Do sequel movies really earn more than non-sequels? Evidence from the US box office
The paper presents the structural model of decision-making process on the residential mortgage market. We empirically estimates key drivers of mortgage borrowing, underwriting, and default process by jointly using market-level monthly data and loan-level data from regional branch of Agency of Home Mortgage Lending (AHML). The multistep estimation procedure allows correcting for sample selection bias and endogeneity and provides consistent parameter estimates. Obtained results shows that risk preferences are changing during the time and AHML borrowers are relatively high risky.
Most of existing scoring systems are based on binary choice models with sample selection. This setting does not allow for up-to-date information about loans to be used and a lot of observations becomes lost. In the paper a model of binary choice with sample selection is extended to the case of many periods. This extension allows for defaults to be modeled for each period that solves the problem of lost observations. This setting also can be used to estimate the effectiveness of existing scoring system of a bank. The model is estimated using data granted by one of commercial banks of Nizhny Novgorod. Sample consists of observations from January 2009 to March 2012.
This conference proceeding includes selected full papers from the 11th EBES Conference – Ekaterinburg. We have accepted papers among resubmitted full papers after the conference ended. In this proceeding you will find a snapshot of topics that are presented in the conference. As expected, our conference has been an intellectual hub for academic discussion for our colleagues in the areas of economics, finance, and business. Participants found an excellent opportunity for presenting new research, exchanging information and discussing current issues. We believe that this conference proceeding and our future conferences will improve further the development of knowledge in our fields.
This article addresses the issue of unobserved heterogeneity in film characteristics influence on box-office. We argue that the analysis of pooled samples, most common among researchers, does not shed light on underlying segmentations and leads to significantly different estimates obtained by researchers running similar regressions for movie success modeling. For instance, it may be expected that a restrictive MPAA rating is a box office poison for a family comedy, whereas it insignificantly influences an action movie’s revenues. Using a finite mixture model we extract two latent groups, the differences between that can be explained in part by the movie genre, the source, the creative type and the production method. On the basis of this result, the authors recommend developing separate movie success models for different segments, rather than adopting an approach, that was commonly used in previous research, when one explanatory or predictive model is developed for the whole sample of movies.
The mortgage crisis that started in the U.S. in 2007 and lasted until 2009 was characterized by an unusually large number of defaults on the subprime mortgage market. As a result, it developed into a global economic recession and placed the stability of the world banking system in jeopardy. Therefore, the issues of credit risk modeling showed the shortcomings of the current credit risk practice. Truncation, or partial observability, and simultaneous equations bias causes sample selection bias. As a result, parameter estimates are biased and inconsistent. Firstly, we provide an overview of current approaches in the mortgage literature to control for the sample selection bias correction, such as the Heckman model and bivariate probit model with selection. Secondly, a review of the most significant mortgage studies discussing this problem is introduced. Specifically, different structural models, specific datasets and empirical results are regarded. In addition, we discuss such key credit risk determinants as borrower characteristics, terms of the mortgage contract, mortgage characteristics, and macroeconomic conditions. Finally, we conclude the discussion with possible research questions.
This paper analyzes the problems of credit risk modeling on the Russian residential mortgage market. The structural model of the credit risk evaluation, which controls for the sample selection bias and endogeneity, is presented. It estimates based on the regional mortgage data. Obtained results can be used to develop the effective risk management systems in credit organizations.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.