Bank risk preferences on the government-insured mortgage market
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.
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.
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.
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 paper models household demand for childcare and mothers' labour force participation in Romania. The model estimates the effects of the price of childcare, mothers' wages, and household characteristics on household behaviour with respect to childcare and maternal employment. We find that both the maternal decision to become employed and the decision to use out-of-home care are sensitive to the price of childcare. A decrease in the price of care can increase the number of working mothers and thus can reduce poverty in some households. We also find that the potential market wage of the mother has a significant positive effect on the decision to purchase market care and on the decision to engage in paid employment. The level of household non-wage income has little effect on maternal employment and on the demand for childcare.