New Economics Papers
on Risk Management
Issue of 2014‒02‒08
six papers chosen by

  1. Pricing Default Risk: The Good, The Bad, and The Anomaly By Ferreira Filipe, Sara; Grammatikos, Theoharry; Michala, Dimitra
  2. Option Pricing, Historical Volatility and Tail Risks By Samuel E. Vazquez
  3. Elimination of systemic risk in financial networks by means of a systemic risk transaction tax By Sebastian Poledna; Stefan Thurner
  4. Cross-correlation asymmetries and causal relationships between stock and market risk within linear response approximation By Stanislav S. Borysov; Alexander V. Balatsky
  5. Performance of credit risk prediction models via proper loss functions By Silvia Figini; Mario Maggi
  6. Volatility Connectedness of Bank Stocks Across the Atlantic By Kamil Yilmaz

  1. By: Ferreira Filipe, Sara; Grammatikos, Theoharry; Michala, Dimitra
    Abstract: While the empirical literature has often documented a “default anomaly”, i.e. a negative relation between default risk and stock returns, standard theory suggests that default risk should be priced in the cross-section. In this paper, we provide an explanation for this apparent puzzle using a new approach. First we calculate monthly physical probabilities of default (PDs) for a large sample of European firms. Second we decompose these estimated PDs into systematic and idiosyncratic components; we measure the systematic part as the sensitivity of the physical PD to an aggregate measure of default risk. While sorting stocks based on physical PDs confirms a possible default anomaly, we find that the relation between the systematic default risk and stock returns is in fact positive. Our results therefore suggest that risker stocks, as measured by the physical PDs, will tend to underperform because they have on average lower exposures to aggregate default risk. Their riskiness is mostly idiosyncratic and can be diversified away.
    Keywords: Default Risk, Merton model, Default Anomaly, Idiosyncratic Risk
    JEL: G11 G12 G15 G33
    Date: 2014–02–01
  2. By: Samuel E. Vazquez
    Abstract: We revisit the problem of pricing options with historical volatility estimators. We do this in the context of a generalized GARCH model with multiple time scales and asymmetry. It is argued that the reason for the observed volatility risk premium is tail risk aversion. We parametrize such risk aversion in terms of three coefficients: convexity, skew and kurtosis risk premium. We propose that option prices under the real-world measure are not martingales, but that their drift is governed by such tail risk premia. We then derive a fair-pricing equation for options and show that the solutions can be written in terms of a stochastic volatility model in continuous time and under a martingale probability measure. This gives a precise connection between the pricing and real-world probability measures, which cannot be obtained using Girsanov Theorem. We find that the convexity risk premium, not only shifts the overall implied volatility level, but also changes its term structure. Moreover, the skew risk premium makes the skewness of the volatility smile steeper than a pure historical estimate. We derive analytical formulas for certain implied moments using the Bergomi-Guyon expansion. This allows for very fast calibrations of the models. We show examples of a particular model which can reproduce the observed SPX volatility surface using very few parameters.
    Date: 2014–02
  3. By: Sebastian Poledna; Stefan Thurner
    Abstract: Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function and collapses. Since recently it is possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (loans), liabilities, and other obligations such as derivatives. In particular it is possible to quantify the share of SR that individual nodes contribute to the overall SR in the financial system. We extend the notion of node-specific SR to individual liabilities in a financial network (liability-specific SR). We use historical, empirical data of interbank liabilities to show that a few liabilities in a nation-wide interbank network contribute to the major fraction of the overall SR. We propose a tax on individual transactions that is proportional to their contribution to overall SR. If a transaction does not increase SR it is tax free. We use a macroeconomic agent based model (CRISIS macro-financial model) with a financial economy to demonstrate that the proposed Systemic Risk Tax (SRT) leads to a self-organized re-structuring of financial networks, that are practically free of SR. This is because risk-increasing transactions will be systematically avoided when a SRT is in place. Systemic stability under a SRT emerges due to a de facto elimination of system-wide cascading failure. ABM predictions agree remarkably well with the empirical data and can be used to understand the relation of credit risk and systemic risk.
    Date: 2014–01
  4. By: Stanislav S. Borysov; Alexander V. Balatsky
    Abstract: We study historical correlation and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market representative portfolio) in the US stock market. We calculate corresponding cross-correlation functions averaged over all stocks for 71 historical stock prices from the Standard & Poor's 500 index for 1992--2013. The provided analysis suggests that cross-correlations maximum value increases near periods of crisis and remains close to 1 since the US housing bubble in 2007. Our analysis is based on the linear response theory approximation and uses asymmetries of cross-correlation function with respect to zero lag. Characteristic regimes, when changes of individual stock risks on average follow changes of the total market risk and vice versa, are observed near market crashes. Corresponding historical dynamics suggests a particular pattern: Shortly before a crash individual stock risks start to influence market risk while after the crash the situation is reversed.
    Date: 2014–01
  5. By: Silvia Figini (Department of Political and Social Sciences, University of Pavia); Mario Maggi (Department of Economics and Management, University of Pavia)
    Abstract: The performance of predictions models can be assessed using a variety of methods and metrics. Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the AUC (Area Under the ROC curve), such as the H index. It is widely recognized that AUC suffers from lack of coherency especially when ROC curves cross. On the other hand, the H index requires subjective choices. In our opinion the problem of model comparison should be more adequately handled using a different approach. The main contribution of this paper is to evaluate the performance of prediction models using proper loss function. In order to compare how our approach works with respect to classical measures employed in model comparison, we propose a simulation studies, as well as a real application on credit risk data.
    Keywords: Model Comparison, AUC, H index, Loss Function, Proper Scoring Rules, Credit Risk
    Date: 2014–01
  6. By: Kamil Yilmaz (Koc University)
    Abstract: This paper presents an analysis of the dynamic measures of volatility connectedness of major bank stocks in the US and the EU member countries. The results show that in the early stages of the US financial crisis in 2007 and 2008, the direction of the volatility connectedness was from the US banks towards the EU banks. However, once the financial crisis became global in the last quarter of 2008, volatility connectedness became bi-directional. The surge in volatility connectedness from the EU banks to the US banks in June 2011 was unprecedented, reflecting the scale of deterioration in the state of the EU banks. Finally, the within-connectedness of the US banks fluctuated throughout our sample period, while the within-connectedness of the EU banks increased steadily since 2007, a reflection of the fact that the European debt and banking crisis has not ended yet.
    Keywords: Risk measurement, systemic risk, connectedness, systemically important financial institutions, vector autoregression, variance decomposition
    JEL: C3 G2
    Date: 2014–02

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