in the privatisation process and in providing an alternative source of finance and investment amongst firms and households. In this thesis, we provide an econometric analysis of asset returns on the WSE. On the WSE, there exists a one-period censoring of asset returns moving beyond a pre-specified range introduced to limit speculative behaviour and volatility on the market. Our principal aim has been to incorporate such a feature into any analyses undertaken. Considering predictability, in an explicit intertemporal model of an investor's effective demand for a single asset facing a binding quantity constraint, we show that the market may not be predictable and return predictability on such constrained markets is non-trivial. Proposing an approach to model the predictability of returns, we find evidence of predictability of six main returns series on the WSE. Considering the volatility of asset returns, we investigate whether the finding for developed stock markets that negative shocks entering the market lead to a larger return volatility than positive shocks of a similar magnitude also applies to two emerging Eastern European markets. Concentrating on the WSE and the Budapest Stock Exchange, we therefore also examine whether the findings differ depending on the exchanges' institutional microstructures. We propose an approach to model the conditional volatility of returns on a quantity constrained market such as the WSE and find no evidence of an asymmetric impact of news on the volatility of returns on either exchange.

This study develops a theoretical framework for the analysis of regular patterns in public financial behaviour, and applies that framework in an empirical assessment of budgetary policies in the United States and in the Netherlands. Its purpose and scope are threefold. First, it sheds theoretical light on economic considerations guiding public financial behaviour in a dynamic model of optimal taxation. The resulting idea, that it may be sensible to smooth taxation over time,is subsequently extended to a more general model of the public finances, which involves spending, taxation, debt and money creation in an effort to control the government budget. Second, using modern econometric methods the practical relevance of this model is illustrated with estimations for the United States and the Netherlands. Third, the model is sufficiently flexible to allow for a number of more institutional insights. In this respect the emphasis is placed on the Dutch economy and public finances. The thesis thus engages economic theory, econometric technique and institutional and macroeconomic background in a combined effort to understand and evaluate regular patterns in public financial behaviour. Its findings have implications for each of these three areas of economic interest.

This Thesis contains an examination of the time-series properties of swap spreads, their relation with credit spreads and an estimation of the risk premium embedded in the swap spread curve. Chapter 2 introduces the main institutional aspects of swap markets, and studies the time-series properties of swap spreads. These are shown to be non-stationary and display a time-varying conditional volatility. Chapter 3 provides evidence of cointegration between corporate bond spreads and swap spreads. We estimate an error-correction model, including additional variables such as the level and slope of the yield curve, taking into account the exogenous structural break due to the crisis of August 1998. We find evidence that the relation between swap and credit spreads arises from the swap cash flows being indexed to Libor rates. Chapter 4 studies the risk premium in the term structure of the swap spreads, obtaining evidence that it is time-varying. The slope of the swap spread curve is shown to predict the changes in swap spreads. These results are relevant for the study of the risk premium in credit markets, and extend the existing literature on riskless Treasury securities. Chapter 6 develops the asymptotic properties of the quadratic variation estimator of the volatility of a continuous time diffusion process. We explore the case in which the number of observations tends to infinity, while the time between them remains fixed. For the case of a geometric Brownian motion, we show that the estimator is asymptotically biased, but the bias is a random variable that converges. We study the behaviour of this random variable via a simulation study, that shows that it typically has a "small" effect. We conclude by exploring some practical applications related the specification of the volatility for financial time series.

Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications.

In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation

function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used.

The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario.

The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear

to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability.

The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It

focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the

two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Doctorat en sciences économiques, Orientation économie
info:eu-repo/semantics/nonPublished

This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this

Natural disasters have always been and probably always will be a problem for humans and their settlements. With global warming seemingly increasing the frequency and strength of the climate related disasters, and more and more people being settled in urban centers, the ability to model and predict damage is more important than ever.The aim of this thesis has been to model and analyze a broad range of disaster types and the kind of impact that they have. By modeling damage indices for disaster types as different as hurricanes and volcanic eruptions, the thesis helps with understanding both similarities and differences between how disasters work and what impact they have on societies experiencing them. The thesis comprises four different chapters in addition to this introduction, where all of them include modeling of one or more types of natural disasters and their impact on real world scenarios such as local budgets, birth rates and economic growth.Chapter 2 is titled “Natural Disaster Damage Indices Based on Remotely Sensed Data: An Application to Indonesia". The objective was to construct damage indices through remotely sensed and freely available data. In short, the methodology exploits that one can use nightlight data as a proxy for economic activity. Then the nightlights data is matched with remote sensing data typically used for natural hazard modeling. The data is then used to construct damage indices at the district level for Indonesia, for different disaster events such as floods, earthquakes, volcanic eruptions and the 2004 Christmas Tsunami. The chapter is forthcoming as a World Bank Policy Research Paper under Skoufias et al. (2017a).Chapter 3 utilizes the indices from Chapter 2 to showcase a potential area of use for them. The title is “The Reallocation of District-Level Spending and Natural Disasters: Evidence from Indonesia" and the focus is on Indonesian district-level budgets. The aim was to use the modeled intensity from Chapter 2 to a real world scenario that could affect policy makers. The results show that there is evidence that some disaster types cause districts to move costs away from more general line items to areas such as health and infrastructure, which are likely to experience added pressure due to disasters. Furthermore, volcanic eruptions and the tsunami led to less investment into more durable assets both for the year of the disaster and the following year. This chapter is also forthcoming as a World Bank Policy Research Paper under Skoufias et al. (2017b).The fourth chapter, titled “Urban Global Impact of Earthquakes from 2004 through 2013", is a short chapter focusing on earthquake damage and economic growth. This chapter is an expansion of the index used in the previous two chapters, where we use global data instead of focusing on a single country. Using a comprehensive remotely sensed dataset of contour mapsof global earthquakes from 2004 through 2013 and utilizing global nightlights as an economic proxy we model economic impact in the year of the quakes and the year after. Overall, it is shown that earthquakes negatively impact local urban light emissions by 0.7 percent.Chapter 5 is named “A Whirlwind Romance: The Effect of Hurricanes on Fertility in Early 20th Century Jamaica" and deviates from the prior chapters in that it is a historical chapter that looks at birth rates in the early 1900s. The goal was to use the complete and long-term birth database for Jamaica and match this with hurricane data to check fertility rates. We create a hurricane destruction index derived from a wind speed model that we combine with data on more than 1 million births across different parishes in Jamaica. Analyzing the birth rate following damaging hurricanes, we find that there is a strong and significant negative effect of hurricane destruction on the number of births
Natural disasters have always been and probably always will be a problem for humans and their settlements. With global warming seemingly increasing the frequency and strength of the climate related disasters, and more and more people being settled in urban centers, the ability to model and predict damage is more important than ever.The aim of this thesis has been to model and analyze a broad range of disaster types and the kind of impact that they have. By modeling damage indices for disaster types as different as hurricanes and volcanic eruptions, the thesis helps with understanding both similarities and differences between how disasters work and what impact they have on societies experiencing them. The thesis comprises four different chapters in addition to this introduction, where all of them include modeling of one or more types of natural disasters and their impact on real world scenarios such as local budgets, birth rates and economic growth.Chapter 2 is titled “Natural Disaster Damage Indices Based on Remotely Sensed Data: An Application to Indonesia". The objective was to construct damage indices through remotely sensed and freely available data. In short, the methodology exploits that one can use nightlight data as a proxy for economic activity. Then the nightlights data is matched with remote sensing data typically used for natural hazard modeling. The data is then used to construct damage indices at the district level for Indonesia, for different disaster events such as floods, earthquakes, volcanic eruptions and the 2004 Christmas Tsunami. The chapter is forthcoming as a World Bank Policy Research Paper under Skoufias et al. (2017a).Chapter 3 utilizes the indices from Chapter 2 to showcase a potential area of use for them. The title is “The Reallocation of District-Level Spending and Natural Disasters: Evidence from Indonesia" and the focus is on Indonesian district-level budgets. The aim was to use the modeled intensity from Chapter 2 to a real world scenario that could affect policy makers. The results show that there is evidence that some disaster types cause districts to move costs away from more general line items to areas such as health and infrastructure, which are likely to experience added pressure due to disasters. Furthermore, volcanic eruptions and the tsunami led to less investment into more durable assets both for the year of the disaster and the following year. This chapter is also forthcoming as a World Bank Policy Research Paper under Skoufias et al. (2017b).The fourth chapter, titled “Urban Global Impact of Earthquakes from 2004 through 2013", is a short chapter focusing on earthquake damage and economic growth. This chapter is an expansion of the index used in the previous two chapters, where we use global data instead of focusing on a single country. Using a comprehensive remotely sensed dataset of contour mapsof global earthquakes from 2004 through 2013 and utilizing global nightlights as an economic proxy we model economic impact in the year of the quakes and the year after. Overall, it is shown that earthquakes negatively impact local urban light emissions by 0.7 percent.Chapter 5 is named “A Whirlwind Romance: The Effect of Hurricanes on Fertility in Early 20th Century Jamaica" and deviates from the prior chapters in that it is a historical chapter that looks at birth rates in the early 1900s. The goal was to use the complete and long-term birth database for Jamaica and match this with hurricane data to check fertility rates. We create a hurricane destruction index derived from a wind speed model that we combine with data on more than 1 million births across different parishes in Jamaica. Analyzing the birth rate following damaging hurricanes, we find that there is a strong and significant negative effect of hurricane destruction on the number of births

Modern banking theories provide a host of explanations for the existence of intermediaries, highlight their important influence on economic growth, delineate the risks inherent in the services they provide, and illustrate the market failures and real costs of bank failures that precipitate the need for regulation and oversight of the sector. This thesis is a collection of three essays that looks at three of these key aspects of financial intermediaries - the development of financial intermediaries, the function of the lender of last resort that has emerged as an important part of the safety net afforded to financial intermediaries, and the occurrence of financial crises. The first chapter of this thesis provides an introduction to the academic literature on financial intermediation covering different theories put forward to explain their emergence, and highlighting the risks inherent in their operation. It emphasizes the crucial functions they perform in the economy and makes a case for regulation and oversight of the sector to reduce the incidence and alleviate the effects of financial crises. The second chapter seeks to determine the policy and institutional factors that influence the development of financial institutions as measured across three dimensions - depth, efficiency, and stability. Applying the concept of the financial possibility frontier, developed by Beck and Feyen (2013) and formalized by Barajas et al. (2013b), we determine key policy variables affecting the gap between actual levels of development and benchmarks predicted by structural variables. Our dynamic panel estimation shows that inflation, trade openness, institutional quality, and banking crises significantly affect financial development. We also assess the impact of the policy variables across the different dimensions of development thereby identifying complementarities and potential trade-offs for policy makers. The third chapter models the role of the lender of last resort (LoLR) in a general equilibrium framework. We allow for heterogeneous agents and a risk-averse banking sector, and incorporate the frictions of endogenous default, liquidity, and money. Adverse supply shocks in monetary endowments trigger default, leading to deterioration in the value of bank assets, and subsequent bank illiquidity in some states of the world. LoLR intervention is then assessed with regards to its economy-wide effect on welfare, bank profitability, and the level of default. The results provide a justification for constructive ambiguity. The fourth chapter aims to provide an explanation for the incidence of financial crises by combining insights from agency theory and Minsky's financial instability hypothesis (Minsky, 1992) in a model with endogenous default. Our theoretical model shows that the probability of a financial crisis increases as the quality of shareholder information decreases. We then develop a measure for the quality of shareholder information following Simon (1989) and show that the market-wide quality of shareholder information: i) is poor (with no trend) in the Pre-SEC period (1840 to 1934); ii) improves substantially following the SEC reforms; and iii) gradually declines starting in the 1960s/70s until it is now back to pre-SEC levels. This matches up with the standard list of US financial crises (as in Reinhart and Rogoff 2009; Reinhart 2010) and supports our hypothesis that the likelihood of a financial crisis increases with deterioration in the quality of shareholder information.

Comovement is ubiquitous in financial markets. The evolution of asset characteristics, such as price, volatility or liquidity, exhibits a high degree of correlation across assets---a phenomenon that in this thesis will generically be denoted with the term comovement. The origins of such comovement are legion. In their investment decisions, economic agents are not only influenced by their idiosyncrasies---a large part of investment motivations are shared over a population. Demographics or the political situation can generate constraints that are similar for a large number of people. A country's geography can greatly influence the sectors in which it is most productive, which implies that many people are sometimes subject to the same risk factors. Moreover, it is well known that mimesis is part of human psychology, and that people mimic their peers even when taking personal decisions. For these reasons, and many more, financial markets have a very systematic character, and studying the nature and intensity of such comovement is important from a risk management point of view.

This thesis studies comovement in financial markets under three dimensions. First, I consider comovement in equity liquidity. The liquidity of an asset is the ease with which that asset can be bought or sold. Liquidity can be measured in various ways and the first chapter concludes that market movements of two different liquidity measures have the same origin. Second, I study the impact correlation comovement on the price of stocks. The correlations between stock returns and the market return evolve through time and are correlated themselves. The effect of this correlation comovement on asset prices is however ambiguous and there is not enough evidence to depict a clear image. Finally, I develop a model to investigate contagion dynamics in the secondary market for European sovereign bonds over the past two years. More particularly, I study whether changes in the bond price of one specific country have an impact the next day on the average bond price in Europe. The study concludes of that bonds of France, Ireland, Portugal, Spain and Italy have been most contagious, whereas the much more volatile Greek bonds have had little impact on the other European countries.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

We begin with the outlining the motivation of this research as there are still so many unanswered research questions on our complex financial and economic systems. The philosophical background and the advances of econometrics and econophysics are discussed to provide an overview of the stochastic and nonstochastic modelling and these disciplines are set as a central theme for the thesis. This thesis investigates the effectiveness of financial econometrics models such as Gaussian, ARCH (1), GARCH (1, 1) and its extensions as compared to econophysics models such as Power Law model, Boltzmann-Gibbs (BG) and Tsallis Entropy as statistical models of volatility in US S&P500, Dow Jones and NASDAQ stock index using daily data. The data demonstrate several distinct behavioural characteristics, particularly the increased volatility during 1998 to 2004. Power Laws appear to describe the large fluctuations and other characteristics of stock price changes. Surprisingly, these Power Laws models also show significant correlations for different types and sizes of markets and for different periods and sub-periods of markets. The results show the robustness of Power Law analysis, with the Power Law exponent (0.4 to 2.4) staying within the acceptable range of significance (83% to 97%), regardless of the percentage change in the index return. However, the procedure for testing empirical data against a hypothesised power-law distribution using a simple rank-frequency plot of the data and the data binning process can turn out to be a spurious result for the distribution. As for the stochastic processes such as ARCH (1) and GARCH (1, 1) the models are explicitly confined to the conditional behaviour of the data and the unconditional behaviour has often been described via moments. In reality, it is the unconditional tail behaviour that accounts for the tail behaviour and hence, we have to convert the unconditional tail behaviour and express the models as two-dimensional stochastic difference equation using the processes of Starica (Mikosch 2000). The results show the random walk prediction successfully describes the stock movements for small price fluctuations but fails to handle large price fluctuations. The Power Law tests prove superior to the stochastic tests when stock price fluctuations are substantially divergent from the mean. One of the main points of the thesis is that these empirical phenomena are not present in the stochastic process but emerge in the non-parametric process. The main objective of the thesis is to study the relatively new field of Econophysics and put its work in perspective relative to the established if not altogether successful practice of econometric analysis of stock market volatility. One of the most exciting characteristics of Econophysics is that, as a developing field, no models as yet perfectly represent the market and there is still a lot of fundamental research to be done. Therefore, we begin to explore the application of statistical physics method particularly Tsallis entropy to give a new insights into problems traditionally associated with financial markets. The results of Tsallis entropy surpass all expectations and it is therefore one of the most robust methods of analysis. However, it is now subject to some challenge from McCauley, Bassler et. al., as they found that the stochastic dynamic process (sliding interval techniques) used in fat tail distributions is time dependent.

Thesis (Ph.D.)--University of Western Sydney, 2008.
Thesis submitted to fulfil the requirements for the degree of Doctor of Philosophy in the School of Economics and Finance, College of Business, University of Western Sydney. Includes bibliography.

Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Program in Real Estate Development in Conjunction with the Center for Real Estate, 2012.
Page 143 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 135-137).
Inspired by Singapore's recent effort in building its new skyline in Maria Bay, the thesis intends to employ econometric structural modeling techniques to Singapore's office market for the period from 1975 to 2011. Using data collected from Singapore's Urban Redevelopment Authority, the regression models established by rent, demand and supply equations, dissect the market behavior and project an understanding of the underlying correlation and market mechanism. With which, the thesis forecasts for the next 10 years, in quarterly interval, the movement trajectory of Singapore's office market. Living and working as activities in this current milieu where role play in the system of power are essential to success was problematized; In the era when social and financial "cloud participation" has given rise to ebay, Facebook, Twitter and Wikipedia, what does work, live and play mean in this current environment where indulgence and consumption for its very own sake is very much part of the cultural lifestyle. Where is Architect in this financial time? In as much as it is about providing plausible answers, this thesis challenges the existing power system in the Real Estate industry, instead of taking dweller's spatial appropriation as guerrilla activities, the thesis proposes ways that channels private equity "financial cloud participation" into system of value production. Architectural proposition therefore works in way which turns these underlying power struggle scenarios into formal expression.
by Aoran Alex Sun.
S.M.in Real Estate Development
M.Arch.

The research examines how the banking sector in Zambia faired in the wake of the global financial crisis, and the ensuing global recession that followed. Even prior to the crisis, weaknesses within the Zambian Banking sector were already identified by a World Bank/IMF financial sector assessment. The research therefore aims to gain a better understanding of the potential destabilizing factors to the Zambia Banking sector, and provide key players (Policymakers, Regulators and Banks) with knowledge on how best to manage and overcome these adverse effects, in times of a financial crisis. A Vector Error Correction Model (VECM) is estimated using commonly identified macroeconomic and banking sector indicators from selected Anglophonic African countries that were affected by the crisis at the time. The selected variables include, Return on Assets (ROA); Non-Performing Loans (NPL); Foreign Assets (FA); Interbank Lending Rate (IBLR); Liquidity (LQD); Credit to Private Sector (PRV); Foreign Exchange Rate (FOREX); Inflation (INFL); Copper Price (CU); and a ‘dummy’ variable (CRISIS). The direction of causality between the variables is further established using the VAR Granger Causality Test. Results of the model suggests that although the CRISIS was found to cause the ROA, it had no significant effect on its outcome, implying that overall the crisis had very little effect on the Zambian banking sector’s profitability. It was the liquidity (LQD) variable instead which was found to have a significant effect on the ROA. In times of a financial crisis, it is therefore recommended that policy makers and regulators apply more stringent regulatory and monetary policy instruments. This would counter the adverse effects on the liquidity and profitability of the Banking sector, and thus ensure its stability.

This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior. A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model. The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries.

Financial liberalization in South Africa was a process that took the form of various legal reforms very a long period of time. This study uses quarterly financial data from 1969 quarter one to 2009 quarter four to analyse this process. The data used was pertinent to the financial liberalization theorem by McKinnon (1973) and Shaw (1973). The examination of the relationships between the various macro economic variables has important implications for effective policy formulation. The empirical analysis is carried out in four phases: the preliminary analysis, the principal component analysis (PCA), the cointegration analysis and pair wise Granger causality tests. The preliminary analysis examines trends over the sample period and reports the on the correlation between the selected variables. The PCA analysis was used to create indexes for financial liberalization, taking into account the phase wise nature of legal reforms. The generated index was representative of the process of financial liberalization from 1969 to 2009. A financial development index was also created using the various traditional measures of financial development and through PCA which investigated interrelationships among the variables according to their common sources of movement. Cointegration analysis is carried out using the Johansen cointegration procedure which investigates whether there is long-run comovement between South African economic growth and the selected macroeconomic variables. Where cointegration is found, Vector Error-Correction Models (VECMs) are estimated in order to examine the short-run adjustments. For robustness, many control variables were added into the model. The results showed that there are positive long run relationships between economic growth and financial liberalization, financial development and a negative relationship with interest rates. The Granger results suggested that the MS hypothesis does not manifest accurately in the South African data. The implications of the results were that financial liberalization has had positive effects on economic growth and thus any impediments to full financial liberalization must be removed albeit with considerations towards employment and local productivity. Financial development also possessed positive long run relationships with economic growth, although results differed based on the financial development proxy used. Thus, financial development must be improved primarily through liberalizing the banking sector and spurring savings.

Diese Arbeit enthält vier Aufsätze über die Beziehungen zwischen Handelsstrategien auf Aktienmärkten und dem Zustand des Marktes. Es werden ökonometrische Methoden angewendet um den Markteinfluss von limit order Platzierungen, die Eigenschaften von limit ordern sowie die Verwendung von versteckten ordern zu analysieren. Im Kapitel 1 quantifizieren wir die Effekte der limit order Platzierung auf Preisquotierungen am Börsenplatzes Euronext. Wir zeigen, dass eine limit order signifikante Informationen enthält und illustrieren inwieweit ihr Einfluss auf den Markt von ihren Charakteristika und dem Zustand des Orderbuchs abhängt. Das Kapitel 2 enthält empirische Resultate über die limit order Aktivität und den Markteinfluss von limit orders an der New Yorker NASDAQ Börse. Wir dokumentieren, dass Marktteilnehmer die Platzierung von limit orders mit kleinen Volumina präferieren, diese aber sofort nach ihrem Einsatz wieder löschen. Basierend auf der geschätzten Marktauswirkung einer limit order schlagen wir eine Methode zur Prognose ihres optimalen Volumens vor. Im Kapitel 3 werden die limit order-Strategien von Marktteilnehmern in intransparenten Märkten untersucht. Wir zeigen, dass die Position der sogenannten versteckten Liquidität im Orderbuch von diversen Variablen abhängt, die den Zustand des Marktes beschreiben. Die Daten suggerieren, dass Händler die Platzierung sogenannter hidden orders im Hinblick auf günstige Liquidität am Markt und dem "picking-off"-Risiko ausbalancieren. Im letzten Kapitel 4 präsentieren wir ein Softwaresystem zur Rekonstruktion von Orderbüchern und zur Extrahierung von Orderflussinformationen aus message stream Daten. Die Basismodule des Systems basieren auf allgemeinen Orderbuch-Ereignissen. Sie sind abstrakt gehalten und können so einfach auf beliebige Märkte mit elektronischen Orderbüchern angewendet werden.
In four essays, this thesis examines the interaction between traders'' strategies and the state of market by the econometric analysis of maket impact of limit order submission, the typical properties of order flow and the traders'' usage of hidden orders. Chapter 1 quantifies short-term and long-term effects of limit order submissions on quotes in Euronext. We show that limit orders have significant information content and the maginitude of their impact on the quotes depends on both the order''s characteritics and the state of limit order books (LOBs). Chapter 2 provides new empirical evidence on order submission activities and market impacts of limit orders at NASDAQ. We find that traders dominantly submit small size limit orders and cancell most of them immediately after submission. Based on the estimated market impact of orders, we propose a method to predict the optimal size of a limit order conditional on its position in the LOB and the desired impact. Chapter 3 analyzes traders'' decisions on using undisclosed orders in opaque markets. Our empirical findings show that market conditions affect traders'' order submission strategies and suggest that traders balance their hidden order placements to compete for the provision of liquidity and protect themselves against picking-off risk. Chapter 4 presents a program framework for reconstructing LOBs as well as extracting order flow information from message stream data. We design the basic modules of the system in an abstract layer based on common order events in limit order markets, so that it can be easily adapted to data at any limit order markets.