nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒08‒13
ten papers chosen by

  1. When Gambling for Resurrection is Too Risky By Divya Kirti
  2. On Swing Pricing and Systemic Risk Mitigation By Sheheryar Malik; Peter Lindner
  3. Which Banks Recover From Large Adverse Shocks? By Emilia Bonaccorsi di Patti; Anil Kashyap
  4. Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach By Luca Barbaglia; Christophe Croux; Ines Wilms
  5. How to Predict Financial Stress? An Assessment of Markov Switching Models By Thibaut Duprey; Benjamin Klaus
  6. Spectral backtests of forecast distributions with application to risk management By Michael B. Gordy; Hsiao Yen Lok; Alexander J. McNeil
  7. A new database for financial crises in European countries By Lo Duca, Marco; Koban, Anne; Basten, Marisa; Bengtsson, Elias; Klaus, Benjamin; Kusmierczyk, Piotr; Lang, Jan Hannes; Detken, Carsten; Peltonen, Tuomas
  8. Vector-Valued Multivariate Conditional Value-at-Risk By Merve Merakli; Simge Kucukyavuz
  9. Political Distribution Risk and Aggregate Fluctuations By Thorsten Drautzburg; Jesús Fernández-Villaverde; Pablo Guerrón-Quintana
  10. Financial option insurance By Qi-Wen Wang; Jian-Jun Shu

  1. By: Divya Kirti
    Abstract: Rather than taking on more risk, US insurers hit hard by the crisis pulled back from risk taking, relative to insurers not hit as hard by the crisis. Capital requirements alone do not explain this risk reduction: insurers hit hard reduced risk within assets with identical regulatory treatment. State level US insurance regulation makes it unlikely this risk reduction was driven by moral suasion. Other financial institutions also reduce risk after large shocks: the same approach applied to banks yields similar results. My results suggest that, at least in some circumstances, franchise value can dominate, making gambling for resurrection too risky.
    Date: 2017–08–01
  2. By: Sheheryar Malik; Peter Lindner
    Abstract: Swing pricing allows a fund manager to transfer to redeeming or subscribing investors the costs associated with their trading activity, thus potentially discouraging large flows. This liquidity management tool, which is already used in major jurisdictions, may also help mitigate systemic risk. Here we develop and apply a methodology to investigate whether swing pricing does in fact help dampen flows out of funds, especially during periods of market stress. Drawing on evidence of first-mover advantage within a group of ‘swinging’ corporate bond funds, we provide policy considerations for enhancing the tool’s effectiveness as a systemic risk mitigant.
    Keywords: Financial crises;Liquidity management; Mutual funds; Redemptions; Systemic risk; Swing pricing, Liquidity management, Mutual funds, Redemptions, Systemic risk, Swing pricing, Model Construction and Estimation, Quantitative Policy Modeling
    Date: 2017–07–18
  3. By: Emilia Bonaccorsi di Patti; Anil Kashyap
    Abstract: We analyze the fate of 110 Italian banks that experienced abrupt drops in profitability, from which about 1/3 recover. Recovery depends primarily on post-shock adjustments made by the banks, particularly to their loan portfolios. Matched bank-borrower data shows that recovering banks are significantly more aggressive in managing their riskiest clients. The risk management differences are consistent with some banks cutting credit to very riskiest clients while others appear to be gambling for reclamation by continuing to extend credit to high risk borrowers.
    JEL: G21 G28
    Date: 2017–08
  4. By: Luca Barbaglia; Christophe Croux; Ines Wilms
    Abstract: Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the possible fat-tailed distribution of the VAR model errors using a VAR model with errors following a multivariate Student t-distribution with unknown degrees of freedom. Moreover, we study volatility spillovers among a large number of assets. To this end, we use penalized estimation of the VAR model with t-distributed errors. We study volatility spillovers among energy, biofuel and agricultural commodities and reveal bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
    Date: 2017–08
  5. By: Thibaut Duprey; Benjamin Klaus
    Abstract: This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is owing to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the Markov switching model is outperforming the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress.
    Keywords: Business fluctuations and cycles, Central bank research, Econometric and statistical methods, Financial markets, Financial stability, Financial system regulation and policies, Monetary and financial indicators
    JEL: C54 G01 G15
    Date: 2017
  6. By: Michael B. Gordy; Hsiao Yen Lok; Alexander J. McNeil
    Abstract: We study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modeled probability level. The choice of the kernel function makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and propose novel variants as well. We assess the size and power of the backtests in realistic sample sizes, and in particular demonstrate the tradeoff between power and specificity in validating quantile forecasts.
    Date: 2017–08
  7. By: Lo Duca, Marco; Koban, Anne; Basten, Marisa; Bengtsson, Elias; Klaus, Benjamin; Kusmierczyk, Piotr; Lang, Jan Hannes; Detken, Carsten; Peltonen, Tuomas
    Abstract: This paper presents a new database for financial crises in European countries, which serves as an important step towards establishing a common ground for macroprudential oversight and policymaking in the EU. The database focuses on providing precise chronological definitions of crisis periods to support the calibration of models in macroprudential analysis. An important contribution of this work is the identification of financial crises by combining a quantitative approach based on a financial stress index with expert judgement from national and European authorities. Key innovations of this database are (i) the inclusion of qualitative information about events and policy responses, (ii) the introduction of a broad set of non-exclusive categories to classify events, and (iii) a distinction between event and post-event adjustment periods. The paper explains the two-step approach for identifying crises and other key choices in the construction of the dataset. Moreover, stylised facts about the systemic crises in the dataset are presented together with estimations of output losses and fiscal costs associated with these crises. A preliminary assessment of the performance of standard early warning indicators based on the new crises dataset confirms findings in the literature that multivariate models can improve compared to univariate signalling models. JEL Classification: G01, E44, E58, E60, H12
    Keywords: central bank statistics, crises database, early warning models, financial crises, macroprudential
    Date: 2017–07
  8. By: Merve Merakli; Simge Kucukyavuz
    Abstract: In this study, we propose a new definition of multivariate conditional value-at-risk (MCVaR) as a set of vectors for discrete probability spaces. We explore the properties of the vector-valued MCVaR (VMCVaR) and show the advantages of VMCVaR over the existing definitions given for continuous random variables when adapted to the discrete case.
    Date: 2017–08
  9. By: Thorsten Drautzburg; Jesús Fernández-Villaverde; Pablo Guerrón-Quintana
    Abstract: We argue that political distribution risk is an important driver of aggregate fluctuations. To that end, we document significant changes in the capital share after large political events, such as political realignments, modifications in collective bargaining rules, or the end of dictatorships, in a sample of developed and emerging economies. These policy changes are associated with significant fluctuations in output and asset prices. Using a Bayesian proxy-VAR estimated with U.S. data, we show how distribution shocks cause movements in output, unemployment, and sectoral asset prices. To quantify the importance of these political shocks for the U.S. as a whole, we extend an otherwise standard neoclassical growth model. We model political shocks as exogenous changes in the bargaining power of workers in a labor market with search and matching. We calibrate the model to the U.S. corporate non-financial business sector and we back up the evolution of the bargaining power of workers over time using a new methodological approach, the partial filter . We show how the estimated shocks agree with the historical narrative evidence. We document that bargaining shocks account for 34% of aggregate fluctuations.
    JEL: E32 E37 E44 J20
    Date: 2017–08
  10. By: Qi-Wen Wang; Jian-Jun Shu
    Abstract: The option is a financial derivative, which is regularly employed in reducing the risk of its underlying securities. However, investing in option is still risky. Such risk becomes much severer for speculators who utilize option as a means of leverage to increase their potential returns. In order to mitigate risk on their positions, the rudimentary concept of financial option insurance is introduced into practice. Two starkly-dissimilar concepts of insurance and financial option are integrated into the formation of financial option insurance. The proposed financial product insures investors option premiums when misfortune befalls on them. As a trade-off, they are likely to sacrifice a limited portion of their potential profits. The loopholes of prevailing financial market are addressed and the void is filled by introducing a stable three-entity framework. Moreover, a specifically designed mathematical model is proposed. It consists of two portions: the business strategy of matching and a verification-and-modification process. The proposed model enables the option investors with calls and puts of different moneyness to be protected by the issued option insurance. Meanwhile, it minimizes the exposure of option insurers position to any potential losses.
    Date: 2017–08

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