This paper studies dynamical and cross-sectional structures of bonds, typically used as risk-free assets in mathematical finance. After reviewing a mathematical theory on common factors, also known as principal components, we compute empirical common factors for 10 US government bonds (3month, 6month, 1year, 2year, 3year, 5year, 7year, 10year, 20year, and 30year) from the daily data for the period 1993-2006 (data for earlier period is not complete) obtained from the official web site www treas.gov. We find that the principal common factor contains 91% of total variance and the first two common-factors contain 99.4% of total variance. Regarding the first three common factors as stochastic processes, we find that the simple AR(1) models produce sample paths that look almost indistinguishable (in characteristic) from the empirical ones, although the AR(1) models do not seem to pass the normality based Portmanteau statistical test. Slightly more complicated ARMA(1,1) models pass the test. To see the independence of the first two common factors, we calculate the empirical copula (the joint distribution of transformed random variables by their marginal distribution functions) of the first two common-factors. Among many commonly used copulas (Gaussian, Frank, Clayton, FGM, Gumbel), the copula that corresponds to independent random variables is found to fit the best to our empirical copula. Loading coefficients (that of the linear combinations of common factors for various individual bonds) are briefly discussed. We conclude from our empirical analysis that yield-to-maturity curves of US government bonds from 1993 to 2006 can be simply modelled by two independent common factors which, in turn, can be modelled by ARMA(1,1) processes.

This thesis consists of three essays on the liquidity characteristics and traders behavior in the main market for agricultural commodity futures in India, the National Commodity and Derivatives Exchange. This electronic trading platform was launched at the end of 2003 and subsequently became the third largest agricultural futures market globally. The first essay estimates the impact of speculators capital constraints on their willingness to provide liquidity as measured by trade participation, and on overall market liquidity as measured by bid-ask spread. To overcome the standard identification problem, the study exploits exogenous variation in trading performance in the form of losses in one asset unrelated to the fundamentals of another asset. The study finds that a small number of traders accounts for an overwhelming share of trading activity and participate in the market for a large number of commodities. Consistent with theoretical predictions, a negative shock to these active traders aggregate capital causes an increase in future bid-ask spread, but the economic magnitude of the estimated effect is small. Changes in competition to provide liquidity explain a considerable fraction of the variation in subsequent market liquidity. The effect is non-linear: the bid-ask spread is smallest around a natural level of competition, but increases as competition intensity deviates away from this point. Using the same dataset, the second essay investigates sources of traders superior returns in local commodities. Investors bias their portfolios towards local commodities, crops that are differentially grown within 100km of their location, and earn returns in these commodities that are 3.2% higher than in their non-local commodities, even amongst traders who turnover positions frequently. This differential is greatest in crops that are weather sensitive and for which India has a high percentage of world production. The results are consistent with traders possessing superior domestic supply information on local commodities because their proximity to crop production causes information acquisition costs to be lower. The third essay analyzes the trading decisions and performance of all three trader categories – individuals, brokers, and commercial institutions – participating in agricultural commodity markets in India. In contrast to U.S. commodity markets, individuals represent about 80% of participants by number, and contribute between 40-50% of trading activity and open interest in the market. Client commercial institutions account for less than 5% of overall trading activity, but for up to 35% of open interest； although fewest by number, broker proprietary trading desks account for a large portion of trading activity. Brokers are the most active group in spread strategies, while both brokers and individuals engage frequently in day-trading activities. Broker proprietary accounts are highly diversified across commodities trading 14 commodities on average, compared to about 4 traded by the other types. In aggregate, brokers make the largest amount of profits, and they do so consistently over time. The mean broker accounts profits from both intra-day and overnight profits is almost 40 to 60 times larger than the corresponding profits obtained by the mean client institution or individual. In contrast, individuals lose significant amounts of money. Trading activity, open interest and profitability are concentrated within each market participant group. This study also analyzes the impact of market-wide characteristics, and beyond that, the impact of peer actions and outcomes on individuals decisions to enter into commodities futures market. Aggregate entry rates of both individuals and companies in the commodity futures market are positively serially correlated, and increasing with trading volume and commodity market returns. The actions and market outcomes of local peers affect entry decisions. The number of new individual traders in a zip-code is highly positively serially correlated, and zip-codes with more active participants experience higher entry rates in the future. Moreover, the recent returns of individual traders in a zip-code are positively correlated with the future number of individual entries in that zip-code； the influence of peer returns is restricted to situations when neighbors experience negative returns. Our findings suggest that information about negative peer performance is more likely to spread among individuals than information about positive peer performance, or that the individuals in our sample react only to learning about negative peer returns.

Gaussian normal error assumption and OLS-based regression estimations form the basis of co-integration testing techniques. However, many studies have found evidence that financial and some important economic time series data such as exchange rate returns and inflation rates are subject to high variability. In particular, their innovations exhibit the features of skewness, excessive peakness around the mean and heavier tails than those of the Gaussian normal distribution. Stable distributions which are used to model high variability in data and infinite-variance processes) provide more realistic distributional assumptions than the Gaussian distribution for heavy-tailed financial and economic time series. Least Absolute Deviation LAD) based estimators often yield robust results for heavy-tailed data compared to least squares based estimators. In this dissertation, as a natural extension of the extant studies, we consider a new robust residual-based co-integration test under the assumption of infinite-variance errors which are in the domain of attraction of a stable law. We implement the least absolute deviation LAD) procedure in our regression estimations. In part I, the new co-integration tests are proposed. The test is parametric: the critical values of the test statistic depend on the stability index of the stable distribution from which the errors are driven. The unit root test statistic we consider under the null of no co-integration is taken from Samarakoon and Knight 2009, Econometric Reviews, 28, 314–334). We find the critical values of these new co-integration tests through Monte Carlo simulations and observe that the null convergence of the test statistic is faster for lighter tails. Size and power comparisons are included to evaluate the performance of the new residual-based tests relative to conventional OLS-based ones which are due to Caner 1998, J. of Econometrics, 86, 155–175). We observe that the LAD-based tests have power advantages over the OLS-based tests as the sample size gets larger and the tails get heavier with infinite-variance error assumption, yet there are more size distortions associated with LAD-based tests especially for small sample sizes compared to OLS-based ones. The new tests are employed to test for forward rate unbiasedness hypothesis FRUH) with daily frequency data for a sample of eight currencies Australian dollar, Canadian dollar, French franc, German mark, Italian lira, Japanese yen, Swiss franc and U.K. pound) against the U.S. dollar for 1-month, 3-month, 6-month and 1-year forward contracts. We also run fully-modified ordinary least squares FM-OLS) and fully-modified least absolute deviation FM-LAD) estimators on the co-integrating regressions to test the coefficient restrictions which are implied by the FRUH. We observe that tests involving longer maturity forward contracts 6-month and 1-year) and LAD-based co-integration tests mostly provide the evidence that is inconsistent with FRUH. In part II, weak and strong-form purchasing power parity PPP) relations are re-examined by using LAD-based procedures under infinite-variance error assumption. LAD-based co-integration tests that are proposed in part I are used to test for weak-form PPP. FM-OLS and FM-LAD procedures are used to test for the strong-form PPP hypothesis. The results from LAD-based estimations are compared to their OLS-based counterparts. Monthly exchange rate per U.S. dollar) and PPI data for a sample of eight countries Austria, Canada, Denmark, Germany, Japan, Netherlands, Sweden and the U.K.) from 1973:1 to 2009:12 are used for estimation purposes. Neither weak-form nor strong-form PPP relations can be justified empirically regardless of the estimation procedure. Results from the new residual-based co-integration tests give slightly more support of the weak-form PPP.

This dissertation uses flexible finite mixture distributions to simultaneously make inferences about the group membership of heterogenous firms and households and their behavior. It first exhibits the relationship existing among several alternative mixture models and shows how their comparison can be used to learn about specific features of a given panel data set. These models are then used to learn about a firms financial states and about the interdependence between these states and a firms investment decision using a panel data set on US manufacturing firms obtained from the COMPUSTAT database. Their comparison reveals that time dependence is a more important issue than the interdependence between the firms financial status and its investment decision when it comes to the fit of the models to the data. Similar models are also used to reexamine the effect of the 1997 healthcare reform enacted in Germany using a panel data set on the number of doctor visits derived from the German Socio-Economic Panel by Winkelmann 2004) for the period going from 1995 to 1999. Again, we find evidence in favor of time dependence. However, controlling for the presence of random effects in the mixture components leads to models which fit much better to the data.

This dissertation uses flexible finite mixture distributions to simultaneously make inferences about the group membership of heterogenous firms and households and their behavior. It first exhibits the relationship existing among several alternative mixture models and shows how their comparison can be used to learn about specific features of a given panel data set. These models are then used to learn about a firms financial states and about the interdependence between these states and a firms investment decision using a panel data set on US manufacturing firms obtained from the COMPUSTAT database. Their comparison reveals that time dependence is a more important issue than the interdependence between the firms financial status and its investment decision when it comes to the fit of the models to the data. Similar models are also used to reexamine the effect of the 1997 healthcare reform enacted in Germany using a panel data set on the number of doctor visits derived from the German Socio-Economic Panel by Winkelmann 2004) for the period going from 1995 to 1999. Again, we find evidence in favor of time dependence. However, controlling for the presence of random effects in the mixture components leads to models which fit much better to the data.