Panel Data Models with Unobserved Multiple Time- Varying Effects to Estimate Risk Premium of Corporate Bonds

We use a panel cointegration model with multiple time- varying individual effects to control for the missing factors in the credit spread puzzle. Our model specification enables as to capture the unobserved dynamics of the systematic risk premia in the bond market. In order to estimate the dimensionality of the hidden risk factors jointly with the model parameters, we rely on a modified version of the iterated least squares method proposed by Bai, Kao, and Ng (2009). Our result confirms the presence of four common risk components affecting the U.S. corporate bonds during the period between September 2006 and March 2008. However, one single risk factor is sufficient to describe the data for all time periods prior to mid July 2007 when the subprime crisis was detected in the financial market. The dimensionality of the unobserved risk components therefore seems to reflect the degree of difficulty to diversify the individual bond risks.Panel Data Model; Factor Analysis; Credit Spread; Systematic Risk Premium;

Aggregate Behavior and Microdata

It is shown how one can effectively use microdata in modelling the change over time in an aggregate (e.g. mean consumption expenditure) of a large and heterogeneous population. The starting point of our aggregation analysis is a specification of explanatory variables on the micro-level. Typically, some of these explanatory variables are observable and others are unobservable. Based on certain hypotheses on the evolution over time of the joint distributions across the population of these explanatory variables we derive a decomposition of the change in the aggregate which allows a partial analysis: to isolate and to quantify the effect of a change in the observable explanatory variables. This analysis does not require an explicit treatment of the unobservable variables.

Common functional component modelling

Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data.nonparametric risk management, generalized hyperbolic distribution, functional data analysis

Panel Data Models with Unobserved Multiple Time - Varying Effects to Estimate Risk Premium of Corporate Bonds

We use a panel cointegration model with multiple time- varying individual effects to control for the enigmatic missing factors in the credit spread puzzle. Our model specification enables as to capture the unobserved dynamics of the systematic risk premia in the bond market. In order to estimate the dimensionality of the hidden risk factors jointly with the model parameters, we rely on a modified version of the iterated least squares method proposed by Bai, Kao, and Ng (2009). Our result confirms the presence of four common risk components affecting the U.S. corporate bonds during the period between September 2006 and March 2008. However, one single risk factor is sufficient to describe the data for all time periods prior to mid July 2007 when the subprime crisis was detected in the financial market. The dimensionality of the unobserved risk components therefore seems to reflect the degree of difficulty to diversify the individual bond risks.Corporate Bond; Credit Spread; Systematic Risk Premium; Panel; Data Model with Interactive Fixed Effects; Factor Analysis; Dimensionality Criteria; Panel Cointegration

Common Functional Principal Components

Functional principal component analysis (FPCA) based on the Karhunen-Lo`eve decomposition has been successfully applied in many applications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only by the theoretical challenge of this data situation but also by the actual question of dynamics of implied volatility (IV) functions. For different maturities the logreturns of IVs are samples of (smooth) random functions and the methods proposed here study the similarities of their stochastic behavior. Firstly we present a new method for estimation of functional principal components from discrete noisy data. Next we present the two sample inference for FPCA and develop two sample theory. We propose bootstrap tests for testing the equality of eigenvalues, eigenfunctions, and mean functions of two functional samples, illustrate the test-properties by simulation study and apply the method to the IV analysis.Functional Principal Components, Nonparametric Regression, Bootstrap, Two Sample Problem

Asymptotics and Consistent Bootstraps for DEA Estimators in Non-parametric Frontier Models

Non-parametric data envelopment analysis (DEA) estimators based on linear programming methods have been widely applied in analyses of productive efficiency. The distributions of these estimators remain unknown except in the simple case of one input and one output, and previous bootstrap methods proposed for inference have not been proven consistent, making inference doubtful. This paper derives the asymptotic distribution of DEA estimators under variable returns-to-scale. This result is then used to prove that two different bootstrap procedures (one based on sub-sampling, the other based on smoothing) provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as well as smoothing of the DEA frontier estimate. Both bootstrap procedures allow for dependence of the inefficiency process on output levels and the mix of inputs in the case of input-oriented measures, or on inputs levels and the mix of outputs in the case of output-oriented measures.bootstrap, frontier, efficiency, data envelopment analysis, DEA

Common Functional Component Modelling

Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data

Aggregation under structural stability: the change in consumption of a heterogeneous population

It is shown how one can effectively use cross-section data in modelling the change over time in aggregate consumption expenditure of a heterogeneous population. The starting point of our aggregation analysis is a dynamic behavioral relation on the household level. Based on certain hypotheses on the evolution of the distribution of income and household characteristics we derive explanatory variables for the change in aggregate consumption expenditure which are quite different from the explanatory variables on the household level. It is shown that U.K. Family Expenditure Data support our theoretical model for aggregate consumption.