This thesis is composed of three chapters that form two parts. The first part is composed of two chapters and studies problems related to the exotic option market. In the first chapter we are interested in a numerical problem. More precisely we derive closed-form approximations for the price of some exotic options in the Black and Scholes framework. The second chapter discusses the construction of multivariate Lévy processes with and without stochastic volatility. The second part is composed of one chapter. It deals with a completely different issue. There we will study the problem of individual and temporal aggregation in panel data models.

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This thesis evaluates the effect of electricity co-operation regarding import and export on electricity prices for OECD countries and on CO2 emissions for the world. In addition, the study investigates which kinds of renewable energies provide the best economic future for Canada and the U.S. There are three main sections to the thesis. Firstly, panel data analysis determines the electricity price functions, using 29 OECD countries’ yearly data from 1980 to 2007. Membership of the European Union, used to investigate effect of high level co-operation on price, is seen to decrease household and industry prices, but is not significant for household price. The effect of electricity trading in OECD countries is not found to deliver cheaper electricity suggesting that these countries need to co-operate more closely to increase competition and improve efficiency in electricity markets. Secondly, panel data analysis determines parameters of the CO2 emissions function, using 131 countries’ yearly data from 1971 to 2007. The world results show that electricity co-operation is highly significant in decreasing CO2 emissions per unit of generation, thus supporting the hypothesis. At the continent level, Asia shows the highest CO2 decrease from electricity import, with the lowest decrease being for Africa. Electricity export for North America, Latin America and Europe is found to be highly significant in decreasing CO2 emissions. Finally, time series analysis of yearly data for Canada and the U.S. from 1978 to 2009 is used to determine the electricity price functions. For Canada, electricity import is found to be highly significant in decreasing household electricity price, but not so for the U.S. Renewable energies such as wind and hydro are seen to be the future of electricity generation for Canada, but the results for the U.S. indicate that no type of renewable energy can reduce electricity price.

This paper investigates the price impact cost for MSCI constituents on the Taiwan Stock Exchange (TSE) from Jan. 2001 to Dec. 2004. While the behavior of price impact cost in U.S. security markets has been extensively analyzed, there are few studies about it in the pure limit-order markets. Unlike Breen, Hodrick, and Korajczyk (2002), a panel data model is applied to fit our cross-sectional and time series data. We find that the price impact cost is well predicted by predetermined firm characteristics and exhibits a Ushaped pattern over the trading day. Furthermore, the evidence suggests that the reformations of trading regulations and the improvements of information disclosures would have a significant effect on the price impact cost for overall stocks.

The relationship between liquidity and return predictability may be an im- portant aspect to consider when investing in cryptoassets. We examine this relation using both cross-sectional as well as panel data. First, we calculate a set of predictability measures and aggregate the results into four variables. We then regress the predictability variables on a set of controls and two measures of liquidity, specifically the Amihud illiquidity ratio and the Corwin-Schultz spread estimate. The other independent variables include the logarithm of volume, turnover ratio and Garman-Klass volatility. Results from the cross- sectional analysis indicate that liquidity negatively impacts the degree of return predictability. Moreover, findings from a subset of panel data, including only 50 cryptoassets with the largest market capitalization, provide some evidence in favor of this relationship. Results from full panel data, however, present contradictory evidence. For these regressions, liquidity is found to be either in- significant or to possess a positive impact on the degree of return predictability. Altogether, we obtain mixed evidence about the effect of cryptoasset liquidity on return predictability. JEL Classification C53, C58, G14 Keywords Cryptoassets, Predictability, Liquidity, Panel data Title...

Like any other company, life insurance companies maintain a combination of debt and equity for their long-term financing, which forms their capital structure. Many theories have been developed in the literature to focus upon the determinants that are likely to affect leverage decisions of the life insurance firms in the post life Risk-Based Capital (RBC) regulation era. This report documents the application of multiple regression techniques to derive and analyze a Capital Asset Ratio (CAP) model based on the data pertaining to a large number of life insurance companies during 2000 to 2004. The data set is organized as panel data. Model coefficients, together with the error structure, are analyzed using SAS software to develop a valid model that tries to explain the Capital to Asset Ratio (CAP) for life insurers in terms of various variables of interest. The latter include return on capital, total assets, and two measures of risk: asset risk and product risk, etc. A balanced panel dataset was extracted from the given unbalanced input dataset containing missing entries. In addition, a selected few of the explanatory variables were chosen from a large group present in the input dataset based on previous work on relations among asset risk, product risk and capital in the life insurance industry by Etti G. Baranoff and Thomas W. Sager (2002). Fixed Effects model was chosen based on the assumption that the firm-specific effects were correlated to the explanatory variables. Differencing method was employed so that OLS estimator could safely be used for the coefficients in the regression model. Based on the proposed model, it is found that Capital to Asset Ratio has positive relationships with product risk and return on capital, with the corporate form of organization, and with membership in an affiliated group of companies. On the other hand, it has a negative relationship with company’s size and the ratio of life premiums or annuity premiums to the total premiums generated. / text

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This paper revisits Grunfeld's well-known investment data, one of the most widely used data sets in all of econometrics, on the occasion of their 50th anniversary. It presents, apparently for the first time after the publication of the original Chicago Ph.D. thesis, the full data set and points out errors and inconsistencies in several currently available versions. It also revisits a number of empirical studies from the literature of the last five decades. / Series: Research Report Series / Department of Statistics and Mathematics

The main purpose of this thesis is to investigate the effect of corruption on income inequality (that could serve as indicator of the welfare of whole society). Since post-communist countries of Central and Eastern Europe had issues with providing effective policies for adapting to the market economy, we want to discover main drivers of this situation. We examined previous researches that suggest both positive and negative correlation between corruption level and income inequality. Main obstacle of the research is inherent heterogeneity present across countries. Our analysis was performed on two datasets: 11 post- communist countries CEE and additional 17 European Union countries. We implemented different estimation methods and discovered that panel Vector autoregressive model is the best choice. Within the panel structure we tackled individual heterogeneity by estimating fixed effects and clustering on the country level, implemented dynamic relationship in the dependent variable and solved endogeneity problem by using instrumental variable. We found that corruption has positive relationship with income inequality. Furthermore, other important drivers are: social spending, education level and unemployment. As a result, we suggested the ways to decrease corruption on the appropriate example of...

The influence of ‘credit crunch’ on Small and Medium sized Enterprises (SMEs) has been of concern to the government, regulators, banks, the enterprises and the public. Using a large dataset of UK SMEs’ records covering the early period of the ‘credit crunch’, the influence of the ‘credit crunch’ on SMEs have been studied. It uses cross-sectional method, panel data models and GAM to provide a detailed examination of SMEs performance. Both newly established and matured SMEs, segmented by age, are considered separately. The data contains 79 variables which covered obligors’ general condition, financial information, directors’ portfolio and other relevant credit histories. The ‘credit crunch’ is a typical ‘black swan’ phenomenon. As such there is a need to examine whether the stepwise logistic model, the industries prime modelling tool, could deal with the sudden change in SMEs credit risk. Whilst it may be capable of modelling the situation alternatives models may be more appropriate. It provides a benchmark for comparison to other models and shows how well the industry’s standard model performs. Given cross-sectional models only provide aggregative level single time period analysis, panel models are used to study SMEs performance through the crisis period. To overcome the pro-cyclic feature of logistic model, macroeconomic variables were added to panel data model. This allows examination of how economic conditions influence SMEs during ‘credit crunch’. The use of panel data model leads to a discussion of fixed and random effects estimation and the use of explanatory macroeconomic variables. The panel data model provides a detailed analyse of SMEs’ behaviour during the crisis period. Under parametric models, especially logistic regression, data is usually transformed to allow for the non-linear correlation between independent variable and dependent variable. However, this brings difficulty in understanding influence of each independent variable’s marginal effects. Another way of dealing with this is to add non-parametric effects. In this study, Generalized Additive Models (GAM) allows for non-parametric effects. A natural extension of logistic regression is a GAM model with logistic link function. In order to use the data in their original state an alternative method of processing missing values is proposed, which avoids data transformation, such as the use of weights of evidence (WoE). GAM with original data could derive a direct marginal trend and plot how explanatory variables influence SMEs’ ‘bad’ rate. Significant non-parametric effects are found for both ‘start-ups’ and ‘non-start-ups’. Using GAM models results in higher prediction accuracy and improves model transparency by deriving explanatory variables’ marginal effects.