This thesis consists of three empirical essays on certain aspects of the behaviour of the stock market. The first study measures the impact of political reform on stock market volatility in Southeast Asian countries using a GARCH-family of model. We find that these major political changes have positive impact on the stability of the stock market. The second study employs an Autoregressive Distributed Lag model and Toda-Yamamoto (1995) Granger causality test to assess the interaction between Thailand's stock market and macroeconomic variables. We find long-run and short-run interactions exists between the stock market index and macro variables. The third study provides another look at the volatility of the stock exchange through variance decomposition. With a short-length dataset from Thailand, we find that discount rate news and cash flow news are equally important.

Since the activities of market participants can be influenced by financial outcomes, providing accurate forecasts of these financial outcomes can help participants to reduce the risk of adjusting to any market change in the future. Predictions of financial outcomes have usually been obtained by conventional statistical models based on researchers' knowledge. With the development of data collection and storage, an extensive set of explanatory variables will be extracted from big data capturing more economic theories and then applied to predictive methods, which can increase the difficulty of model interpretation and produce biased estimation. This may further reduce predictive ability. To overcome these problems, variable selection techniques are frequently employed to simplify model selection and produce more accurate forecasts. In this PhD thesis, we aim to combine variable selection approaches with traditional reduced-form models to define and forecast the financial outcomes in question (market implied ratings, Initial Public Offering (IPO) decisions and the failure of companies). This provides benefits for market participants in detecting potential investment opportunities and reducing credit risk. Making accurate predictions of corporate credit ratings is a crucial issue for both investors and rating agencies, since firms' credit ratings are associated with financial flexibility and debt or equity issuance. In Chapter 2, we attempt to determine market-implied credit ratings in relation to financial ratios, market-driven factors and macroeconomic indicators. We conclude that applying variable selection techniques, the least absolute shrinkage and selection operator (LASSO) and its extension (Elastic net) can improve predictive power. Moreover, the predictive ability of LASSO-selected models is clearly better than that of the benchmark ordered probit model in all out-of-sample predictions. Finally, fewer predictors can be selected into LASSO models controlled by BIC-type tuning parameter to produce more accurate out-of-sample prediction than its counterpart AIC-type selector. Next, the LASSO technique is further applied to binary event prediction. A bank's decision to go public by issuing an Initial Public Offering (IPO) is the binary object in Chapter 3, which transforms the operations and capital structure of a bank. Much of the empirical investigation in this area focuses on the determinants of the IPO decision, applying accounting ratios and other publicly available information in non-linear models. We mark a break with this literature by offering methodological extensions as well as an extensive and updated US dataset to predict bank IPOs. Combining the least absolute shrinkage and selection operator (LASSO) with a Cox proportional hazard, we uncover value in several financial factors as well as market-driven and macroeconomic variables, in predicting a bank's decision to go public. Importantly, we document a significant improvement in the model's predictive ability compared to standard frameworks used in the literature. Finally, we show that the sensitivity of a bank's IPO to financial characteristics is higher during periods of global financial crisis than in calmer times. Moving to another line of variable selection techniques, Bayesian Model Averaging (BMA) is combined with reduced-form models in Chapter 4. The failure of companies is closely related to the health of the whole economy, since the beginning of the most recent global crisis was the bankruptcy of Lehman Brothers. In this chapter, we forecast the failure of UK private firms incorporating with financial ratios and macroeconomic variables. Since two important financial crises and firm heterogeneities are covered in our dataset, the predictive powers of candidate models in different periods and cross-sections are validated. We first detect that applying BMA to the discrete hazard models can improve the predictive performance in different sub-periods. However, comparing the results with classified models, it should be noted that the Naive Bayes (NB) classifier provides slightly higher predictive accuracy than BMA models of discrete hazard models. Moreover, the predictive performance of the discrete hazard model and its BMA version are more sensitive to adding time or industry dummy variables than other competing models. Considering financial crisis or firm heterogeneity can influence the predictive power of each candidate model in the out-of-sample prediction of failure.

The current thesis is a collection of essays on costly signaling, smoothing (partial adjustment), and risk shifting through various pay outs by bank holding firms. The thesis is based on three chapters, or sections, which are through econometric investigations on the above mentioned topics. The major findings of the investigations are, one, a detailed firm level information content analysis of costly signaling by banks via different pay out methods, two, that partial adjustment or smoothing via pay outs can also be perceived as costly signals which is based on the information content of allied measures like bank specific speed of adjustments, and half-life periods, three, that rather than dividend pay outs share repurchases play relatively significant role in risk shifting exhibited by banking firms. Chapter 1 is devoted to the analysis of different types of dividend and other pay out signaling under information asymmetry (between the outsider shareholders of banks and the insider managers), and impact of various bank specific variables on the levels of pay outs/ signaling, thus revealing the information content of such signaling. Both panel data analysis and vector auto regression analysis have been conducted to achieve these findings. Another finding in this section is a comparative analysis between share repurchases and dividend pay outs by bank holding firms. Chapter2 is devoted to the investigation of bank specific partial adjustments of dividends, a modified partial adjustment model is used which is capable of investigating bank specific speeds of adjustments and half-life periods which may vary over periods. Such a model is an improvement over basic smoothing models in the standard literature which have mainly investigated the industry average speed of adjustment, and hence less efficient in investigating the bank specific information content of such measures. Chapter 3 provides analysis based on a system of equations model on, one, whether risk shifting has been exhibited by the bank holding firms for a comprehensive period between 1990-2015, and two, which are the specific pay out channels through which such risk shifting or wealth transfers have taken place.

This thesis analyzes models of financial markets that incorporate the possibility of arbitrage opportunities. The first part demonstrates how explicit formulas for optimal trading strategies in terms of minimal required initial capital can be derived in order to replicate a given terminal wealth in a continuous-time Markovian context. Towards this end, only the existence of a square-integrable market price of risk (rather than the existence of an equivalent local martingale measure) is assumed. A new measure under which the dynamics of the stock price processes simplify is constructed. It is shown that delta hedging does not depend on the "no free lunch with vanishing risk" assumption. However, in the presence of arbitrage opportunities, finding an optimal strategy is directly linked to the non-uniqueness of the partial differential equation corresponding to the Black-Scholes equation. In order to apply these analytic tools, sufficient conditions are derived for the necessary differentiability of expectations indexed over the initial market configuration. The phenomenon of "bubbles," which has been a popular topic in the recent academic literature, appears as a special case of the setting in the first part of this thesis. Several examples at the end of the first part illustrate the techniques contained therein. In the second part, a more general point of view is taken. The stock price processes, which again allow for the possibility of arbitrage, are no longer assumed to be Markovian, but rather only It^o processes. We then prove the Second Fundamental Theorem of Asset Pricing for these markets: A market is complete, meaning that any bounded contingent claim is replicable, if and only if the stochastic discount factor is unique. Conditions under which a contingent claim can be perfectly replicated in an incomplete market are established. Then, precise conditions under which relative arbitrage and strong relative arbitrage with respect to a given trading strategy exist are explicated. In addition, it is shown that if the market is quasi-complete, meaning that any bounded contingent claim measurable with respect to the stock price filtration is replicable, relative arbitrage implies strong relative arbitrage. It is further demonstrated that markets are quasi-complete, subject to the condition that the drift and diffusion coefficients are measurable with respect to the stock price filtration.

The recent financial crisis has revealed major shortcomings in the existing approaches for modeling credit derivatives. This dissertation studies various issues related to the modeling of credit derivatives: hedging of portfolio credit derivatives, calibration of dynamic credit models, and modeling of credit default swap portfolios. In the first part, we compare the performance of various hedging strategies for index collateralized debt obligation (CDO) tranches during the recent financial crisis. Our empirical analysis shows evidence for market incompleteness: a large proportion of risk in the CDO tranches appears to be unhedgeable. We also show that, unlike what is commonly assumed, dynamic models do not necessarily perform better than static models, nor do high-dimensional bottom-up models perform better than simpler top-down models. On the other hand, model-free regression-based hedging appears to be surprisingly effective when compared to other hedging strategies. The second part is devoted to computational methods for constructing an arbitrage-free CDO pricing model compatible with observed CDO prices. This method makes use of an inversion formula for computing the aggregate default rate in a portfolio from expected tranche notionals, and a quadratic programming method for recovering expected tranche notionals from CDO spreads. Comparing this approach to other calibration methods, we find that model-dependent quantities such as the forward starting tranche spreads and jump-to-default ratios are quite sensitive to the calibration method used, even within the same model class. The last chapter of this dissertation focuses on statistical modeling of credit default swaps (CDSs). We undertake a systematic study of the univariate and multivariate properties of CDS spreads, using time series of the CDX Investment Grade index constituents from 2005 to 2009. We then propose a heavy-tailed multivariate time series model for CDS spreads that captures these properties. Our model can be used as a framework for measuring and managing the risk of CDS portfolios, and is shown to have better performance than the affine jump-diffusion or random walk models for predicting loss quantiles of various CDS portfolios.

The 2007-2009 financial crisis has shed light on the importance of contagion and systemic risk, and revealed the lack of adequate indicators for measuring and monitoring them. This dissertation addresses these issues and leads to several recommendations for the design of an improved assessment of systemic importance, improved rating methods for structured finance securities, and their use by investors and risk managers. Using a complete data set of all mutual exposures and capital levels of financial institutions in Brazil in 2007 and 2008, we explore in chapter 2 the structure and dynamics of the Brazilian financial system. We show that the Brazilian financial system exhibits a complex network structure characterized by a strong degree of heterogeneity in connectivity and exposure sizes across institutions, which is qualitatively and quantitatively similar to the statistical features observed in other financial systems. We find that the Brazilian financial network is well represented by a directed scale-free network, rather than a small world network. Based on these observations, we propose a stochastic model for the structure of banking networks, representing them as a directed weighted scale free network with power law distributions for in-degree and out-degree of nodes, Pareto distribution for exposures. This model may then be used for simulation studies of contagion and systemic risk in networks. We propose in chapter 3 a quantitative methodology for assessing contagion and systemic risk in a network of interlinked institutions. We introduce the Contagion Index as a metric of the systemic importance of a single institution or a set of institutions, that combines the effects of both common market shocks to portfolios and contagion through counterparty exposures. Using a directed scale-free graph simulation of the financial system, we study the sensitivity of contagion to a change in aggregate network parameters: connectivity, concentration of exposures, heterogeneity in degree distribution and network size. More concentrated and more heterogeneous networks are found to be more resilient to contagion. The impact of connectivity is more controversial: in well-capitalized networks, increasing connectivity improves the resilience to contagion when the initial level of connectivity is high, but increases contagion when the initial level of connectivity is low. In undercapitalized networks, increasing connectivity tends to increase the severity of contagion. We also study the sensitivity of contagion to local measures of connectivity and concentration across counterparties --the counterparty susceptibility and local network frailty-- that are found to have a monotonically increasing relationship with the systemic risk of an institution. Requiring a minimum (aggregate) capital ratio is shown to reduce the systemic impact of defaults of large institutions; we show that the same effect may be achieved with less capital by imposing such capital requirements only on systemically important institutions and those exposed to them. In chapter 4, we apply this methodology to the study of the Brazilian financial system. Using the Contagion Index, we study the potential for default contagion and systemic risk in the Brazilian system and analyze the contribution of balance sheet size and network structure to systemic risk. Our study reveals that, aside from balance sheet size, the network-based local measures of connectivity and concentration of exposures across counterparties introduced in chapter 3, the counterparty susceptibility and local network frailty, contribute significantly to the systemic importance of an institution in the Brazilian network. Thus, imposing an upper bound on these variables could help reducing contagion. We examine the impact of various capital requirements on the extent of contagion in the Brazilian financial system, and show that targeted capital requirements achieve the same reduction in systemic risk with lower requirements in capital for financial institutions. The methodology we proposed in chapter 3 for estimating contagion and systemic risk requires visibility on the entire network structure. Reconstructing bilateral exposures from balance sheets data is then a question of interest in a financial system where bilateral exposures are not disclosed. We propose in chapter 5 two methods to derive a distribution of bilateral exposures matrices. The first method attempts to recover the balance sheet assets and liabilities "sample by sample". Each sample of the bilateral exposures matrix is solution of a relative entropy minimization problem subject to the balance sheet constraints. However, a solution to this problem does not always exist when dealing with sparse sample matrices. Thus, we propose a second method that attempts to recover the assets and liabilities "in the mean". This approach is the analogue of the Weighted Monte Carlo method introduced by Avellaneda et al. (2001). We first simulate independent samples of the bilateral exposures matrix from a relevant prior distribution on the network structure, then we compute posterior probabilities by maximizing the entropy under the constraints that the balance sheet assets and liabilities are recovered in the mean. We discuss the pros and cons of each approach and explain how it could be used to detect systemically important institutions in the financial system. The recent crisis has also raised many questions regarding the meaning of structured finance credit ratings issued by rating agencies and the methodology behind them. Chapter 6 aims at clarifying some misconceptions related to structured finance ratings and how they are commonly interpreted: we discuss the comparability of structured finance ratings with bond ratings, the interaction between the rating procedure and the tranching procedure and its consequences for the stability of structured finance ratings in time. These insights are illustrated in a factor model by simulating rating transitions for CDO tranches using a nested Monte Carlo method. In particular, we show that the downgrade risk of a CDO tranche can be quite different from a bond with same initial rating. Structured finance ratings follow path-dependent dynamics that cannot be adequately described, as usually done, by a matrix of transition probabilities. Therefore, a simple labeling via default probability or expected loss does not discriminate sufficiently their downgrade risk. We propose to supplement ratings with indicators of downgrade risk. To overcome some of the drawbacks of existing rating methods, we suggest a risk-based rating procedure for structured products. Finally, we formulate a series of recommendations regarding the use of credit ratings for CDOs and other structured credit instruments.

This thesis studies a mathematical problem that arises in modeling the prices of option contracts in an important part of global financial markets, the fixed income option market. Option contracts, among other derivatives, serve an important function of transferring and managing financial risks in today's interconnected financial world. When options are traded, we need to specify what the underlying asset an option contract is written on. For example, is it an option on IBM stock or on precious metal? Is it an option on Sterling-Euro exchange rate or on US dollar interest rates? Usually option markets are organized according to their underlying assets and they can be traded either on exchanges or over-the-counter. The scope of this thesis is the option markets on currency exchange rates and interest rates, which are less familiar to the general public than those of equities and commodities, and are mostly traded over-the-counter as bi-lateral agreements among large financial institutions such as investment banks, central banks, commercial banks, government agencies, and large corporations. Since early 1970's, the Black-Scholes-Merton option model has become the market standard of buying and selling standard option contracts of European style, namely calls and puts. Of particular importance is this ever more quantitative approach to the practice of option trading, in which the volatility parameter of the Black-Scholes-Merton's model has become the market "language'' of quoting option prices. Despite its tremendous success, the Black-Scholes-Merton model has exhibited a few well-known deficiencies, the most important of which are first, the assumption that the underlying asset is lognormally distributed and second, the volatility of the underlying asset's return is constant. In reality, the return distribution of an underlying asset can exhibit various level of tail behavior, ranging from "sub''-normal to normal, from lognormal to "super''-lognormal. Also the implied volatilities of liquidly traded options generally vary with both option strikes and option maturity. This variation with strike is termed the "volatility skew'' or the "volatility smile''. Naturally as market evolves, so does the model. People then start to look for the new standard. Among various successful extensions, models with constant elasticity of variance (CEV) prove to be able to generate enough range of return distributions while models with volatility itself being stochastic start to become popular in terms fitting the "smile'' or "skew'' phenomenon of option implied volatilities. In 2002, the combination of CEV model with stochastic volatility, particulary the SABR model, became the new market standard in fixed income option market. This is the starting point of this thesis. However, being the market standard also poses new challenges, which are speed and accuracy. Three mathematical aspects of the model prevent one from obtaining a strictly speaking closed form solution of its joint transition density, namely the nonlinearity from the CEV type local volatility function, the coupling between the underlying asset process and the volatility process, and finally the correlation between the two driving Brownian motions. We look at the problem from a PDE perspective where the joint transition density follows a linear second order equation of parabolic type in non-divergence form with coordinate-dependent coefficients. Particularly, we construct an expansion of the joint density through a hierarchy of parabolic equations after applying a financially justified scaling and a series of well designed transformations. We then derive accurate asymptotic formulas in both free-boundary conditions and absorbing-boundary conditions. We further establish an existence result to characterize the truncation error and examined extensively the derived formulas through various numerical examples. Finally we go back to the fixed income market itself and use our result to examine empirically whether today's option prices traded at different expiries contain information on predicting future levels of option prices, using ten-year over-the-counter FX option data from a major investment bank dealer desk. Our theoretical results for the joint density of the SABR model serve as a basis for banks and dealers to manage the forward smile risk of their fixed income option portfolio. Our empirical studies extend the forward concept from interest rate term structure modeling to interest rate volatility term structure modeling and examine the relationship between today's forward implied volatility and future spot implied volatility.

This dissertation investigates the sources of inefficiencies in financial sector and effects of these inefficiencies on the economy. In the first chapter, I analyze the effects of asset prices on financial institutions in a dynamic stochastic general equilibrium model including bank defaults and related agency costs. I find that pecuniary externalities exist in asset prices as decentralized banks do not internalize the effects of their lending on asset price distributions. These externalities lead to excess risk taking and leverage in the financial sector. Excess risk taking behavior deteriorates welfare of both depositors and banks in a stochastic economy. I show that a restricted social planner is able to improve welfare by limiting the leverage in the economy. In planner's problem, robust banking system is more resilient against the shocks. This in turn creates more stable economy with lower bankruptcy costs and increases welfare. Thus, I show that significant economic gains are possible with appropriate regulations in the financial sector. In the second chapter, I examine the welfare effects of pecuniary asset price externalities using a dynamic stochastic general equilibrium model. I show that decentralized financial system is socially inefficient due to pecuniary price externalities. I compare various regulations using quantitative welfare analysis. I find that bailout policies cause moral hazard problems and induce excess risk taking. Therefore, such policies worsen the inefficiency. However, macro-prudential policies limit the leverage and provide resilience against the systemic shocks. Thus, these policies mitigate distortions and improve welfare. Furthermore, I show that combination of bailout and prudential reserve requirement policies is pareto better than other regulations. Finally, I introduce credit default swaps (CDS) into the model and find that CDSs can mitigate the distortions. But the benefits of CDSs are limited to the size of systemic shocks. If systemic shocks are big enough, CDS linkages will make crisis contagious among the financial institutions. In the third chapter, I analyze the impacts of asymmetric information and imperfect monitoring on financial sector using a single period model with agency costs. I solve the model analytically comparing different levels of imperfect monitoring on heterogeneous banks. I find that information asymmetries and noises in monitoring encourage risk taking behaviors among the banks with low loan returns. I also show that these asymmetries cause inefficiently low lending among banks with high loan returns. In the extension of the model, I analyze government's incentive to prevent asymmetric information using regulatory tools such as stress tests. I analytically show that if the government is elected for short term and the rate of low return banks is high in the economy, government won't have incentive to announce real type of the banks.

The fall in housing market prices has played a major role in triggering the Great Recession. This led to the collapse of markets for mortgage-backed securities, and to a precipitous fall in their ratings. This thesis studies the downgrading of mortgaged-backed Collateralized Debt Obligation (CDO), and the factors that drive mortgage default loans. In Chapter 1, I look at the CDO market. The downgrading of the tranches of Collateralized Debt Obligation (CDO) products backed by real estate related assets has caused severe disruptions in the housing and financial markets. The rating agencies have been criticized for the opacity in the rating process of the CDO products and also for giving the CDO tranches higher ratings than they deserved. However, not enough attention has been paid to the decision making process of the agencies to downgrade the CDO tranches. We use data from Moody's CDO database to reconstruct the process through which Moody's eventually downgraded the tranches. We use a discrete hazard rate model to study the variables that were relevant in the downgrading of the tranches of the CDOs. The empirical results show that out of the many CDO specific variables relevant to their ratings made available by Moody's few have any explanatory power beyond the Moody's Deal Scores (MDS). We show that the MDS could be explained by the changes in the Case-Shiller Composite-20 Index and Markit ABX.HE indices. Further analysis shows that Moody's mostly relied on the changes in the Case-Shiller indexes in revising the MDS. In Chapter 2 I look at the factors that influence default rates. The chapter uses a Structural Vector Autoregression (SVAR) model to study the dynamics of the impact of unemployment and home price index shocks on mortgage default rates from 1979 to 2000 and from 2001 to 2010. We first fit the model to the 1979 to 2000 sample and forecast the changes in the national and regional mortgage default rates from 2001 to 2010. The model did a good job in forecasting the actual changes in the mortgage default rates from 2001 to 2007; however, it failed after 2008. The results for the 1979 to 2000 and 2001 to 2010 periods indicate that the dynamic response of the mortgage default rate to unemployment and home price index shocks changed at the national, regional and state levels after 2000. Unemployment and home price shocks seem to have become more important during the 2001 to 2010 period. The two shocks are responsible on average for about 60% of the movement in the regional mortgage default rates during this period. Except for the Pacific region, California and Florida, most of the variations in the mortgage default rates at the national, regional and state levels are explained by the unemployment shocks. The post 2000 results could be attributed to the increase in the number of mortgage loan borrowers who were more susceptible to unemployment and negative home price shocks.

In this thesis, we investigate the existence of relative arbitrage opportunities in a Markovian model of a financial market, which consists of a bond and stocks, whose prices evolve like Itô processes. We consider markets where investors are constrained to choose from among a restricted set of investment strategies. We show that the upper hedging price of (i.e. the minimum amount of wealth needed to superreplicate) a given contingent claim in a constrained market can be expressed as the supremum of the fair price of the given contingent claim under certain unconstrained auxiliary Markovian markets. Under suitable assumptions, we further characterize the upper hedging price as viscosity solution to certain variational inequalities. We, then, use this viscosity solution characterization to study how the imposition of stricter constraints on the market affect the upper hedging price. In particular, if relative arbitrage opportunities exist with respect to a given strategy, we study how stricter constraints can make such arbitrage opportunities disappear.