Central bank research hub - Papers by Michael B. Gordy
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Research hub papers by author Michael B. GordyenSpectral Backtests of Forecast Distributions with Application to Risk Management
https://www.federalreserve.gov/econres/feds/files/2018021pap.pdf
Board of Governors of the Federal Reserve System Finance and Economics Discussion Series by Michael B. Gordy and Alexander J. McNeilSpectral Backtests of Forecast Distributions with Application to Risk Management2018-03-23T00:00:21ZWe 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. In an empirical application, we backtest forecast distributions for the overnight P of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.Spectral Backtests of Forecast Distributions with Application to Risk ManagementFull texthttps://www.federalreserve.gov/econres/feds/files/2018021pap.pdfAlexander J. McNeilMichael B. GordyMichael B. Gordy and Alexander J. McNeil2018-03-23Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC52G21G28G32Counterparty Risk and Counterparty Choice in the Credit Default Swap Market
http://www.federalreserve.gov/econresdata/feds/2016/files/2016087pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Wenxin Du, Salil Gadgil, Michael B. Gordy, and Clara VegaCounterparty Risk and Counterparty Choice in the Credit Default Swap Market2016-10-10T10:40:59ZWe investigate how market participants price and manage counterparty risk in the post-crisis period using confidential trade repository data on single-name credit default swap (CDS) transactions. We find that counterparty risk has a modest impact on the pricing of CDS contracts, but a large impact on the choice of counterparties. We show that market participants are significantly less likely to trade with counterparties whose credit risk is highly correlated with the credit risk of the reference entities and with counterparties whose credit quality is relatively low. Furthermore, we examine the impact of central clearing on CDS pricing. Contrary to the previous literature, but consistent with our main findings on pricing, we find no evidence that central clearing increases transaction spreads.Counterparty Risk and Counterparty Choice in the Credit Default Swap MarketFull texthttp://www.federalreserve.gov/econresdata/feds/2016/files/2016087pap.pdfSalil GadgilWenxin DuMichael B. GordyClara VegaWenxin Du, Salil Gadgil, Michael B. Gordy, and Clara Vega2016-11Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesG12G13G24The Bank as Grim Reaper: Debt Composition and Bankruptcy Thresholds
http://www.federalreserve.gov/econresdata/feds/2016/files/2016069pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Mark Carey and Michael B. GordyThe Bank as Grim Reaper: Debt Composition and Bankruptcy Thresholds2016-08-23T06:23:00ZWe offer a model and evidence that private debtholders play a key role in setting the endogenous asset value threshold below which corporations declare bankruptcy. The model, in the spirit of Black and Cox (1976), implies that the recovery rate at emergence from bankruptcy on all of the firm's debt taken together is increasing in the pre-bankruptcy share of private debt in all debt. Empirical evidence supports this and other implications of the model. Indeed, debt composition has a more economically material empirical influence on recovery than all other variables we try taken together.The Bank as Grim Reaper: Debt Composition and Bankruptcy ThresholdsFull texthttp://www.federalreserve.gov/econresdata/feds/2016/files/2016069pap.pdfMark CareyMichael B. GordyMark Carey and Michael B. Gordy2016-08-23Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesG12G32G33Bayesian Estimation of Time-Changed Default Intensity Models
http://www.federalreserve.gov/econresdata/feds/2015/files/2015002pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. Gordy and Pawel J. SzerszenBayesian Estimation of Time-Changed Default Intensity Models2015-04-13T12:33:59ZGordy and Pawel J. Szerszen. We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on firm-level time-series of CDS spreads, and find strong in-sample evidence of stochastic volatility in this market. Relative to the widely-used CIR model for the default intensity, we find that stochastic time-change offers modest benefit in fitting the cross-section of CDS spreads at each point in time, but very large improvements in fitting the time-series, i.e., in bringing agreement between the moments of the default intensity and the model-implied moments. Finally, we obtain model-implied out-of-sample density forecasts via auxiliary particle filter, and find that the time-changed model strongly outperforms the baseline CIR model.Bayesian Estimation of Time-Changed Default Intensity ModelsAbstracthttp://www.federalreserve.gov/econresdata/feds/2015/index.htm#2015002Full texthttp://www.federalreserve.gov/econresdata/feds/2015/files/2015002pap.pdfPawel J. SzerszenMichael B. GordyMichael B. Gordy and Pawel J. Szerszen2015-02-02Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC11C15C58G12G17Granularity Adjustment for Regulatory Capital Assessment
http://www.ijcb.org//www.ijcb.org/journal/ijcb13q3a2.pdf
IJCB International Journal of Central Banking by Michael B. Gordy and Eva LtkebohmertGranularity Adjustment for Regulatory Capital Assessment2013-09-03T12:35:59ZThe credit value-at-risk model underpinning the internalGranularity Adjustment for Regulatory Capital AssessmentAbstracthttp://www.ijcb.org/journal/ijcb13q3a2.htmFull texthttp://www.ijcb.org//www.ijcb.org/journal/ijcb13q3a2.pdfMichael B. GordyEva LtkebohmertMichael B. Gordy and Eva Ltkebohmert2013-09IJCB International Journal of Central BankingExpectations of functions of stochastic time with application to credit risk modeling
http://www.federalreserve.gov/pubs/feds/2013/201314/201314pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Ovidiu Costin, Michael B. Gordy, Min Huang, and Pawel J. SzerszenExpectations of functions of stochastic time with application to credit risk modeling2013-03-26T06:21:59ZOvidiu Costin, Michael B. Gordy, Min Huang, and Pawel J. Szerszen. We develop two novel approaches to solving for the Laplace transform of a time-changed stochastic process. We discard the standard assumption that the background process is Levy. Maintaining the assumption that the business clock and the background process are independent, we develop two different series solutions for the Laplace transform of the time-changed process. In fact, our methods apply not only to Laplace transforms, but more generically to expectations of smooth functions of random time. We apply the methods to introduce stochastic time change to the standard class of default intensity models of credit risk, and show that stochastic time-change has a very large effect on the pricing of deep out-of-the-money options on credit default swaps.Expectations of functions of stochastic time with application to credit risk modelingAbstracthttp://www.federalreserve.gov/pubs/feds/2013/201314/201314abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2013/201314/201314pap.pdfMin HuangPawel J. SzerszenOvidiu CostinMichael B. GordyOvidiu Costin, Michael B. Gordy, Min Huang, and Pawel J. Szerszen2013-03-25Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesOn the distribution of a discrete sample path of a square-root diffusion
http://www.federalreserve.gov/pubs/feds/2012/201212/201212pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. GordyOn the distribution of a discrete sample path of a square-root diffusion2012-05-01T06:23:59ZWe derive the multivariate moment generating function (mgf) for the stationary distribution of a discrete sample path of n observations of a square-root diffusion (CIR) process, X(t). The form of the mgf establishes that the stationary joint distribution of (X(t(1)),...,X(t(n))) for any fixed vector of observation times (t(1),...,t(n)) is a Krishnamoorthy-Parthasarathy multivariate gamma distribution. As a corollary, we obtain the mgf for the increment X(t+dt)-X(t), and show that the increment is equivalent in distribution to a scaled difference of two independent draws from a gamma distribution. Simple closed-form solutions for the moments of the increments are given.On the distribution of a discrete sample path of a square-root diffusionAbstracthttp://www.federalreserve.gov/pubs/feds/2012/201212/201212abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2012/201212/201212pap.pdfMichael B. GordyMichael B. Gordy2012-04-27Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesGranularity Adjustment for Mark-to-Market Credit Risk Models
http://www.federalreserve.gov/pubs/feds/2010/201037/201037pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. Gordy and James MarroneGranularity Adjustment for Mark-to-Market Credit Risk Models2010-06-22T06:29:59ZMichael B. Gordy and James Marrone. The impact of undiversified idiosyncratic risk on value-at-risk and expected shortfall can be approximated analytically via a methodology known as granularity adjustment (GA). In principle, the GA methodology can be applied to any risk-factor model of portfolio risk. Thus far, however, analytical results have been derived only for simple models of actuarial loss, i.e., credit loss due to default. We demonstrate that the GA is entirely tractable for single-factor versions of a large class of models that includes all the commonly used mark-to-market approaches. Our approach covers both finite ratings-based models and models with a continuum of obligor states. We apply our methodology to CreditMetrics and KMV Portfolio Manager, as these are benchmark models for the finite and continuous classes, respectively. Comparative statics of the GA with respect to model parameters in CreditMetrics reveal striking and counterintuitive patterns. We explain these relationships with a stylized model of portfolio riskGranularity Adjustment for Mark-to-Market Credit Risk ModelsAbstracthttp://www.federalreserve.gov/pubs/feds/2010/201037/201037abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2010/201037/201037pap.pdfJames MarroneMichael B. GordyMichael B. Gordy and James Marrone2010-06-21Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesConstant Proportion Debt Obligations: A Post-Mortem Analysis of Rating Models
http://www.federalreserve.gov/pubs/feds/2010/201005/201005.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. Gordy and Soren WillemannConstant Proportion Debt Obligations: A Post-Mortem Analysis of Rating Models2010-03-29T17:42:00ZMichael B. Gordy and Soren Willemann. In its complexity and its vulnerability to market volatility, the CPDO might be viewed as the poster child for the excesses of financial engineering in the credit market. This paper examines the CPDO as a case study in model risk in the rating of complex structured products. We demonstrate that the models used by S&P and Moody's would have assigned very low probability to the spread levels realized in the investment grade corporate credit default swap market in late 2007, even though these spread levels were comparable to those of 2002. The spread levels realized in the first quarter of 2008 would have been assigned negligibly small probabilities. Had the models put non-negligible likelihood on attaining these high spread levels, the CPDO notes could never have achieved investment grade status. We conclude with larger lessons for the rating of complex products and for modeling credit risk in general.Constant Proportion Debt Obligations: A Post-Mortem Analysis of Rating ModelsAbstracthttp://www.federalreserve.gov/pubs/feds/2010/201005/201005abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2010/201005/201005.pdfMichael B. GordySoren WillemannMichael B. Gordy and Soren Willemann2010-02-04Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesNested Simulation in Portfolio Risk Measurement
http://www.federalreserve.gov/pubs/feds/2008/200821/200821pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. Gordy and Sandeep JunejaNested Simulation in Portfolio Risk Measurement2008-04-30T17:38:00ZRisk measurement for derivative portfolios almost invariably calls for nested simulation. In the outer step one draws realizations of all risk factors up to the horizon, and in the inner step one re-prices each instrument in the portfolio at the horizon conditional on the drawn risk factors. Practitioners may perceive the computational burden of such nested schemes to be unacceptable, and adopt a variety of second-best pricing techniques to avoid the inner simulation. In this paper, we question whether such short cuts are necessary. We show that a relatively small number of trials in the inner step can yield accurate estimates, and analyze how a fixed computational budget may be allocated to the inner and the outer step to minimize the mean square error of the resultant estimator. Finally, we introduce a jackknife procedure for bias reduction and a dynamic allocation scheme for improved efficiency.Nested Simulation in Portfolio Risk MeasurementAbstracthttp://www.federalreserve.gov/pubs/feds/2008/200821/200821abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2008/200821/200821pap.pdfMichael B. GordySandeep JunejaMichael B. Gordy and Sandeep Juneja2008-04Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC15G32Granularity adjustment for Basel II
http://www.bundesbank.de/download/bankenaufsicht/dkp/200701dkp_b.pdf
Deutsche Bundesbank Banking Supervision Discussion Papers by Michael B. Gordy, Eva LütkebohmertGranularity adjustment for Basel II2007-08-02T12:35:00ZGranularity adjustment for Basel IIFull texthttp://www.bundesbank.de/download/bankenaufsicht/dkp/200701dkp_b.pdfMichael B. GordyEva LütkebohmertMichael B. Gordy, Eva Lütkebohmert2007-02Deutsche Bundesbank Banking Supervision Discussion PapersSwitching Costs and Adverse Selection in the Market for Credit Cards: New Evidence
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2005/wp05-16.pdf
Philadelphia Fed Working Papers by Paul S. Calem, Michael B. Gordy, and Loretta J. MesterSwitching Costs and Adverse Selection in the Market for Credit Cards: New Evidence2005-07-01T12:00:00ZSwitching Costs and Adverse Selection in the Market for Credit Cards: New EvidenceFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2005/wp05-16.pdfLoretta J. MesterMichael B. GordyPaul S. CalemPaul S. Calem, Michael B. Gordy, and Loretta J. Mester2005Federal Reserve Bank of Philadelphia Working PapersA Risk-Factor Model Foundation for Ratings-Based Bank Capital Rules
http://www.federalreserve.gov/pubs/feds/2002/200255/200255pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Michael B. GordyA Risk-Factor Model Foundation for Ratings-Based Bank Capital Rules2003-02-05T09:48:00ZA Risk-Factor Model Foundation for Ratings-Based Bank Capital RulesAbstracthttp://www.federalreserve.gov/pubs/feds/2002/200255/200255abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2002/200255/200255pap.pdfMichael B. GordyMichael B. Gordy2002-11Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesG31G38