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on Risk Management |
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Issue of 2015‒08‒07
ten papers chosen by |
| By: | Peter Mitic (Santander UK); Bertrand K. Hassani (Grupo Santander et Centre d'Economie de la Sorbonne) |
| Abstract: | A method of allocating Operational Risk regulatory capital using the Shapley method for a large number of business units, supported by a service, is proposed. A closed-form formula for Shapley allocations is developed under two principal assumptions. First, if business units form coalitions, the value added to the coalition by a new entrant depends on a constant proportionality factor. This factor represents the diversification that can be achieved by combining operational risk losses. Second, that the service should reduce the capital payable by business units, and that this reduction is calculated as an integral part of the allocation process. We ensure that allocations of capital charges are acceptable to and are understandable by both risk and senior managers. The results derived are applied to recent loss data |
| Keywords: | Allocation; Shapley; operational risk; diversification; service; Game theory; capital value |
| JEL: | C71 C63 C46 |
| Date: | 2015–06 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:15056&r=rmg |
| By: | Preston, Richard; Walters, Cory G. |
| Keywords: | Risk and Uncertainty, |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea15:206435&r=rmg |
| By: | Beteto Wegner, Danilo Lopomo |
| Abstract: | A tractable model of the formation of financial networks is developed, allowing the use of concepts from portfolio theory. The optimal financial network maximizes a Sharpe ratio defined for financial networks, whereas the equilibrium financial net- work emerges from banks bargaining over future proceeds of co-investment oppor- tunities. Measures of financial fragility, systemic risk and robustness are developed. The equilibrium financial network is shown to be the most connected and with the lowest level of financial fragility, whereas the optimal is the one least connected and with lowest exposure to systemic risk, being also the most robust financial network. |
| Keywords: | Government interventions, financial fragility, financial networks, sytemic risk, robustness, Financial Economics, G1, G2, G3, |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:ags:uqsers:179222&r=rmg |
| By: | Xisong Jin; Francisco Nadal De Simone |
| Abstract: | This study measures investment funds? systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories National Central Banks report to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the build-up of investment funds? vulnerabilities which may lead to a disorderly unraveling. The paper uses a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM). The framework models investment funds? distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. In addition, the estimates of the common components of the investment funds? distress measures may contain some early warning features, and identifying the macro and financial variables most closely associated with them may serve to guide macro-prudential policy. The relative importance of these variables differs from those associated with the common components of marginal measures of distress. Thus this framework can contribute to the formulation of macro-prudential policy. |
| Keywords: | financial stability; investment funds; procyclicality, macro-prudential policy; structural credit risk models; probability of distress; non-linearities; generalized dynamic factor model; dynamic copulas |
| JEL: | C1 E5 F3 G1 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:bcl:bclwop:bclwp095&r=rmg |
| By: | Anna Costello (Massachusetts Institute of Technology); Nina Boyarchenko (Federal Reserve Bank of New York) |
| Abstract: | This paper explores the sources of counterparty risk in material supply relationships. Using long-term supply contracts collected from SEC filings, we test whether three sources of counterparty risk -- financial exposure, product quality risk, and redeployability risk -- are priced in the equity returns of linked firms. Our results show that equity holders require compensation for exposure to all three sources of risk. Specifically, offering trade credit to counterparties and investing in relationshipspecific assets increase a firm’s exposure to counterparty risk. Further, we show that contracts with protective financial covenants and product warranties mitigate the transmission of risk. Overall, we provide evidence on the channels of supply-chain risk and show that shareholders recognize the role of contractual features in mitigating counterparty risk. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed015:235&r=rmg |
| By: | Norton, Michael; Boucher, Stephen; Verteramo Chiu, Leslie |
| Abstract: | This paper describes the application of geostatistics to weather index insurance in order to systematically analyze spatial basis risk inherent in index insurance contracts. The notion of spatial autocorrelation is in general overlooked by index insurance practitioners, but has profound implications for the effectiveness of the insurance offered. The analysis shows that it is possible to oer contracts from multiple weather stations to a single farmer, and that doing so will likely reduce the basis risk from a single contract. The two major implications of the paper are 1) that index insurance should be offered in more flexible contracts that allow farmers to hedge their production according to their perceptions of basis risk and their appetites for risk, and 2) the tradeoff between local (yield) correlation and spatial correlation needs to be more carefully considered, as it may even be better to oer contracts with poor yield correlation if they can include more spatial coverage. |
| Keywords: | Risk, Insurance, Geostatistics, Spatial Statistics, Weather, Hedging, Research Methods/ Statistical Methods, Risk and Uncertainty, |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea15:205755&r=rmg |
| By: | Andrei N. Soklakov |
| Abstract: | "What are the origins of risks?" and "How material are they?" -- these are the two most fundamental questions of any risk analysis. Quantitative Structuring -- a technology for building financial products -- provides economically meaningful answers for both of these questions. It does so by considering risk as an investment opportunity. The structure of the investment reveals the precise sources of risk and its expected performance measures materiality. We demonstrate these capabilities of Quantitative Structuring using a concrete practical example -- model risk in options on vol-targeted indices. |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1507.07216&r=rmg |
| By: | Lee, Yu Na; Bellemare, Marc F.; Just, David R. |
| Abstract: | In his seminal 1971 article, Sandmo showed that when faced with an uncertain output price, a risk-averse firm manager would hedge by producing less than he would have when faced with a certain output price. We take Sandmo’s prediction, among other things, to the lab. We study in turn the effects of price risk (i.e., uncertain prices whose distribution is known) and price ambiguity (i.e., uncertain prices whose distribution is not known, but whose range is known) while controlling for our subjects’ income risk preferences. Our experimental protocol closely mimics Sandmo’s theoretical model. For price risk, we use a two-stage randomization strategy aimed first at studying the effect of price uncertainty relative to price certainty, and then the effect of increases in price uncertainty conditional on there being price uncertainty. For price ambiguity, we use the same randomization strategy to study the effect of price ambiguity relative to price certainty while preventing our subjects from guessing the shape of the price distribution. For price risk, we find that, in stark contradiction to Sandmo’s theoretical result, the presence of price uncertainty causes subjects to produce more than they do under price certainty, but that increases in price uncertainty makes them decrease their production monotonically. For price ambiguity, results are mixed and depend on whether the portion of the experiment aimed at eliciting our subjects’ income risk aversion is played before or after the price uncertainty game. Lastly, we use our price risk data to study the problem structurally, in order to get at preference heterogeneity, and find that our structural results are consistent with our reduced-form results. |
| Keywords: | Risk and Uncertainty, Price Risk, Price Ambiguity, Experimental Economics, Institutional and Behavioral Economics, Risk and Uncertainty, |
| Date: | 2015–05–27 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea15:205763&r=rmg |
| By: | Boutwell, J. Luke; Westra, John |
| Abstract: | Coastal communities are highly sensitive to economic damage from tropical storms. Wetland restoration is often proposed as a measure of protection from storm damage. This paper investigates the relationship between coastal storms, wetlands and communities by analyzing storm events and resulting damages from storms making landfall in Louisiana. A factor analysis is used to describe the extent to which wetlands mitigate economic damages, and an assessment of factor scores suggest that there is a storm intensity threshold for mitigation provided by wetland ecosystems. |
| Keywords: | coastal, damage, factor analysis, hurricane, risk, storm surge, wetlands, Environmental Economics and Policy, Resource /Energy Economics and Policy, Risk and Uncertainty, Q24, Q54, Q56, Q57, |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ags:saea14:162509&r=rmg |
| By: | Pavel V. Shevchenko; Jonas Hirz; Uwe Schmock |
| Abstract: | Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk$^+$ methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. These common factors introduce dependence between policyholders in annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. Moreover this framework allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include improvement in health treatments or better medication. In this paper, using publicly available data for Australia, we estimate the model using Markov chain Monte Carlo method to identify leading death causes across all age groups including long term forecast for 2031 and 2051. On top of general reduced mortality, the proportion of deaths for certain certain causes has changed massively over the period 1987 to 2011. Our model forecasts suggest that if these trends persist, then the future gives a whole new picture of mortality for people aged above 40 years. Neoplasms will become the overall number-one death cause. Moreover, deaths due to mental and behavioural disorders are very likely to surge whilst deaths due to circulatory diseases will tend to decrease. This potential increase in deaths due to mental and behavioural disorders for older ages will have a massive impact on social systems as, typically, such patients need long-term geriatric care. |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1507.07162&r=rmg |