This dissertation deals with issues of forecasting in financial markets. The first part of my dissertation is motivated by the observation that most parametric volatility models follow Engle's (1982) original idea of modelling the volatility of asset returns as a function of only past information. However, current returns are potentially quite informative for forecasting, yet are excluded from these models. The first and second chapters of this dissertation try to address this question from both a theoretical and an empirical perspective. The second part of this dissertation deals with the important issue of forecast evaluation and selection in unstable environments, where it is known that the existing methodology can generate spurious and potentially misleading results. In my third chapter, I develop a new methodology for forecast evaluation and selection in such an environment. In the first chapter, $\textit{Real-time GARCH}$, I propose a new parametric volatility model, which retains the simple structure of GARCH models, but models the volatility process as a mixture of past and current information as in the spirit of Stochastic Volatility (SV) models. This provides therefore a link between GARCH and SV models. I show that with this new model I am able to obtain better volatility forecasts than the standard GARCH-type models; improve the empirical fit of the data, especially in the tails of the distribution; and make the model faster in its adjustment to the new unconditional level of volatility. Further, the new model offers a much needed framework for specification testing as it nests the standard GARCH models. This chapter has been published in the $\textit{Journal of Financial Econometrics}$ (Smetanina E., 2017, Real-time GARCH, $\textit{Journal of Financial Econometrics}$, 15(4), 561-601.) In chapter 2, $\textit{Asymptotic Inference for Real-time GARCH(1,1) model}$, I investigate the asymptotic properties of the Gaussian Quasi-Maximum-Likelihood estimator (QMLE) for the Real-time GARCH(1,1) model, developed in the first chapter of this dissertation. I establish the ergodicity and $\beta$-mixing properties of the joint process for squared returns and the volatility process. I also prove strong consistency and asymptotic normality for the parameter vector at the usual $\sqrt{T}$ rate. Finally, I demonstrate how the developed theory can be viewed as a generalisation of the QMLE theory for the standard GARCH(1,1) model. In chapter 3, $\textit{Forecast Evaluation Tests in Unstable Environments}$, I develop a new methodology for forecast evaluation and selection in the situations where the relative performance between models changes over time in an unknown fashion. Out-of-sample tests are widely used for evaluating models' forecasts in economics and finance. Underlying these tests is often the assumption of constant relative performance between competing models, however this is invalid for many practical applications. In a world of changing relative performance, previous methodologies give rise to spurious and potentially misleading results, an example of which is the well-known ``splitting point problem''. I propose a new two-step methodology designed specifically for forecast evaluation in a world of changing relative performance. In the first step I estimate the time-varying mean and variance of the series for forecast loss differences, and in the second step I use these estimates to construct new rankings for models in a changing world. I show that the new tests have high power against a variety of fixed and local alternatives.

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The fundamental objective of the analysis of financial time series is to unveil the random mechanism, i.e. the probability law, underlying financial data. The effort to identify the truth that governs the observations involves proposing and estimating reasonable statistical models that well explain the empirical features of data. This thesis develops some new nonparametric tools that can be exploited in this context; the efficacy and validity of their use are supported by computational advancements and surging availability of large/complex (`big') data sets. Chapter 1 investigates the conditional first moment properties of financial returns. We propose multivariate extensions of the popular Variance Ratio (VR) statistic, aiming to test linear predictability of returns and weak-form market efficiency. We construct asymptotic distribution theories for the statistics and scalar functions thereof under the null hypothesis of no predictability. The imposed assumptions are weaker than those widely adopted in the literature, and in our view more credible with regard to the underlying data generating process we expect for stock returns. It is also shown that the limit theories can be extended to the long horizon and large dimension cases, and also to allow for a time varying risk premium. Our methods are applied to CRSP weekly returns from 1962 to 2013; the joint tests of the multivariate hypothesis reject the null at the 1% level for all horizons considered. Chapter 2 is about nonparametric estimation of conditional moments. We propose a local constant type estimator that operates with an infinite number of conditioning variables; this enables a direct estimation of many objects of econometric interest that have dependence upon the infinite past. We show pointwise and uniform consistency of the estimator and establish its asymptotic nomality in various static and dynamic regressions context. The optimal rate of estimation turns out to be of logarithmic order, and the precise rate depends on the Lambert W function, the smoothness of the regression operator and the dependence of the data in a non-trivial way. The theories are applied to investigate the intertemporal risk-return relation for the aggregate stock market. We report an overall positive risk-return relation on the S&P 500 daily data from 1950-2017, and find evidence of strong time variation and counter-cyclical behaviour in risk aversion. Lastly, Chapter 3 concerns nonparametric volatility estimation with high frequency time series. While data observed at finer time scale than daily provide rich information, their distinctive empirical properties bring new challenges in their analysis. We propose a Fourier domain based estimator for multivariate ex-post volatility that is robust to two major hurdles in high frequency finance: asynchronicity in observations and the presence of microstructure noise. Asymptotic properties are derived under some mild conditions. Simulation studies show our method outperforms time domain estimators when two assets with different liquidity are traded asynchronously.

This report examines two trading strategies on crude oil futures contracts by employing four time series models. Using daily prices of crude oil futures contracts in recent two years, we found that those models with better predictive ability will generate more profitable opportunities with lower risk from the result of simulated trading process. However, the two trading strategies associated with different models perform completely different. The empirical reasoning for the performance of different model-strategies is discussed, as well as applying the appropriate models and strategies in different markets. This work helps the research and development in statistical trading strategies / text

The first essay of this dissertation studies the determinants and effects of firms' participation in a voluntary pollution reduction program (VPR) initiated by government regulators. This research presents empirical evidence in support of the "enforcement theory" for VPRs, which predicts that (1) participation is rewarded by relaxed regulatory scrutiny; (2) the anticipation of this reward spurs firms to participate in the program; and (3) the program rewards regulators with reduced pollution. The results also indicate that firms' VPR participation, and pollutant reductions themselves, were prompted by a firm's likelihood of becoming a boycott target and/or being subject to environmental interest group lobbying for tighter standards.In the second essay, a nonparametric regression estimator which can accommodate two empirically relevant data environments is proposed. The first data environment assumes that at least one of the explanatory variables is discrete. In such an environment, a "cell" approach which estimates a separate regression for each discrete cell, has generally been employed. The second data environment assumes that one needs to estimate a set of regression functions that belong to different individuals. In both environments the proposed estimator attempts to reduce estimation error by incorporating extraneous data from the other individuals or "cells" when estimating the regression function for a given individual or "cell". The simulation results for the proposed estimator demonstrate a strong potential in empirical applications.In the third essay, the nonparametric approach proposed in the second essay is used to estimate the parameters of the short-term interest rate diffusion. The nonparametric estimators of the drift of the short rate proposed by Stanton (1997) and Jiang (1998) can produce spurious nonlinearities due to the persistent dependence and limited sampling period of interest rates. The simulations show that the proposed estimator significantly attenuates the spurious nonlinearities of Stanton's nonparametric estimator. An empirical study of the US term structure of interest rates is presented based on the proposed estimator and two other competing models. The results suggest that the estimation of the short rate diffusion parameters using additional data from yields of different maturities has significant economic implications on the valuation interest rate derivatives.

Doctor of Philosophy / Department of Economics / Lance Bachmeier / This dissertation consists of one essay in financial econometrics and two essays in quantitative industrial organization. The first essay studies the relationship between stock return volatility and current and prior shocks to oil price volatility. We study the behavior of aggregate stock markets as well as individual industry sectors. Our results show that lagged stock return volatility is the main determinant of current stock return volatility in aggregate markets, with oil price volatility providing no additional information that can be used to forecast stock return volatility. For individual industry sectors, we find a robust and stable prediction relationship only for the chemicals industry. Additional estimation exercises confirm the robustness of these results. The second essay uses a Bertrand-Nash price-competition framework to models a vertically integrated provider (VIP) that is a monopoly supplier of an essential input for downstream production. An input price that is “too high” can lead to inefficient foreclosure and one that is “too low” creates incentives for nonprice discrimination. The range of non-exclusionary input prices is circumscribed by the input prices generated on the basis of upper-bound and lower-bound displacement ratios. The admissible range of the ratio of downstream to upstream “price-cost” margins for the VIP is increasing in the degree of product differentiation and reduces to a single ratio in the limit as the products become perfectly homogeneous. The third essay explores the relationship between upstream input prices and downstream market exclusion under a Stackelberg quantity-competition framework. Market exclusion is a concern when input prices are “too high” and “too low” because it can result in inefficient foreclosure and sabotage, respectively. Consistent with the results obtained in the second essay, the safe harbor range of downstream to upstream “price-cost” margin ratios is decreasing in the degree of product homogeneity and approaches a single ratio in the limit as the products become perfectly homogeneous. This single margin ratio preserves equality between the VIP’s wholesale and retail “price-cost” margins. A key finding for competition policy is that the bounds of non-exclusionary input prices are markedly wider under Bertrand-Nash competition than they are under Stackelberg competition. Hence, it is critical that the antitrust and regulatory authorities understand the nature of the industry competition so that rules governing permissible conduct are properly calibrated to yield efficient outcomes.

We compare the traditional GARCH models with a semiparametric approach based on extreme value theory and find that the semiparametric approach yields more accurate predictions of Value-at-Risk (VaR). Using traditional parametric approaches based on GARCH and EGARCH to model the conditional volatility, we calculate univariate one-day ahead predictions of Value-at-Risk (VaR) under varying distributional assumptions. The accuracy of these predictions is then compared to that of a semiparametric approach, based on results from extreme value theory. For the 95% VaR, the EGARCH’s ability to incorporate the asymmetric behaviour of return volatility proves most useful. For higher quantiles, however, we show that what matters most for predictive accuracy is the underlying distributional assumption of the innovations, where the normal distribution falls behind other distributions which allow for thicker tails. Both the semiparametric approach and the conditional volatility models based on the t-distribution outperform the normal, especially at higher quantiles. As for the comparison between the semiparametric approach and the conditional volatility models with t-distributed innovations, the results are mixed. However, the evidence indicates that there certainly is a place for extreme value theory in financial risk management.

Cette thèse examine les canaux par lesquels la crise financière mondiale aurait une incidence sur les institutions financières islamiques et les instruments qui peuvent aider à contenir une crise dans un système bancaire dualiste. Notre échantillon couvre 467 banques conventionnelles et 90 banques islamiques dans 16 pays pour la période 2000-2008. Nous estimons la stabilité financière (z-score) dans les banques conventionnelles et les banques islamiques. Le z-score est devenu l’instrument le plus employé par les chercheurs en économie financière (Boyd et Runkle, 1993; Maechler, Mitra et Worrell, 2005; Beck et Laeven, 2006; Laeven et Levine, 2006; Hesse et Čihák, 2007, 2008, 2010; Mercieca, Laeven et Levine, 2009; Beck; Demirgüç-Kunt et Merrouche, 2010). A cette fin, nous utilisons un modèle d'estimation robuste et quantile. Cette thèse compare les causes de l’insolvabilité entre les banques islamiques et les banques conventionnelles dans les pays de la région du Moyen Orient et Afrique du nord (MOAN) et du Sud-est asiatique. À cet effet, nous utilisons différents facteurs microéconomiques, macroéconomiques et également un certain nombre d’autres indicateurs systémiques. / The international financial crisis naturally prompts the question of whether IIFS are robust and resilient or may be swept into crisis by a global wave and if so through what channels. This thesis considers channels through which the world financial crisis would affect IIFS, their features that may help contain it and those that may foster post crisis recovery in a dual banking system. Our sample covers 467 conventional banks and 90 Islamic banks in 16 countries for the period 2000-2008, a range advanced economies and emerging markets. We estimation the financial stability (z-score) in conventional and Islamic banks. The z-score has become a popular measure of bank soundness (Boyd and Runkle, 1993; Maechler, Mitra, and Worrell, 2005; Beck and Laeven, 2006; Laeven and Levine, 2006; Hesse and Čihák, 2007, 2008, 2010; Mercieca, Laeven and Levine, 2009; Beck; Demirgüç-Kunt and Merrouche, 2010). With a robust and a quantile estimation model, this empirical analysis explores causes of insolvency risk in Islamic and conventional banks in Middle East and North Africa (MENA) and Southeast Asian countries, by controlling for various factors, bank-by-bank data, macroeconomic and other system-wide indicators.

The international financial crisis naturally prompts the question of whether IIFS are robust and resilient or may be swept into crisis by a global wave and if so through what channels. This thesis considers channels through which the world financial crisis would affect IIFS, their features that may help contain it and those that may foster post crisis recovery in a dual banking system. Our sample covers 467 conventional banks and 90 Islamic banks in 16 countries for the period 2000-2008, a range advanced economies and emerging markets. We estimation the financial stability (z-score) in conventional and Islamic banks. The z-score has become a popular measure of bank soundness (Boyd and Runkle, 1993; Maechler, Mitra, and Worrell, 2005; Beck and Laeven, 2006; Laeven and Levine, 2006; Hesse and Čihák, 2007, 2008, 2010; Mercieca, Laeven and Levine, 2009; Beck; Demirgüç-Kunt and Merrouche, 2010). With a robust and a quantile estimation model, this empirical analysis explores causes of insolvency risk in Islamic and conventional banks in Middle East and North Africa (MENA) and Southeast Asian countries, by controlling for various factors, bank-by-bank data, macroeconomic and other system-wide indicators.

Cette thèse est articulée autour de trois risques financiers que sont : la liquidité, la contagion et le risque systémique. Ces derniers sont au centre de toutes les attentions depuis la crise de 2007-08 et resteront d’actualité à la vue des évènements que rencontrent les marchés financiers. Le premier chapitre de cette thèse présente un facteur de liquidité de financement obtenu par l’interprétation d’un phénomène de contagion en termes de risque de liquidité de marché. Nous proposons dans le second chapitre, une méta-mesure de cette liquidité de marché. Cette dernière tient compte de l’ensemble des dimensions présentes dans la définition de la liquidité en s’intéressant à la dynamique de plusieurs mesures de liquidité simultanément. L’objectif du troisième chapitre est de présenter une modélisation des rendements du marché permettant la prise en compte de la liquidité de financement dans l’estimation de la DCoVaR. Ainsi, ce travail propose une nouvelle mesure du risque systémique ayant un comportement contracyclique. Pour finir, nous nous intéressons à l’hypothèse de non-linéarité de la structure de dépendance entre les rendements de marché et ceux des institutions financières. Au cœur de la mesure du risque systémique, cette hypothèse apparait contraignante puisqu’elle n’a que peu d’impact sur l’identification des firmes les plus risquées mais peut compliquer considérablement l’estimation de ces mesures. / The aim of this thesis is to improve the management of financial risks through the employment of econometric methods. We focus on liquidity (market and funding), contagion and systemic risk, which have attracted a particularly large interest in the last years of financial turmoil. Firstly, we construct a funding liquidity factor based on the contagion effects that market liquidity risks encounter. This procedure can be useful to provide a better management of the liquidity mismatch among the assets and liabilities of a fund. Secondly, we propose a meta-measure of liquidity which incorporates multiple liquidity measures through the use of a conditional correlation model. As a result, we are able to detect drastic liquidity problems by using a single measure. Thirdly, we propose a new modeling framework for financial returns by adding an extra component related to funding liquidity to the standard DCoVaR model. In this way we obtain a countercyclical measure of systemic risk. Finally, we study to which extent a change in the estimation method affects the identification of systemically relevant Financial Institutions. In particular, the most popular measures aim at capturing the nonlinearity of the dependence structure between financial firms and market returns. We show, however, that similar results can be obtained by simply assuming a linear dependence, which can also largely simplify the estimation.