|
on Risk Management |
|
Issue of 2011‒09‒22
seven papers chosen by |
| By: | Lev Ratnovski; Enrico Perotti; Razvan Vlahu |
| Abstract: | The paper studies risk mitigation associated with capital regulation, in a context where banks may choose tail risk asserts. We show that this undermines the traditional result that high capital reduces excess risk-taking driven by limited liability. Moreover, higher capital may have an unintended effect of enabling banks to take more tail risk without the fear of breaching the minimal capital ratio in non-tail risky project realizations. The results are consistent with stylized facts about pre-crisis bank behavior, and suggest implications for the optimal design of capital regulation. |
| Keywords: | Bank regulations , Banks , Capital , Economic models , Risk management , |
| Date: | 2011–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:11/188&r=rmg |
| By: | Burcu Aydin; Myeong-Suk Kim; Ho-Seong Moon |
| Abstract: | This paper assesses the interconnectedness across Korean banks using three alternative methodologies. Two methodologies utilize high frequency financial data while the third uses bank balance sheet data to assess banks’ bilateral exposures, systemically vulnerable banks, and systemically risky banks. The analysis concludes that while Korean banks are interconnected, both the financial risk and contagion risk from such interconnectedness have declined significantly in the aftermath of the global financial crisis. |
| Keywords: | Banking systems , Banks , Credit risk , Economic models , Financial risk , Korea, Republic of , |
| Date: | 2011–08–18 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:11/201&r=rmg |
| By: | Raffaella Calabrese (Geary Institute, University College Dublin); Silvia Angela Osmetti (Department of Statistics, University Cattolica del Dacro Cuore, Milan) |
| Abstract: | The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure is the maximum likelihood method. This model accommodates skewness and it presents a generalization of GLMs with log-log link function. In credit risk analysis a pivotal topic is the default probability estimation. Since defaults are rare events, we apply the GEV regression to empirical data on Italian Small and Medium Enterprises (SMEs) to model their default probabilities. |
| Date: | 2011–09–15 |
| URL: | http://d.repec.org/n?u=RePEc:ucd:wpaper:201120&r=rmg |
| By: | Jakub W. Jurek (Bendheim Center for Finance, Princeton University); Erik Stafford (Harvard Business School, Finance Unit) |
| Abstract: | This paper studies the cost of capital for alternative investments. We document that the risk profile of the aggregate hedge fund universe can be accurately matched by a simple index put option writing strategy that offers monthly liquidity and complete transparency over its state-contingent payoffs. The contractual nature of the put options in the benchmark portfolio allows us to evaluate appropriate required rates of return as a function of investor risk preferences and the underlying distribution of market returns. This simple framework produces a number of distinct predictions about the cost of capital for alternatives relative to traditional mean-variance analysis. |
| Date: | 2011–08 |
| URL: | http://d.repec.org/n?u=RePEc:hbs:wpaper:12-013&r=rmg |
| By: | Frédéric Abergel (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris); Nicolas Millot (MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris) |
| Abstract: | Local risk minimization is studied for the hedging of derivatives - a general (non quadratic) risk criterion is studied, and the optimality conditions are derived. |
| Date: | 2011–05–24 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-00620843&r=rmg |
| By: | Tor Jacobson; Jesper Lindé; Kasper Roszbach |
| Abstract: | This paper studies the relationship between macroeconomic fluctuations and corporate defaults while conditioning on industry affiliation and an extensive set of firm-specific factors. By using a panel data set for virtually all incorporated Swedish businesses over 1990-2009, a period which includes a full-scale banking crisis, we find strong evidence for a substantial and stable impact from aggregate fluctuations on business defaults. A standard logit model with financial ratios augmented with macroeconomic factors can account surprisingly well for the outburst in business defaults during the banking crisis, as well as the subsequent fluctuations in default frequencies. Moreover, the effects of macroeconomic variables differ across industries in an economically intuitive way. Out-of-sample evaluations show that our approach is superior to models that exclude macro information and standard well-fitting time-series models. Our analysis shows that firm-specific factors are useful in ranking firms' relative riskiness, but that macroeconomic factors are necessary to understand fluctuations in the absolute risk level. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1029&r=rmg |
| By: | Jo\~ao P. da Cruz; Pedro G. Lind |
| Abstract: | This article makes use of the apparent indifference that the market has been devoting to the developments made on the fundamentals of quantitative finance, to introduce novel insight for better understanding market evolution.We show how these drops and crises emerge as a natural result of local economical principles ruling trades between economical agents and present evidence that heavy-tails of the return distributions are bounded by constraints associated with the topology of agent relations. Finally, we discuss how these constraints may be helpful for properly evaluate model risk. |
| Date: | 2011–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1109.2803&r=rmg |