|
on Financial Markets |
|
Issue of 2009‒01‒10
four papers chosen by |
| By: | Morten L. Bech (Federal Reserve Bank of New York, 33 Liberty Street, New York, NY 10045, USA.); Enghin Atalay (University of Chicago, Department of Economics, 1126 East 59th Street, Chicago, IL 60637, USA.) |
| Abstract: | We explore the network topology of the federal funds market. This market is important for distributing liquidity throughout the financial system and for the implementation of monetary policy. The recent turmoil in global financial markets underscores its importance. We find that the network is sparse, exhibits the small world phenomenon and is disassortative. Reciprocity tracks the federal funds rate and centrality measures are useful predictors of the interest rate of a loan. JEL Classification: E4, E58, E59, G1. |
| Keywords: | Network, topology, interbank, money market. |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20080986&r=fmk |
| By: | Patrick Bajari; Chenghuan Sean Chu; Minjung Park |
| Abstract: | The turmoil that started with increased defaults in the subprime mortgage market has generated instability in the financial system around the world. To better understand the root causes of this financial instability, we quantify the relative importance of various drivers behind subprime borrowers' decision to default. In our econometric model, we allow borrowers to default either because doing so increases their lifetime wealth or because of short-term budget constraints, treating the decision as the outcome of a bivariate probit model with partial observability. We estimate our model using detailed loan-level data from LoanPerformance and the Case-Shiller home price index. According to our results, one main driver of default is the nationwide decrease in home prices. The decline in home prices caused many borrowers' outstanding mortgage liability to exceed their home value, and for these borrowers default can increase their wealth. Another important driver is deteriorating loan quality: The increase of borrowers with poor credit and high payment to income ratios elevates default rates in the subprime market. We discuss policy implications of our results. Our findings point to flaws in the securitization process that led to the current wave of defaults. Also, we use our model to evaluate alternative policies aimed at reducing the rate of default. |
| JEL: | G18 G2 G33 R51 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:14625&r=fmk |
| By: | Antonella Foglia (Banca d'Italia) |
| Abstract: | This paper reviews the quantitative methods used at selected central banks to stress testing credit risk, focusing in particular on the methods used to link macroeconomic drivers of stress with bank specific measures of credit risk (macro stress test). Stress testing credit risk is an essential element of the Basel II Framework; because of their financial stability perspective, central banks and supervisors are particularly interested in quantifying the macro-to-micro linkages and have developed a specific modeling expertise in this field. In assessing current macro stress testing practices, the paper highlights the more recent developments and a number of methodological challenges that may be useful for supervisors in their review process of the banks' stress test models as required by the Basel II Framework. It also contributes to the on-going macroprudential research efforts that aim to integrate macroeconomic oversight and prudential supervision, in the direction of early identification of key vulnerabilities and assessment of macro-financial linkages. |
| Keywords: | Macro stress testing, financial stability, macro-prudential analysis, credit risk,probability of default |
| JEL: | E32 E37 G21 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:opques:qef_37_08&r=fmk |
| By: | Maria Rosa Nieto; Esther Ruiz |
| Abstract: | We review several procedures for estimating and backtesting two of the most important measures of risk, the Value at Risk (VaR) and the Expected Shortfall (ES). The alternative estimators differ in the way the specify and estimate the conditional mean and variance and the conditional distribution of returns. The results are illustrated by estimating the VaR and ES of daily S&P500 returns. |
| Keywords: | Backtesting, Extreme value, GARCH models, Leverage effect |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws087326&r=fmk |