Measuring Counterparty Credit Exposure to a Margined Counterparty

Gibson, Michael S.

doi:10.17016/feds.2005.50

Cited by 5 publications

(2 citation statements)

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“…The effect of threshold and margin period has been examined by Gibson [12] in a simplified setup, and by Ballotta et al [3] in a more general structural model with jumps. The main insight is that collateralization is not able to fully eliminate counterparty risk, especially in presence of market shocks.…”

Section: Example 13 Let Us Considermentioning

confidence: 99%

A Gentle Introduction to Default Risk and Counterparty Credit Modelling

2016

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Section: Example 13 Let Us Considermentioning

confidence: 99%

A Gentle Introduction to Default Risk and Counterparty Credit Modelling

2016

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“…A challenge for building an advanced approach is the incorporation of collateral [14], which is straightforward in the SA-CCR. For each calculation of exposure max(PV(t) − C(t); 0) per market data scenario and time step, the expected value of the collateral C(t) has to be estimated.…”

Section: Advanced Approach For Calculating Ead For Cvamentioning

confidence: 99%

CVA for Pricing: Comparison of CEM, SA-CCR, and Advanced Approach

Marquart¹

2016

Wilmott

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Three different methods for calculating exposure at default for credit valuation adjustments (CVA) are described: the new regulatory standard approach for counterparty credit risk (SA‐CCR), the widely used current exposure method (CEM), and an advanced approach are introduced and compared regarding their quantitative impact on swap pricing. The paper also gives an overview on differences between the regulatory approach for calculating exposure at default for CVA capital requirements and the best practice approach for estimating international financial reporting standards accounting fair value adjustments.

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The Household Economy Approach. Managing the impact of climate change on poverty and food security in developing countries

Seaman

Sawdon²,

Acidri³

et al. 2014

Climate Risk Management

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a b s t r a c tClimate change is expected to have severe effects on the populations of developing countries because many of these depend heavily on agriculture for income, have large impoverished rural populations which rely on agriculture for subsistence, and are financially and technically least equipped to adapt to changing conditions. Planning to target measures to support adaptation to reduce the impact of climate change on poverty and food insecurity requires methods of identifying vulnerable households. This paper describes an established approach to vulnerability assessment, the 'Household Economy Approach' (HEA) and its potential application to the management of climate change in developing countries. The HEA is widely used by Governments and others, chiefly in Africa, for the assessment of household vulnerability to poverty and food security. HEA uses a model based on Amartya Sen's entitlement theory and detailed social and economic data to simulate the impact of weather related, price, policy and other shocks on household income and food access, to provide information for decision making. In developing countries climate change will be experienced in terms of increased climate variability and an increased frequency of extreme events. HEA provides a way of managing the effects of year to year shocks to prevent impoverishment and the erosion of household resilience. It also provides the information needed to develop scenarios to support the design of policies to support longer term adaptation. HEA data has already been collected for large areas of Africa.

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Measuring Counterparty Credit Exposure to a Margined Counterparty

Cited by 5 publications

References 1 publication

A Gentle Introduction to Default Risk and Counterparty Credit Modelling

A Gentle Introduction to Default Risk and Counterparty Credit Modelling

CVA for Pricing: Comparison of CEM, SA-CCR, and Advanced Approach

The Household Economy Approach. Managing the impact of climate change on poverty and food security in developing countries

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