The Pricing of Correlated Default Risk: Evidence from the Credit Derivatives Market

Tarashev, Nikola; Zhu, Hao

doi:10.2139/ssrn.967330

Cited by 19 publications

(29 citation statements)

References 29 publications

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“…The CDS market has grown rapidly in recent years, and the CDS spread is considered to be a superior measure of credit risk to bond spreads (see Longstaff et al, 2005;Blanco et al, 2005;Zhu, 2006;Forte and Peña, 2009, for example) or loan spreads (see Norden and Wagner, 2008). Following Duffie (1999) and Tarashev and Zhu (2008a), it is straightforward to derive the risk-neutral PD from the observed CDS spread ðs i;t Þ:…”

Section: Estimating Risk-neutral Pdsmentioning

confidence: 99%

A framework for assessing the systemic risk of major financial institutions

Huang

Zhou

Zhu³

2009

Journal of Banking & Finance

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547

311

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a b s t r a c tIn this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro-macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.

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Section: Estimating Risk-neutral Pdsmentioning

confidence: 99%

A framework for assessing the systemic risk of major financial institutions

Huang

Zhou

Zhu³

2009

Journal of Banking & Finance

Self Cite

547

311

View full text Add to dashboard Cite

show abstract

“…10 Our analysis could be extended by randomizing recovery rates. However, evidence in Tarashev and Zhu (2008) suggests that the added complexity may not add much additional insight in terms of results; they indicate, based on data collected from Markit for 136 entities, that the recovery rate market participants expect varies in a narrow range around 40 percent for daily data from late 2003 to early 2005.…”

Section: Datamentioning

confidence: 99%

“…We do this using a copula (Li, 2000) implemented by Monte Carlo simulation. Our approach has much in common with measures of systemic risk and stress indicators for banks (Tarashev and Zhu, 2008;Huang et al, 2009;Segoviano and Goodhart, 2009). …”

Section: Introductionmentioning

confidence: 99%

Assessing Credit Risk in Money Market Fund Portfolios

Collins

Gallagher

2014

SSRN Journal

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This paper measures credit risk in prime money market funds (MMFs), studies how such credit risk evolved in 2011-2012, and tests the efficacy of the Securities and Exchange Commission's (SEC) January 2010 reforms. To accomplish this, we estimate the credit default swap premium (CDS) needed to insure each fund's portfolio against credit losses. We also calculate by Monte Carlo the cost of insuring a fund against losses amounting to over 50 basis points. We find that credit risk of prime MMFs rose from June to December 2011 before receding in 2012. Contrary to common perceptions, this did not primarily reflect funds' credit exposure to eurozone banks. Instead, credit risk in prime MMFs rose because of the deteriorating credit outlook of banks in the Asia/Pacific region. Finally, we find evidence that the SEC's 2010 liquidity and weighted average life (WAL) requirements reduced the credit risk of prime MMFs. JEL: G01, G18, G23, G28

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“…Based on these observations, recent finance literature has drawn more attention to the correlation of credit risk and on credit risk contagion (see e.g. Tarashev and Zhu (2008)). One possibility to account for cross-sectional spillover effects in a statistical model is to include spatial lags following Cliff and Ord (1973).…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation and Inference with Spatial Lags and Cointegration

Mutl

Sögner

2013

SSRN Journal

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We study dynamic panel data models where the long run outcome for a particular crosssection is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator and investigate its small sample distribution in a simulation study. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, firm specific and industry data. A "closeness" measure for firms is based on inputoutput matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.

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The Pricing of Correlated Default Risk: Evidence from the Credit Derivatives Market

Cited by 19 publications

References 29 publications

A framework for assessing the systemic risk of major financial institutions

A framework for assessing the systemic risk of major financial institutions

Assessing Credit Risk in Money Market Fund Portfolios

Parameter Estimation and Inference with Spatial Lags and Cointegration

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