Eva Ferreira

Length of time spent in Chapter 11 bankruptcy: a censored partial regression model

This paper investigates original issuers of high yield bonds in Chapter 11 bankruptcy to determine which factors affect the length of time spent in Chapter 11. In order to do this analysis a flexible new duration model is proposed, the censored partial regression model. This model allows consideration of the effect of some variables on the duration using a nonparametric functional form. It is found that the choice of prepackaged Chapter 11, the length of time negotiating before filling for Chapter 11, the profitability, the highly leveraged transactions, the participation on different disputes, the role of vulture funds and some institutional changes turn out to be relevant to analyse this duration.

Testing for Differences Between Conditional Means in a Time Series Context

In this article we study tests for equality of two regression curves when the inputs are driven by a time series. The basic process underlying the test statistics is the empirical process of the time series marked by the difference in the pertaining dependent variables. The main results hold under strict stationarity of the input variables, but no mixing condition or special modeling of the time series will be necessary. A simulation study is reported on, which illustrates the quality of the distributional approximation and the power of the tests for small to moderate sample sizes. An application to a real dataset is also included.

A nonparametric method to estimate time varying coefficients under seasonal constraints

This paper proposes a new method to estimate nonparametrically a univariate time varying coefficients model. This estimation procedure allows to incorporate both, seasonal and smoothness constraints. The resulting estimator nests as particular cases many other estimators proposed in the literature. We derive its asymptotic bounds and we also show consistency and the asymptotic distribution. Finally, we illustrate its performance by estimating the Spanish money multiplier and we provide a data driven method to compute the smoothing parameters.

An algorithm to estimate time-varying parameter SURE models under different types of restriction

A nonparametric method to estimate time-varying coefficients in seemingly unrelated regression equations models is proposed. The procedure presents two main advantages with respect to other proposals in literature. First, it allows to incorporate both cross and time-varying restrictions into the parameters. Second, the estimator is obtained in a closed form, and there is no need of an iterative method to compute its value. However, this computation requires to solve a linear system where the number of equations and coefficients increases with the sample size. This problem is overcome by using an algorithm that reduces the computation cost. The algorithm enables to solve the linear system in a recursive manner where (in each step) a lower dimensional linear system is solved and there is no increase with sample size.

Kernel regression estimates of growth curves using nonstationary correlated errors

We study the nonparametric estimation of the average growth curve under a very general parametric form of the covariance structure that allows for monotone transformation of the time scale. We also investigate the properties of optimal bandwidth selection methods and compare the results with those obtained under stationarity.

Conditional Beta Pricing Models: A Nonparametric Approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.

Semiparametric approaches to signal extraction problems in economic time series

Abstract Nonparametric regression methods have become a very useful tool to extract trend signals in economic time series. However, this approach performs poorly when seasonality is present. To overcome this difficulty, we propose two alternative methods to deal with seasonal effects. In both approaches the trend is specified nonparametrically, but the seasonal component specification is different. First, we propose a partial linear model where the parametric part is a dummy-variable specification for the seasonality. Secondly, we consider the seasonal component to be a smooth function of time and, therefore, the model falls within the class of additive models. We offer efficient algorithms for calculating values of the parameter estimators for each of these approaches and we derive asymptotic properties for the estimators in the partial linear model. Finally, we illustrate these methods when applied to the Spanish industrial production index for energy.

Modelling the duration of firms in Chapter 11 bankruptcy using a flexible model

Abstract This paper analyzes the duration of firms in Chapter 11 bankruptcy using a flexible model without assuming any probability distribution. We use bootstrap techniques to make inference on the estimators, and propose a new bootstrap procedure for censored samples.

Optimal dynamic resource allocation to prevent defaults

We consider a resource allocation problem to decide how to share resources among different companies facing financial difficulties. The objective is to minimize the long term cost due to default events. Using the framework of Multi-Armed Restless Bandits, the optimal policy assigns an index value to each company, which orders its priority to be funded. The index generalizes the return-on-investment (ROI) index under the static setting, and we analyse the influence of the future events on the optimal dynamic policy.

Dynamic binomials with an application to gender bias analysis

We analyze the dynamics of a stochastic process driven by binomial random variables, where the probability of success depends on the past realization. We study the limit behavior when the group size is fixed but the number of iterations increases. It will become apparent that the so-called policy function and its fixed point play an outstanding role. Some applications to a statistical analysis of gender bias are also briefly discussed.