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on Risk Management |
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Issue of 2016‒01‒29
eleven papers chosen by |
| By: | Jonas Hirz; Uwe Schmock; Pavel V. Shevchenko |
| Abstract: | In this paper we describe a useful risk management tool to analyse annuity and life insurance portfolios where mortality is modelled stochastically. Yet, there exists a fast and numerically stable algorithm to derive loss distributions exactly, even for large portfolios. We provide various estimation procedures based on publicly available data. The model allows for various other applications, including mortality forecasts. Compared to the Lee-Carter model, we have a more flexible framework, get tighter bounds and can directly extract several sources of uncertainty. Straight-forward model validation techniques are available. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.04557&r=rmg |
| By: | Richard Harris; Linh Nguyen; Evarist Stoja |
| Abstract: | We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali, Demirtas, and Levy (2009) with a Markov switching mechanism. We show that the risk-return relationship identified by Bali, Demirtas, and Levy (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship under all states of market volatility. |
| Keywords: | Downside risk; Markov switching; Value-at-Risk; Leverage effect; Volatility feedback effect. |
| JEL: | C13 C14 C58 G10 G11 G12 |
| Date: | 2015–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:bri:accfin:15/2&r=rmg |
| By: | Matt V. Leduc; Sebastian Poledna; Stefan Thurner |
| Abstract: | We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way that makes it more resilient to insolvency cascades. A regulator can use information about the topology of the interbank network to devise a systemic insurance surcharge that is added to the CDS spread. CDS contracts are thus effectively penalized according to how much they contribute to increasing systemic risk. CDS contracts that decrease systemic risk remain untaxed. We simulate this regulated CDS market using an agent-based model (CRISIS macro-financial model) and we demonstrate that it leads to an interbank system that is more resilient to insolvency cascades. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.02156&r=rmg |
| By: | Dungey, Mardi (Tasmanian School of Business & Economics, University of Tasmania); Luciani, Matteo (ECARES, Universite libre de Bruxelles); Matei, Marius (Tasmanian School of Business & Economics, University of Tasmania); Veredas, David (ECARES, Universite libre de Bruxelles) |
| Abstract: | We provide empirical evidence on the degree of systemic risk in Australia before, during and after the Global Financial Crisis. We calculate a daily index of systemic risk from 2004 to 2013 in order to understand how real economy firms influence the outcomes for the rest of the economy. This is done via a mapping of the interconnectedness of the financial and non-financial sectors. The financial sector is in general the home to the most consistently systemically risky firms in the economy. The mining sector becomes occasionally as systemically risky as the financial sector, reflecting the importance of understanding the interrelationships between the financial sector and the real economy in monitoring systemic risks. |
| Keywords: | banking, insurance, systemic risk |
| JEL: | G22 G21 G01 G28 |
| Date: | 2015–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:22658&r=rmg |
| By: | Dungey, Mardi (Tasmanian School of Business and Economics); Tchatoka, Firmin Doko (Tasmanian School of Business and Economics); Wells, Graeme (Tasmanian School of Business and Economics); Yanotti, Maria Belen (School of Business and Exonomics, University of Tasmania) |
| Abstract: | This paper sheds new light on the role of borrower characteristics in mortgage product choice, and how these are impacted by regulatory capital requirements. Using rich loan-level data from the Australian market we analyse the way in which these risk effects impact the choice between adjustable rate mortgages and a range of complex mortgages which provide reduced initial payments.For the first time we find that all three of income, wealth and mobility risks play a role in product choice. We also investigate the role of regulatory capital requirements in an environment where banks hold mortgage risk on their balance sheet and find that the Basel capital discounts based on loan-to-valuation ratios divide otherwise similar borrowers between ARM and CM product choices. Year:2014 |
| Keywords: | Mortgage Choice Basel Banks |
| JEL: | G21 G18 |
| Date: | 2014–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:17831&r=rmg |
| By: | J\'ozsef Mezei; Peter Sarlin |
| Abstract: | This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components that are in turn assessed using a set of risk measures and their relationships. For this purpose, motivated by the development of the Choquet integral, we employ the RiskRank function to aggregate risk measures, allowing for the integration of the interrelation of different factors in the aggregation process. The use of RiskRank is illustrated through a real-world case in a European setting, in which we show that it performs well in out-of-sample analysis. In the example, we provide an estimation of systemic risk from country-level risk and cross-border linkages. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.06204&r=rmg |
| By: | Lilit Popoyan (Institute of Economics, (LEM)); Mauro Napoletano (OFCE Sciences Po and Skema Businnes School); Andrea Roventini (Institute of Economics, (LEM)) |
| Abstract: | We develop an agent-based model to study the macroeconomic impact of alternative macroprudential regulations and their possible interactions with dierent monetary policy rules.The aim is to shed light on the most appropriate policy mix to achieve the resilience of the banking sector and foster macroeconomic stability. Simulation results show that a triple- mandate Taylor rule, focused on output gap, inflation and credit growth, and a Basel III prudential regulation is the best policy mix to improve the stability of the banking sector and smooth output fluctuations. Moreover, we consider the different levers of Basel III and their combinations. We find that minimum capital requirements and counter-cyclical capital buffers allow to achieve results close to the Basel III first-best with a much more simplifiedregulatory framework. Finally, the components of Basel III are non-additive: the inclusion of an additional lever does not always improve the performance of the macro prudential regulation |
| Keywords: | Macro-prudential policy, Basel III regulation, financial stability, monetary policy, agent-based computational economics. |
| JEL: | C63 E52 E6 G01 G21 G28 |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:fce:doctra:1532&r=rmg |
| By: | Ilya Khankov (National Research University Higher School of Economics, Moscow); Henry Penikas (National Research University Higher School of Economics, Moscow) |
| Abstract: | Research is devoted to examination of the classifier, based on copula discriminant analysis (CODA). Performance of the classification of this algorithm was assessed. On samples, modelled with some typical features of corporate default data, sensitivity of the classifier was tested, to sample size, to default rate and to different patterns of variables’ interdependence. Alternative copula families’ selection method is proposed based on certain performance metric optimization. Difference in classification performance of different algorithms are investigated. On real data of Russian corporate defaults, CODA classifier was built. It was supported by single factor analysis, based on discriminant analysis too. Final model demonstrates better classification performance than Linear Discriminant Analysis and Random Forest algorithm, and is comparable to Quadratic Discriminant Analysis. Another experiment was set on data of Russian banks. Single factor analysis was assessed via standard procedure. CODA performance appeared to be lower than of Random Forest here, it was similar to QDA |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0113&r=rmg |
| By: | Szüle, Borbála |
| Abstract: | Return guarantee constitutes a key ingredient of classical life insurance premium calculation. In the current low interest rate environment insurers face increasingly strong financial incentives to reduce guaranteed returns embedded in life insurance contracts. However, return guarantee lowering efforts are restrained by associated demand effects, since a higher guaranteed return makes the net price of the insurance cover lower. This tradeoff between possibly higher future insurance obligations and the possibility of a larger demand for life insurance products can theoretically also be considered when determining optimal guaranteed returns. In this paper, optimality of return guarantee levels is analyzed from a solvency point of view. Availability and some other properties of optimal solutions for guaranteed returns are explored and compared in a simple model for two measures of solvency risk (company-level and contract-level VaR). The paper concludes that a solvency risk minimizing optimal guaranteed return may theoretically exist, although its practical availability can be impeded by economic and regulatory constraints. |
| Keywords: | risk analysis, insurance |
| JEL: | G11 G22 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:cvh:coecwp:2016/02&r=rmg |
| By: | Micha{\l} Barski |
| Abstract: | The probability minimizing problem of large losses of portfolio in discrete and continuous time models is studied. This gives a generalization of quantile hedging presented in [3]. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.03388&r=rmg |
| By: | Ravi Kashyap |
| Abstract: | We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control and hence in the decision making process could potentially offset the uncertainty inherent in the environment and yield better outcomes. This methodology is suitable for the social sciences since the primary source of uncertainty are the members of the system themselves and presently, no methods are known to fully determine the outcomes in such an environment, which perhaps would require being able to read the minds of everyone involved and to anticipate their actions continously. Admittedly, we are not qualified to recommend whether such an approach is conducive for the natural sciences, unless perhaps, bounds can be established on the levels of uncertainty in a system and it is shown conclusively that a better understanding of the system and hence improved decision making will not alter the outcomes. We consider a number of examples and develop both the theoretical framework and empirical tests where such an approach might be helpful. 1. Newsvendor Inventory Management Problem 2. School Admissions. 3. Journal Submissions. 4. Job Candidate Selection. 5. Stock Picking. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.04043&r=rmg |