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17
Volatility, Correlation and Tails for Systemic Risk Measurement, Working Paper Series
, 2010
"... The Great Recession of 2007/2009 has motivated market participants, academics and regulators to better understand systemic risk. Regulation is now designed to reduce systemic risk. However, it is not yet clear how to measure systemic risk and in particular to determine which firms are the major cont ..."
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Cited by 74 (4 self)
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The Great Recession of 2007/2009 has motivated market participants, academics and regulators to better understand systemic risk. Regulation is now designed to reduce systemic risk. However, it is not yet clear how to measure systemic risk and in particular to determine which firms are the major contributors to the overall risk of the economy. This paper focuses on constructing measures of systemic risk based on public market data and consequently provides a quick and inexpensive approach to determining which firms deserve more careful scrutiny and regulation. The measure examined in this paper is the Marginal Expected Shortfall or MES. This is the expected loss an equity investor in a financial firm would experience if the overall market declined substantially. This measure can then be extrapolated to estimate equity losses for this firm in a future crisis and consequently the capital shortage that would be experienced as a consequence of the initial leverage. The contribution to systemic risk is then estimated as the percentage of capital shortfall that can be expected in a future crisis. MES depends upon the volatility of a firm equity price, its correlation with the market return and the comovement of the tails of the distributions. These in turn are estimated by asymmetric versions of GARCH, DCC and nonparametric tail estimators. Empirical results with 102 US financial firms find predictability in both time series and cross section and useful ranking of firms at various stages of the financial crisis.
Financial Risk Measurement for Financial Risk Management
, 2011
"... Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating bot ..."
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Cited by 11 (3 self)
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Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfoliolevel and assetlevel analysis. Assetlevel analysis is particularly challenging because the demands of realworld risk management in financial institutions – in particular, realtime risk tracking in very highdimensional situations – impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also crossfertilize the academic and practitioner communities, promoting improved market risk measurement
A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns
"... Abstract. We compare more than 1000 different volatility models in terms of their fit to the historical ISE100 Index data and their forecasting performance of the conditional variance in an outofsample setting. Exponential GARCH model of Nelson (1991) with “constant mean, tdistribution, one lag ..."
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Cited by 1 (0 self)
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Abstract. We compare more than 1000 different volatility models in terms of their fit to the historical ISE100 Index data and their forecasting performance of the conditional variance in an outofsample setting. Exponential GARCH model of Nelson (1991) with “constant mean, tdistribution, one lag moving average term” specification achieves the best overall performance for modeling the ISE100 return volatility. The tdistribution seems to characterize the distribution of the heavy tailed returns better than the Gaussian distribution or the generalized error distribution. In terms of forecasting performance, the best models are the ones that can accommodate a leverage effect. Results from fitting the selected exponential GARCH model to the historical ISE100 return data indicates that the return volatility reacts to bad news 24 % more than they react to good news as a result of a one standard deviation shock to the returns. As the magnitude of shock increases, the asymmetry becomes larger.
Discriminating between GARCH and Stochastic Volatility via nonnested hypotheses testingI
, 2013
"... Discriminating between GARCH and stochastic volatility via nonnested hypotheses testing D iscussion P aper ..."
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Discriminating between GARCH and stochastic volatility via nonnested hypotheses testing D iscussion P aper
the impact of model risk on capital reserves: A quantitative analysis, Discussion Paper,
, 2011
"... About the impact of model risk on capital reserves: A ..."
An Investigation of Some Hedging Strategies for Crude Oil Market
"... ABSTRACT: This paper examines the performance of bivariate volatility models for the crude oil spot and future returns of the WTI type barrel prices. Besides the volatility of spot and future crude oil barrel returns time series, the hedge ratio strategy is examined through the hedge effectiveness. ..."
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ABSTRACT: This paper examines the performance of bivariate volatility models for the crude oil spot and future returns of the WTI type barrel prices. Besides the volatility of spot and future crude oil barrel returns time series, the hedge ratio strategy is examined through the hedge effectiveness. Thus this study shows hedge strategies built using methodologies applied in the variance modelling of returns of crude oil prices in the spot and future markets, and covariance between these two market returns, which correspond to the inputs of the hedge strategy shown in this work. From the studied models the bivariate GARCH in a Diagonal VECH and BEKK representations was chosen, using three different models for the mean: a bivariate autoregressive, a vector autoregressive and a vector error correction. The methodologies used here take into consideration the denial of assumptions of homoscedasticity and normality for the return distributions making them more realistic.
Method of moments estimation of GOGARCH models Method of Moments Estimation of GOGARCH Models
"... Abstract We propose a new estimation method for the factor loading matrix in generalized orthogonal GARCH (GOGARCH) models. The method is based on the eigenvectors of a suitably defined sample autocorrelation matrix of squares and crossproducts of the process. The method can therefore be easily a ..."
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Abstract We propose a new estimation method for the factor loading matrix in generalized orthogonal GARCH (GOGARCH) models. The method is based on the eigenvectors of a suitably defined sample autocorrelation matrix of squares and crossproducts of the process. The method can therefore be easily applied to highdimensional systems, where likelihoodbased estimation will run into computational problems. We provide conditions for consistency of the estimator, and study its efficiency relative to maximum likelihood estimation using Monte Carlo simulations. The method is applied to European sector returns, and to the correlation between oil and kerosene returns and airline stock returns.
A Service of zbw LeibnizInformationszentrum Wirtschaft Leibniz Information Centre for Economics Dimensions of macroeconomic uncertainty: A common factor analysis Dimensions of macroeconomic uncertainty: A common factor analysis*
"... StandardNutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, ..."
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StandardNutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter OpenContentLizenzen (insbesondere CCLizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Abstract In the current literature uncertainty about the future course of the economy is identified as a possible driver of business cycle fluctuations. In fact, uncertainty surrounds the movements of all economic variables which gives rise to a monitoring problem. We identify the different dimensions of uncertainty in the macroeconomy. To this end, we construct a large dataset covering all forms of economic uncertainty and unravel the fundamental factors that account for the common dynamics therein. These common factors are interpreted as macroeconomic uncertainty. Our results show that the first factor captures business cycle uncertainty while the second factor is identified as oil and commodity price uncertainty. Finally, we demonstrate that a distinction between both types of macroeconomic uncertainty is essential since they have rather different implications for economic activity. JEL Code: C32, C38, E32.
DEPENDENCE OF REAL ESTATE AND EQUITY MARKETS IN CHINA WITH THE APPLICATION OF COPULA
, 2015
"... ABSTRACT Contribution/ Originality This study is one of very few studies which have investigated the dependence structure between real estate and equity markets in both Shanghai Exchange and Shenzhen Exchange with the application of the copula. Moreover, we illustrate the extreme comovement effect ..."
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ABSTRACT Contribution/ Originality This study is one of very few studies which have investigated the dependence structure between real estate and equity markets in both Shanghai Exchange and Shenzhen Exchange with the application of the copula. Moreover, we illustrate the extreme comovement effect between China's real estate and equity markets.
Surface Myoelectric Signal Classification Using the ARGARCH Model
"... Abstract In myoelectric prostheses design, it is normally assumed that the necessary control information can be extracted from the surface myoelectric signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally con ..."
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Abstract In myoelectric prostheses design, it is normally assumed that the necessary control information can be extracted from the surface myoelectric signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally considered an efficient and robust feature set. However, no formal statistical methodologies or tests are reported in the literature to analyze and model the myoelectric signal as an AR process. We analyzed the myoelectric signal as a stochastic timeseries and found that the signal is heteroscedastic, i.e., the AR modeling residuals exhibit a timevarying variance. Heteroscedasticity is a major concern in statistical modeling because it can invalidate statistical tests of significance which may assume that the modeling errors are uncorrelated and that the error variances do not vary with the effects being modeled. We subsequently proposed to model the myoelectric signal as an AutoregressiveGeneralized Autoregressive Conditional Heteroscedastic (ARGARCH) process and used the model parameters as a feature set for signal classification. Multiple statistical tests including the LjungBox Qtest, Engle's test for heteroscedasticity, KolmogorovSmirnov test and the goodness of fit test were performed to show the validity of the proposed model. Our experimental results show that the proposed ARGARCH model coefficients, when used as a feature set in two different classification schemes, significantly outperformed (p < .01) the conventional AR model coefficients.