This thesis introduces the eBAEL (extended Empirical Balanced Augmented Likelihood) method for general analytical moment conditions. It proves that eBAEL has a X2 - limit distribution and provides a consistent estimator. Numerical results demonstrate that the eBAEL ratio statistics exhibits less bias than AEL ratio statistics and has smaller type I errors. This thesis also presents a general framework for calibrating financial agent-based models using eBAEL, where the aim is to find the model parameter for which the true model moments match the given empirical values. It is demonstrated that our proposed approach is able to retrieve that parameter with probability approaching one as the number of simulations increase. Furthermore this thesis demonstrates that the EL approach may also be used for estimating financial agent-based models. In contrast to calibration, estimation via moment matching in particular emphasizes that empirical moments are estimates themselves and the aim is to find a parameter configuration for which the true model moments and true empirical moments coincide. As a numerical benchmark case, the parameters of a Geometric Brownian Motion are calibrated and estimated from its simulated sample paths in comparison to the SMM. In this case the EL approach is able to provide the best mean squared errors for both calibration and estimation and in particular is the most robust calibration method. In terms of calibration efficiency this robustness holds figuratively, as the SMM is only more efficient in cases where it provides worse mean squared errors. Additionally, this thesis also estimates an actual agent-based model of a financial market against empirical moments that are generated at some known model parameter setting. Similarly, the resulting EL mean squared errors are mostly better than those of the SMM.

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The importance of financial markets in a globalised economy cannot be overstated. An obvious example is the 2008 collapse of Lehman Brothers, the consequences of which were not just confined to the United States but spread to almost all developed economies in the world. On a daily basis movement in the world's stock, bond, commodity and currency markets can be affected by as diverse factors as a revision to the inflation rate in China, an unexpected European Union meeting on the Euro or the announcement of company earnings in the U.S. The link between financial markets and the real economy, the increased volatility in financial markets, and the repercussions of financial crises are issues of great interest to economic agents (policymakers, firms, households) around the world. However, they are of even greater significance to developing nations, as they try to raise their living standards. The research presented in this thesis aims to inform the discussion on the pertinence of financial development for economic growth. Following a brief introduction, Chapter 2 sets the scene by reviewing the neo- classical growth models and endogenous growth theory. The rationale for focusing v - on the role of financial development is discussed next followed by all evaluation of the empirical evidence. Chapter 3 concentrates on the measurement of financial de- velopment. Existing measures are examined and a new measure is introduced using the latest available data for the largest possible number of economies. The principal components methodology, which reduces the dimensionality of the data, is used for the construction of this new measure. This is then used to revisit the empirical relationship between financial development and growth in Chapter 4. The method- ology employed is that of least squares dummy variables (LSDV) estimation, and the issue of potential endogeneity is explored through the use of two-stage ordinary least squares (OLS) and generalised method of moments (GMM). Chapter 5 undertakes a large sample analysis to address the relationship between financial development, and the likelihood of financial crises and chapter 6 summaries the findings from this work and discusses limitations and possible extensions. VI.

The growing interest in volatility trading, by many types of financial institutions, has led to a recent surge of interest amongst academics. Variance swaps are the most popular pure volatility trade derivatives, so understanding them is of imperative importance. So therefore, we provide an extensive empirical work in variance swaps spotting trading patterns via using all of the most widely known volatility indices. Additionally, we perform a comprehensive distribution fitting exercise to the variance swap returns, which provides in sights to their accurate modelling. We also show that the 'model-free' volatility index formula, which the exchanges base their volatility indices upon, is actually model-dependent. The main theoretical contribution of this thesis lies in the derivation of a completely model-free risk-neutral moment generating function which is not constrained by the usual underlying and volatility dynamics assumptions. Our model-free risk-neutral moment generating function can generate higher moment indices for assets which are traded or not. Extending the literature beyond the second moment, we derive formulae for model- free volatility, skewness and kurtosis based on our moment generating function. The estimation of these formulae is free from the problems and constraints that the calibration techniques entail. We also test the numerical procedures that are used by the exchanges, finding that they can be considerably improved. A time period of more than 16 years of FTSE 100 options provides a challenging environment in which to implement our theoretical results. Important observations of these data help the adjustment of the model-free formulae to minimise any data related errors, particularly those relating to 'cabinet' options. The absence of a volatility index in the British market inspired the construction of the VFTSE, the first volatility index on the FTSE 100 index. We show that the standard methodology consistently overestimates volatility and that the standard distributions used for model-free volatility modelling are incorrect. Viable alternatives are proposed. Following this, we present for the first time in the literature, term structure of skewness and excess kurtosis time series indices, the SFTSE and KFTSE indices. Using those moment indices, we build and analyse FTSE 100 forward densities using four different methodologies. Our results have important implications for VaR analysis, volatility trading and risk management. Finally, we discuss an options trading application of our moment indices, using FTSE 100 forward densities to spot mispriced FTSE 100 options.

This thesis investigates variance and jump risks in financial markets. Chapter 1 introduces these two concepts. Following this overview, Chapter 2 presents a thorough analysis of the compensation required by investors for their exposure to commodity variance risk. We analyze the payoffs of variance swaps of 21 prominent commodity markets over more than 20 years and find significant variance risk premia in 18 out of 21 markets. We show that commodity variance risk premia are negative, time-varying and their magnitudes increase with variance. Although classical and state-of-the-art factor models cannot explain variations in commodity variance risk premia satisfactorily, we document a significant relation between variance risk premia and macroeconomic beliefs. Chapter 3 builds on the findings of Chapter 2 to study the implication of the volatility risk premium for volatility forecasting. The volatility risk premium drives a wedge between the volatility implied from the risk-neutral probability measure and subsequently realized under the physical probability measure, making implied volatility a biased forecast of realized volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major energy markets.

This thesis collects three empirical essays on firm level investment. The first two empirical chapters aim at making a contribution in understanding a particular feature of foreign direct investment: the concentration of investment in a given country at a specific point in time. We raise the hypothesis that this feature might be explained by herd behaviour, that is, faced with incomplete information about the business conditions of alternative foreign locations, firms copy recent location decisions made by previous investors. In chapter two I devise a strategy to identify herd effects and assess its contribution in explaining the observed pattern of investment location. Using a sample of Swedish multinational enterprises, and controlling for other sources of information about the attractiveness of alternative locations, such as the firm's own experience of different host countries business conditions through past investments, I find that the pattern of location choices is consistent with the herding hypothesis. In the third chapter, I test the herding hypothesis further by replacing location specific dummies with variables that relate to readily and publicly available information on host countries' characteristics and hence likely to enter in the firm's information signal. The econometric analysis shows that the coefficient of the variable accounting for herd effects is robust to the inclusion of other variables affecting location choice. The fourth chapter studies the impact of several features of the regulatory environment on firm-level investment. The analysis focus on four industries which have traditionally been heavily regulated but have undergone a process of regulatory reform. I construct a large panel of firms in OECD countries operating in the electricity, gas, telecommunications and railways sectors. The main contribution of the paper to the existing literature is the finding that the impact of regulation on investment is both sector and firm specific.

The current thesis at tempts to highlight and offer some insight on the issues of regime shifts, contagion and predictability in financial markets. The first chapter explores an important variable in finance and economic analysis, the state of the equity market. I apply a univariate Markov Switching model and try to characterise the non-linear dynamics of the stock markets in G 7 countries. Using this particular class of model, I am able to capture the behaviour of the time series in different regimes and consequently to detect bull and bear regimes. The empirical findings demonstrate statistically significant evidence of more than one regime in each of these stock markets. The in-sample analysis suggests that a simple two-state model with regimes characterised as a high volatility bear regime with negative mean returns and a bull regime with positive mean returns and low volatility is able to capture the time-varying volatility in stock returns . The second chapter examines the correlation between stock and bond returns, considering a range of univariate, bivariate and trivariate Markov Switching models for the U.S. and the U.K. using data for the sample period 1986-2010. The objective of this chapter is two-fold: first to observe how the correlation between returns on stocks and bonds changes as the economy moves from a bull to bear regime, and second to explore the effect of monetary policy, as expressed by interest rates, on the correlation of stock and bond returns. A specification test reports evidence that the joint distribution of stock and bond returns may only be described by a two-regime MS-VAR(l) model. I have found evidence of flight-to-quality phenomena, as during bear regimes, the prices of the two asset classes tend to co-move less compared to a bull regime. This result appears robust to the inclusion of an economic predictor variable, the three-month T-bill rate, in which case not only the mean, the variance and the correlation between stock and bond returns become time-varying as driven by the hidden Markov regime, but also the ability of current interest rate to forecast subsequent stock and bond returns becomes strongly time-varying. The third chapter tests for contagion firstly, within the Euro Area (EA hereafter), and secondly from the U.S. to the E.A.. Using "co-exceedances" - the joint occurrences of extreme negative and positive returns in different countries in a given day - I define contagion within regions as the fraction of the co-exceed.ances that cannot be explained by fundamentals (covariates). On the other hand, contagion across regions can be defined as the fraction of the co-exceedance events in the E.A. that is left unexplained by its own covariates, but that is explained by the exceedances from the U.S. Having applied a. multinomial logistic regression model to daily returns on 14 European stock markets for the period 2004-2012, I can provide the following summary of the results. Firstly, I found evidence of contagion within the E.A. Especially, the E.A. ten-year government bond yield and the EUR/USD exchange rate fail to adequately explain the probability of co-exceedances in Europe. Therefore, these variables are important determinants of regional crashes. Secondly, I have observed that negative movements in stock prices follow continuation patterns - co-exceedances cluster across time. Thirdly, there is no statistically significant evidence of contagion from the U.S. to the E.A., in the sense that U.S . exceedances fail to explain high probabilities of co-exceedances in the E.A .. This result holds under a large battery of robustness checks. The fourth chapter investigates the out-of-sample predictability of stock returns, and addresses the issue of whether combinations of individual model forecasts are able to provide significant out-of- sample gains relative to the historical average. Empirical analysis for the German stock returns over the period from 1973 to 2012 implies that, firstly, term spread has the in-sample ability to predict stock returns, secondly, and most importantly, this variable successfully delivers consistent out-of-sample forecast gains relative to the historical average, and thirdly, combination forecasts do not appear to offer significant evidence of consistently beating the historical average forecasts of the stock returns. Results are robust using both statistical and economic criteria, and hold across different out-of-sample forecast evaluation periods

This thesis investigates a number of issues caused by informational asymmetries between firms and investors. It looks at two situations where problems arise because of asymmetric information, and examines possible solutions, and presents one case where asymmetric information is the solution to a problem. The first situation developed in chapter two provides a theoretical explanation based on reputational concerns for why certification intermediaries like rating agencies may exhibit excess sensitivity to the business cycle and for differences in ratings across agencies. It also analyses how competition in this industry affects the behaviour of these intermediaries and how this depends on reputational disparities among the different competitors. Chapter three looks at the impact of auditor rotation on the gathering and disclosure of information about projects taking into account that longer auditor tenures can generate substantial savings in information collection costs but also make auditors more willing to preserve future expected rents and private benefits. It also shows that the regulation of auditing procedures becomes less relevant if accounting transparency increases. In contrast with the previous two cases, the next chapter finds that co-ordination failures among small creditors during debt renegotiation can be mitigated by the presence of a large and more informed creditor or by a voting requirement. It examines how the strength of these effects depends on the relative precision of private information of the small and large creditors and provides a rationale for a diversified capital structure based on the informational role that some creditors might have in case of financial distress.

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I test the conditional international CAPM using 1990–2003 data for nine South-East Asian markets. Previous research has concluded that conditional ICAPM fails to explain expected returns in emerging markets. I argue that this is due to variations in the degree of integration among industry or size components of local equity portfolios. To test this hypothesis, I construct country, industry and market capitalisation portfolios and test the conditional ICAPM separately for each portfolio. The ICAPM is rejected more often for industries which produce mainly locally-traded outputs and for smaller market capitalisation portfolios.