Valuing the Futures Market Clearinghouse's Default Exposure during the 1987 Crash

Bates, David S.; Craine, Roger

doi:10.2307/2601232

Cited by 34 publications

(20 citation statements)

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“…The second measure for the comparison of distinct clearing arrangements is the loss‐concentration ratio, which is defined as a proportion of a maximal replacement‐cost loss incurred by a single agent i to total replacement losses suffered by all agents. Replacement‐cost loss

L_{i}

will only arise when a counterparty default coincides with an adverse price move in excess of the per unit margin collected from that counterparty (Bates and Crain ), for the agent i and initial matrix:

L_{i} = \sum_{j = 1}^{s} D_{j} trueprefixmax ([], 0, ((), X_{i, j, l} Δ p^{l} - | X_{i, j, l} | m_{j}^{i})) .

…”

Section: Simulation Modelmentioning

confidence: 99%

OTC Clearing Arrangements for Bank Systemic Risk Regulation: A Simulation Approach

Lewandowska¹

2015

J of Money Credit Banking

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Based on a simulation approach, this paper compares different over‐the‐counter (OTC) clearing models regarding their netting efficiency and loss‐concentration implications. The results indicate that the mandatory clearing of all standardized OTC derivatives by a Central Counterparty (CCP) propagated by regulators would significantly decrease systemic risk as compared to existing clearing arrangements only if the multilateral netting benefits of the CCP and loss mutualization are fully attained. Therefore, regulators have to ensure that there is a critical mass of asset classes under mandatory clearing, that broad market participation in the CCP is enabled, and that there is an appropriate client asset protection regime.

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L_{i}

L_{i} = \sum_{j = 1}^{s} D_{j} trueprefixmax ([], 0, ((), X_{i, j, l} Δ p^{l} - | X_{i, j, l} | m_{j}^{i})) .

…”

Section: Simulation Modelmentioning

confidence: 99%

OTC Clearing Arrangements for Bank Systemic Risk Regulation: A Simulation Approach

Lewandowska¹

2015

J of Money Credit Banking

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“…Some time series evidence for the specification was provided in Bates and Craine (1999), while Eraker et al (2000) found stronger empirical support. In contrast to the results in ABL, the final two columns of Table 2 indicate that jumps are indeed more likely when volatility is high.…”

Section: Estimates From Stock Index Returnsmentioning

confidence: 99%

Maximum Likelihood Estimation of Latent Affine Processes

Bates

2003

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“…We explore dependence in the arrival process governing jump events in a discrete‐time setting and extend the work of Jorion (1988) and Vlaar and Palm (1993), among others 7 Bates and Craine (1999). allow a volatility factor to drive the intensity.…”

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confidence: 96%

News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns

Maheu

McCurdy

2004

The Journal of Finance

456

204

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This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time‐series dynamics of jump clustering.

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Valuing the Futures Market Clearinghouse's Default Exposure during the 1987 Crash

Cited by 34 publications

References 0 publications

OTC Clearing Arrangements for Bank Systemic Risk Regulation: A Simulation Approach

OTC Clearing Arrangements for Bank Systemic Risk Regulation: A Simulation Approach

Maximum Likelihood Estimation of Latent Affine Processes

News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns

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