nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒09‒18
twelve papers chosen by

  1. Real estate market risk modelling By M. Katyoka; S. Stevenson
  2. Ex-ante real estate Value at Risk calculation method By C.O. Amédée-Manesme; F. Barthélémy
  3. Modeling financial sector joint tail risk in the euro area By Lucas, André; Schwaab, Bernd; Zhang, Xin
  4. Network linkages to predict bank distress By Peltonen, Tuomas A.; Sarlin, Peter; Piloiu, Andreea
  5. Basis risk in static versus dynamic longevity-risk hedging By Clemente De Rosa; Elisa Luciano; Luca Regis
  6. An alternative view of exchange market pressure episodes in emerging Europe: an analysis using Extreme Value Theory (EVT) By Heinz, Frigyes Ferdinand; Rusinova, Desislava
  7. Extreme downside risk and financial crises By Harris, Richard D. F.; Nguyen, Linh H; Stoja, Evarist
  8. Capital regulation in a macroeconomic model with three layers of default By Derviz, Alexis; Mendicino, Caterina; Moyen, Stéphane; Nikolov, Kalin; Stracca, Livio; Clerk, Laurent; Suarez, Javier; Vardoulakis, Alexandros P.
  9. Risks Assessment In Real Esate Investments: An AHP Approach By C. D'Alpaos; R. Canesi
  10. A false sense of security in applying handpicked equations for stress test purposes By Gross, Marco; Población, Javier
  11. Systematic Risk Factors in European Infrastructure Stock Markets By D. Wurstbauer; S. Lang; W. Schaefers; C. Rothballer
  12. Derivatives Markets: From Bank Risk Management to Financial Stability By Guillaume Vuillemey

  1. By: M. Katyoka; S. Stevenson
    Abstract: The global financial crisis towards the end of the last decade saw an increased interest in the role of risk management in the main stream financial investment market. Among other things, the measurement and management of market risk, credit risk and operational risk have become more pronounced than ever before. Value-at-risk (VaR), a tool which assesses the maximum possible loss of an investment, assuming a given confidence level, is widely used in the investment world to measure market and credit risk. This measure has however come under constant criticism as it only considers the maximum loss for a specific confidence level and ignores any losses beyond that threshold, which could arise from extreme events. Secondly, VaR assumes normal distribution of returns and yet this is not the case with most financial returns, which have the added complexity of being susceptible to the phenomenon of ‘fat tails’. (It should be noted however, that it is only the basic version of VaR that has the normality issue and this can be addressed through the use of Monte Carlo.) Thus, the credibility of VaR seems to be losing ground. Though derived from the principles of VaR, the expected shortfall (ES) is being forwarded as an alternative proposition due to its ability to overcome some of the shortcoming of VaR, particularly when it comes to dealing with tail risk. To this effect, the ES is being mooted as a tool for market risk regulation, replacing VaR in the banking sector as proposed by the Basel Committee on Banking Supervision. This said, the ES has its own challenges especially because it cannot be subjected to back-testing due to its non-listable attribute. Furthermore ES is also said to be quite sensitive to extreme values. In the real estate market, very limited research has been conducted on modelling market risk. This study therefore aims to investigate market risk modelling for real estate and assess whether, and / or the extent to which, the expected shortfall model offers a better alternative to VaR in terms of measuring market risk. Public real estate has been chosen as the focus of the study as it is more amenable to the application of VaR compared to private real estate.
    Keywords: Expected Shortfall; Market Risk; Real Estate Risk; Risk Measurement; Value At Risk
    JEL: R3
    Date: 2015–07–01
  2. By: C.O. Amédée-Manesme; F. Barthélémy
    Abstract: The computation of Value at Risk ($VaR$) has long been a problematic issue in commercial real estate. Difficulties mainly arise from the lack of appropriate data, lack of transactions, the non-normality of returns, and the inapplicability of many of the traditional methodologies. In addition, real estate investment is difficult to diversify and specific risk remains latent in investors' portfolio. It follows that risk of the entire market does not correspond to risk an investor bears. Therefore the risk measurements based on index do not represent the specific portfolio risk. As a result, calculation of this risk measure has rarely been done in the Real Estate field. However, following a spate of new regulations such as Basel II, Basel III, NAIC and Solvency II, financial institutions have increasingly been required to estimate and control their exposure to market risk. Hence, financial institutions now commonly use ``internal'' $VaR$ (or Expected Shortfall) models in order to assess their market risk exposure. The purpose of this paper is to propose a model that incorporates real estate portfolio specificities in a real estate VaR model.
    Keywords: Real Estate Finance; Regulation; Risk Measurement; Value At Risk
    JEL: R3
    Date: 2015–07–01
  3. By: Lucas, André; Schwaab, Bernd; Zhang, Xin
    Abstract: We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for numerous financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We apply a conditional law of large numbers in this setting to define joint and conditional risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks between 2011-2012, as well as their steep decline following subsequent policy actions. JEL Classification: G21, C32
    Keywords: dynamic equicorrelation, generalized hyperbolic distribution, large portfolio approximation, law of large numbers
    Date: 2015–08
  4. By: Peltonen, Tuomas A.; Sarlin, Peter; Piloiu, Andreea
    Abstract: Building on the literature on systemic risk and financial contagion, the paper introduces estimated network linkages into an early-warning model to predict bank distress among European banks. We use multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets' view of bank interconnectedness in case of elevated financial stress. The paper finds that early warning models including estimated tail dependencies consistently outperform bank-specific benchmark models with- out networks. The results are robust to variation in model specification and also hold in relation to simpler benchmarks of contagion. Generally, this paper gives direct support for measures of interconnectedness in early-warning models, and moves toward a unified representation of cyclical and cross-sectional dimensions of systemic risk. JEL Classification: G21, G33, C54, D85
    Keywords: bank distress, bank networks, systemic risk
    Date: 2015–07
  5. By: Clemente De Rosa; Elisa Luciano; Luca Regis
    Abstract: This paper provides a simple model for basis risk in a longevity framework, by separating common and idiosyncratic risk factors. Basis risk is captured by a single parameter, that measures the co-movement between the portfolio and the reference population. In this framework, the paper sets out the static, swap-based hedge for an annuity, and compares it with the dynamic, delta-based hedge, achieved using longevity bonds. We assume that the longevity intensity is distributed according to a CIR-type process and provide closed-form derivatives prices and hedges, also in the presence of an analogous CIR process for interest rate risk.
    Keywords: longevity risk, basis risk, static vs. dynamic hedging, longevity swaps, longevity bonds.
    JEL: G22 G32
    Date: 2015
  6. By: Heinz, Frigyes Ferdinand; Rusinova, Desislava
    Abstract: Using extreme value theory tools, we demonstrate that the distributions of the exchange market pressure (EMP) series for most of twelve emerging Europe countries have heavy tails, and disregarding their tail properties may lead to substantial underestimation of the probability of tail events. Using an extreme-value-based EMP crisis definition leads to a different set of crisis determinants compared to a definition based on standard errors. The probability of extreme EMP periods in our sample is affected by global risk aversion, regional contagion, the level of international reserves, foreign direct investment, history of past crises and accumulated domestic credit and real exchange rate related imbalances. JEL Classification: C10, E44, F37, F32, G01
    Keywords: Contagion, Currency crisis, Exchange market pressure, Extreme value theory, Macroeconomic imbalances
    Date: 2015–06
  7. By: Harris, Richard D. F. (University of Exeter); Nguyen, Linh H (University of Exeter); Stoja, Evarist (University of Bristol)
    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; financial crisis; value at risk; leverage effect; volatility feedback effect.
    JEL: C13 C14 C58 G10 G11 G12
    Date: 2015–09–11
  8. By: Derviz, Alexis; Mendicino, Caterina; Moyen, Stéphane; Nikolov, Kalin; Stracca, Livio; Clerk, Laurent; Suarez, Javier; Vardoulakis, Alexandros P.
    Abstract: We develop a dynamic general equilibrium model for the positive and normative analysis of macroprudential policies. Optimizing financial intermediaries allocate their scarce net worth together with funds raised from saving households across two lending activities, mortgage and corporate lending. For all borrowers (households, firms, and banks) external financing takes the form of debt which is subject to default risk. This “3D model” shows the interplay between three interconnected net worth channels that cause financial amplification and the distortions due to deposit insurance. We apply it to the analysis of capital regulation. JEL Classification: E3, E44, G01, G21
    Keywords: Default risk, Financial frictions, Macroprudential policy
    Date: 2015–07
  9. By: C. D'Alpaos; R. Canesi
    Abstract: Purpose: Aim of the paper is to provide an ex-ante valuation model to address risk and uncertainty in real estate investment decisions. We propose a model for risks assessment that helps to evaluate risks and opportunities of real estate assets taking into consideration different aspects of the project and related risks (market risk, valuation risk, market growth risk, operating risk, etc.). Our main objective is rather to provide research tools that reveal the riskiness of a property investment than to provide an interpretative model. Design/Methodology/Approach -Â Rigorous risk assessment measures, based on mathematical algorithms, are here presented. Specifically, we propose an overall risk scoring model to classify real estate investments' riskiness and we propose a procedure for a synthetic risks assessment that, based on the AHP model, will help investors to manage risk exposure and opportunities in property investments. Findings -Â We define the risk components and relative measures according to the literature and experts in real estate investments. We determine each risk component by implementing the mathematical algorithms provided. Then, according to a pool of experts and financial managers' judgments, we define the thresholds to classify each risk component as conservative, moderate, aggressive and finally we aggregate them into a synthetic overall risk index. Numerical examples on urban development projects are presented in order to test the effectiveness of the AHP model in supporting decisions and adapting strategies to a permanently changing environment. Research limitations/implications -Â We provide mathematical algorithms, adaptable and interpretable, that can be generally applied in real estate investments. The proposed model can be easily understood by third parties and applied to different property types. Risk measures and relative thresholds may be dependent on the investment (e.g. new development, renewal, etc.) and the property type (e.g. office vs residential building, etc). As far as the scoring model is concerned, the weighting has been identified with reference to the Italian scenario, and similarly the classification of risks. Originality/values - The risk assessment model here proposed may have interesting effects in terms of risk management strategies. Results are transparent and easy to be understood.
    Keywords: Analytic Hierarchy Process (AHP); Real Estate Investments; Risk Assessment; Uncertainty
    JEL: R3
    Date: 2015–07–01
  10. By: Gross, Marco; Población, Javier
    Abstract: The purpose of this paper is to promote the use of Bayesian model averaging for the design of satellite models that financial institutions employ for stress testing. Banks employing ’handpicked’ equations – while meeting standard economic and econometric soundness criteria – risk significantly underestimating the response of risk parameters and therefore overestimating their capital absorption capacity. We present a set of credit risk models for 18 EU countries based both on the model averaging scheme as well as a series of handpicked equations and apply them to a sample of 108 SSM banks. We thereby aim to illustrate that the handpicked equations may indeed imply significantly lower default flow estimates and therefore overoptimistic estimates for the banks’ capital absorption capacity. The model averaging scheme that we promote should mitigate that risk and also help establish a level playing field with regard to a common level of conservatism across banks. JEL Classification: C11, C22, C51, E58, G21
    Keywords: bank regulation and supervision, model averaging, satellite modeling, stress testing
    Date: 2015–09
  11. By: D. Wurstbauer; S. Lang; W. Schaefers; C. Rothballer
    Abstract: This paper is the first to investigate the systematic risk factors driving European infrastructure equity returns using traditional asset pricing models. As infrastructure companies are exposed to specific risks such as regulatory changes, a lack of product diversification and construction risks, the pricing should differ substantially from that of general equities. Further differences are expected, due to the monopolistic environment in which many infrastructure companies operate. The major issue researchers face when analyzing infrastructure stocks is the lack infrastructure indices with a sufficiently high number of constituents and data history. Therefore, the data sample will be constructed by retrieving all dead and active European stocks and subsequently filtering them, based on the Standard Industrial Classification (SIC). Accordingly, only stocks in the sectors of telecommunication, transport and utilities are chosen. As the industry definitions include a large variety of economic activities in the infrastructure sectors, such as service or product suppliers, the sample needs to be screened in a second step for companies that focus mainly on "pure" infrastructure business. This screening will be conducted on the basis of the business descriptions retrieved from various data sources, such as Datastream and Google Finance and annual reports (asset test and revenue test). As indicated, the body of literature on asset pricing studies of infrastructure equities is very limited. This is surprising, since investor appetite for infrastructure investments and has grown significantly over the past few years. Consequently, this paper contributes to our understanding of infrastructure equities. Portfolio managers and investors can draw on the findings, in order to manage their risk exposure more efficiently.
    Keywords: Asset Pricing; Fama French; Infrastructure Investments; Systematic Risk
    JEL: R3
    Date: 2015–07–01
  12. By: Guillaume Vuillemey (Département d'économie)
    Abstract: Dans sa première partie, cette thèse étudie l’utilisation optimale des produits dérivés par les intermédiaires financiers dans leur gestion du risque, en prêtant spécifiquement attention au marché des dérivés de taux d’intérêt. En modélisant la structure de capital optimale d’une banque, le premier chapitre montre comment l’usage optimal des dérivés affecte certaines décisions souvent étudiées en finance d’entreprise : l’offre de crédit, la transformation de maturité, la politique de dividendes ou les probabilités de défaut. La seconde partie de la thèse étudie au contraire le marché des dérivés comme un système à part entière. Le second chapitre utilise une base de données nouvelle et unique d’expositions bilatérales sur des contrats CDS afin d’offrir une description détaillée de la structure du réseau des expositions. Le troisième chapitre a pour objet la régulation des marchés de produits dérivés. Il étudie la compensation centrale des produits dérivés standardisés, et la demande de collatéral induite par cette réforme à l’échelle mondiale, sous une variété d’hypothèses concernant la microstructure du marché.
    Keywords: Produits dérivés, Intermédiation financière, Compensation centrale, Risque systémique, Derivatives, Financial intermediation, Central clearing, Systemic risk
    Date: 2015–07

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.