New Economics Papers
on Risk Management
Issue of 2013‒10‒25
ten papers chosen by

  1. On the strategic value of risk management By Léautier, Thomas-Olivier; Rochet, Jean-Charles
  2. The Origin of Fat Tails By Martin Gremm
  3. Pricing Default Events: Surprise, Exogeneity and Contagion. By Gouriéroux, C.; Monfort, A.; Renne, J-P.
  4. Risk Assessment and Hazard Mapping By Junko Sagara; Keiko Saito
  5. Market-Based Bank Capital Regulation By Jeremy Bulow; Paul Klemperer
  6. Hydro-meteorological Disasters Associated with Tsunamis and Earthquakes By Junko Sagara
  7. Measuring the Cost-effectiveness of Various DRM Measures By Masato Toyama; Junko Sagara
  8. How to Identify and Forecast Bull and Bear Markets? By Kole, H.J.W.G.; Dijk, D.J.C. van
  9. Energy price transmissions during extreme movements By Marc Joëts
  10. Graphical network models for international financial flows By Paolo Giudici; Alessandro Spelta

  1. By: Léautier, Thomas-Olivier; Rochet, Jean-Charles
    Abstract: This article examines how …rms facing volatile input prices and holding some degree of market power in their product market link their risk management and their production or pricing strategies. This issue is relevant in many industries ranging from manufacturing to energy retailing, where risk averse …rms decide on their hedging strategies before their product market strategies. We …nd that hedging modi…es the pricing and production strategies of …rms. This strategic e¤ect is channelled through the risk-adjusted expected cost, i.e., the expected marginal cost under the probability measure induced by shareholders risk aversion. It has opposite e¤ects depending on the nature of product market competition: hedging toughens quantity competition while it softens price competition. Finally, if …rms can decide not to commit on their hedging position, this can never be an equilibriumoutcome: committing is always a best response to non committing. In the Hotelling model, committing is a dominant strategy for all …rms.
    Keywords: Risk Management, Price and Quantity Competition.
    JEL: G32 L13
    Date: 2013–09–14
  2. By: Martin Gremm
    Abstract: We propose a random walk model of asset returns where the parameters depend on market stress. Stress is measured by, e.g., the value of an implied volatility index. We show that model parameters including standard deviations and correlations can be estimated robustly and that all distributions are approximately normal. Fat tails in observed distributions occur because time series sample different stress levels and therefore different normal distributions. This provides a quantitative description of the observed distribution including the fat tails. We discuss simple applications in risk management and portfolio construction
    Date: 2013–10
  3. By: Gouriéroux, C.; Monfort, A.; Renne, J-P.
    Abstract: In order to derive closed-form expressions of the prices of credit derivatives, standard credit-risk models typically price the default intensities, but not the default events themselves. The default indicator is replaced by an appropriate prediction and the prediction error, that is the default-event surprise, is neglected. Our paper develops an approach to get closed-form expressions for the prices of credit derivatives written on multiple names without neglecting default-event surprises. This approach differs from the standard one, since the default counts necessarily cause the factor process under the risk-neutral probability, even if this is not the case under the historical probability. This implies that the standard exponential pricing formula of default does not apply. Using U.S. bond data, we show that allowing for the pricing of default events has important implications in terms of both data-fitting and model-implied physical probabilities of default. In particular, it may provide a solution to the credit spread puzzle. Besides, we show how our approach can be used to account for the propagation of defaults on the prices of credit derivatives.
    Keywords: Credit Derivative, Default Event, Default Intensity, Frailty, Contagion, Credit Spread Puzzle.
    JEL: E43 E47 G12
    Date: 2013
  4. By: Junko Sagara; Keiko Saito
    Keywords: Urban Development - Hazard Risk Management Food and Beverage Industry Environment - Natural Disasters International Terrorism and Counterterrorism Conflict and Development - Disaster Management Industry
    Date: 2013–01
  5. By: Jeremy Bulow (Standford University, USA); Paul Klemperer (Nuffield College, University of Oxford, UK)
    Abstract: Today’s regulatory rules, especially the easily-manipulated measures of regulatory capital, have led to costly bank failures. We design a robust regulatory system such that (i) bank losses are credibly borne by the private sector (ii) systemically important institutions cannot collapse suddenly; (iii) bank investment is counter-cyclical; and (iv) regulatory actions depend upon market signals (because the simplicity and clarity of such rules prevents gaming by firms, and forbearance by regulators, as well as because of the efficiency role of prices). One key innovation is “ERNs” (equity recourse notes--superficially similar to, but importantly distinct from, “cocos”) which gradually "bail in" equity when needed. Importantly, although our system uses market information, it does not rely on markets being “right”.
    Date: 2013–09–15
  6. By: Junko Sagara
    Keywords: Urban Development - Hazard Risk Management Conflict and Development - Disaster Management Water Resources - River Basin Management Science and Technology Development - Engineering Environment - Natural Disasters
    Date: 2013–01
  7. By: Masato Toyama; Junko Sagara
    Keywords: Urban Development - Hazard Risk Management Transport Economics Policy and Planning Environment - Natural Disasters Private Sector Development - Business in Development Conflict and Development - Disaster Management Transport
    Date: 2013–01
  8. By: Kole, H.J.W.G.; Dijk, D.J.C. van
    Abstract: The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic mea- sure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant.
    Keywords: forecast evaluation;regime switching;stock market;economic comparison
    Date: 2013–10–14
  9. By: Marc Joëts
    Abstract: This paper investigates price transmissions across European energy forward markets at distinct maturities during both normal times and extreme ‡uctuation periods. To this end, we rely on the traditional Granger causality test (in mean) and its multivariate extension in tail distribution developped by Candelon, Joëts, and Tokpavi (2013). Con- sidering forward energy prices at 1, 10, 20, and 30 months, it turns out that no signi…cant causality exists between markets at regular times whereas comovements are at play during extreme periods especially in bear markets. More precisely, energy prices comovements appear to be stronger at short horizons than at long horizons, testifying an eventual Samuelson mechanism in the maturity prices curve. Diversi…cation strategies tend to be more e¢ cient as maturity increases.
    Keywords: Value-at-Risk (VaR); CAViaR approach; risk spillover; Granger causality.
    JEL: C32 Q40
    Date: 2013–10–15
  10. By: Paolo Giudici (Department of Economics and Management, University of Pavia); Alessandro Spelta (Department of Economics and Management, University of Pavia)
    Abstract: The late-2000s financial crisis has stressed the need of understanding the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, that take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of graphical Gaussian models, which can be employed to accurately estimate the adjacency matrix, the main input for the estimation of the interconnections between different countries. We consider different types of graphical models: besides classical ones, we introduce Bayesian graphical models, that can take model uncertainty into account, and dynamic Bayesian graphical models, that provide a convenient framework to model temporal cross-border data, decomposing the model into autoregressive and contemporaneous networks. The paper shows how the application of the proposed models to the Bank of International Settlements locational banking statistics allows the identification of four distinct groups of countries, that can be considered central in systemic risk contagion.
    Keywords: Financial network models, Graphical models, Bayesian model selection
    Date: 2013–10

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