New Economics Papers
on Risk Management
Issue of 2011‒10‒09
eight papers chosen by

  1. Macroprudential Stress Testing of Credit Risk: A Practical Approach for Policy Makers By Buncic, Daniel; Melecky, Martin
  2. A Visual Approach to Community Bank Assessment of Agricultural Portfolio Risk Exposure By Pederson, Glenn D.; Chu, Yu-Szu; Richardson, Wynn
  3. Default probability estimation in small samples - with an application to sovereign bonds By Orth, Walter
  4. A strategy-Proof Test of Portfolio Returns By Dean P. Foster; H. Peyton Young
  5. Improvements in rating models for the German corporate sector By Förstemann, Till
  6. Black swans or dragon kings? A simple test for deviations from the power law By Joanna Janczura; Rafal Weron
  7. Conditional jumps in volatility and their economic determinants By Massimiliano Caporin; Eduardo Rossi; Paolo Santucci de Magistris
  8. Volatility Forecasting: Downside Risk, Jumps and Leverage Effect By Audrino, Francesco; Hu, Yujia

  1. By: Buncic, Daniel; Melecky, Martin
    Abstract: Drawing on the lessons from the global financial crisis and especially from its impact on the banking systems of Eastern Europe, the paper proposes a new practical approach to macroprudential stress testing. The proposed approach incorporates: (i) macroeconomic stress scenarios generated from both a country specific statistical model and historical cross-country crises experience; (ii) indirect credit risk due to foreign currency exposures of unhedged borrowers; (iii) varying underwriting practices across banks and their asset classes based on their relative aggressiveness of lending; (iv) higher correlations between the probability of default and the loss given default during stress periods; (v) a negative effect of lending concentration and residual loan maturity on unexpected losses; and (vi) the use of an economic risk weighted capital adequacy ratio as the relevant outcome indicator to measure the resilience of banks to materialising credit risk. We apply the proposed approach to a set of Eastern European banks and discuss the results.
    Keywords: Supervision, Stress Test, Individual Bank Data, Eastern Europe
    JEL: G28 E58 G21
    Date: 2011–09
  2. By: Pederson, Glenn D.; Chu, Yu-Szu; Richardson, Wynn
    Keywords: Financial Economics, Risk and Uncertainty,
    Date: 2011–09
  3. By: Orth, Walter
    Abstract: In small samples and especially in the case of small true default probabilities, standard approaches to credit default probability estimation have certain drawbacks. Most importantly, standard estimators tend to underestimate the true default probability which is of course an undesirable property from the perspective of prudent risk management. As an alternative, we present an empirical Bayes approach to default probability estimation and apply the estimator to a comprehensive sample of Standard & Poor's rated sovereign bonds. We further investigate the properties of a standard estimator and the empirical Bayes estimator by means of a simulation study. We show that the empirical Bayes estimator is more conservative and more precise under realistic data generating processes.
    Keywords: Low-default portfolios; empirical Bayes; sovereign default risk; Basel II
    JEL: C41 G15 G28 C11
    Date: 2011–09–28
  4. By: Dean P. Foster; H. Peyton Young
    Abstract: Traditional methods for analyzing portfolio returns often rely on multifactor risk assessment, and tests of significance are typically based on variants of the t-test. This approach has serious limitations when analyzing the returns from dynamically traded portfolios that include derivative positions, because standard tests of significance can be ‘gamed’ using options trading strategies. To deal with this problem we propose a test that assumes nothing about the structure of returns except that they form a martingale difference. Although the test is conservative and corrects for unrealized tail risk, the loss in power is small at high levels of significance.
    Keywords: Excess returns, Martingale maximal inequality, Hypothesis test
    JEL: G32 D86
    Date: 2011
  5. By: Förstemann, Till
    Abstract: Group-specific estimations can significantly improve the predictive power of accountingbased rating models. This is shown using a binary logistic regression model applied to the Deutsche Bundesbank's USTAN dataset, which contains 300,000 financial statements provided by German companies for the years 1994 to 2002, i. e. throughout a complete business-cycle. The robustness and the representability of this result is verified through out-of-sample tests and through comparisons with a benchmark model which applies the variables of Moody's RiskCalcTM for Germany. --
    Keywords: Credit Risk,Credit Rating,Probability of Default,Logistic Regression
    JEL: G21 G33 C52
    Date: 2011
  6. By: Joanna Janczura; Rafal Weron
    Abstract: We develop a simple test for deviations from power law tails, which is based on the asymptotic properties of the empirical distribution function. We use this test to answer the question whether great natural disasters, financial crashes or electricity price spikes should be classified as dragon kings or 'only' as black swans.
    Keywords: Black swan; Dragon king; Power-law; Weibull distribution; Tail behavior; Outlier; Hypothesis test;
    JEL: C12 C16 G32
    Date: 2011
  7. By: Massimiliano Caporin (University of Padova); Eduardo Rossi (University of Pavia); Paolo Santucci de Magistris (University of Aarhus)
    Abstract: The volatility of financial returns is affected by rapid and large increments. Such movements can be hardly generated by a pure diffusive process for stochastic volatility. On the contrary jumps in volatility are important because they allow for rapid increases, like those observed during stock market crashes. We propose an extension of HAR model for estimating the presence of jumps in volatility, using the realized-range measure as a volatility proxy. By focusing on a set of 36 NYSE stocks, we show that, once that squared jumps in prices are disentangled from integrated variance, then there is a positive probability of jumps in volatility, conditional on the past information set. We then focus on the contribution of jumps during periods of financial turmoil. We analyze the dependence between the first principal component of the volatility jumps with a set of financial covariates including VIX, S&P500 volume, CDS, and Federal Fund rates. We observe that CDS captures large part of the expected jumps moves, verifying the common interpretation that large and sudden increases in volatility in stock markets over some days in the recent financial crisis have been caused by credit deterioration of US bank sector. Finally, we extend the model incorporating the credit-default swap in the dynamics of the jump size and intensity. The estimates confirm the significant contribution of the credit-default swap to the dynamics of the volatility jump size.
    Keywords: Volatility, Jumps in volatility, Realized range, HAR.
    JEL: C22 G10
    Date: 2011–09
  8. By: Audrino, Francesco; Hu, Yujia
    Abstract: We provide new empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Leverage and volatility feedback effects among continuous and jump components of the S&P500 price and volatility dynamics are examined using recently developed methodologies to detect jumps and to disentangle their size from continuous return and continuous volatility. Granted that jumps in both return and volatility are important components for generating the two effects, we find jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improve the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative then positive jump semivariance. The model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated.
    Keywords: High frequency data, Realized volatility forecasting, Downside risk, Leverage effect
    JEL: C13 C22 C51 C53
    Date: 2011–09

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