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Social Sciences
Economics
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Social Sciences
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Credit Valuation Adjustment (CVA) Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
24.10.2020
Co-author:
Abstract:
Credit valuation adjustment (CVA) is the market price of counterparty credit risk that has become a central part of counterparty credit risk management. By definition, CVA is the difference between the risk-free portfolio value and the true/risky portfolio value. In practice, CVA should be computed at portfolio level. That means calculation should take Master agreement and CSA agreement into account.
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Credit Risk Simulation Methodology
Social Sciences (Economics)
Tim Xiao
Date of upload:
24.10.2020
Co-author:
Abstract:
Counterparty credit risk (CCR) is the risk of loss that will be incurred in the event of default by a counterparty. It will be incurred in the event of default by a counterparty. Only over-the-counter (OTC) derivatives and financial security transactions (e.g., repo) are subject to counterparty risk. If one party of a contract defaults, the non-defaulting party will find a similar contract with another counterparty in the market to replace the default one. That is why counterparty credit risk sometimes is referred as replacement risk. The replacement risk is the MTM value of a counterparty portfolio at the time of the counterparty default.
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Incremental Risk Charge (IRC) Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
17.01.2021
Co-author:
Abstract:
The incremental risk charge (IRC) is a regulatory requirement from the Basel Committee in response to the financial crisis. It supplements existing Value-at-Risk (VaR) and captures the loss due to default and migration events at a 99.9% confidence level over a one-year capital horizon.
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Counterparty Credit Risk Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
24.10.2020
Co-author:
Abstract:
Counterparty credit risk (CCR) refers to the risk that a counterparty to a bilateral financial derivative contract may fail to fulfill its contractual obligation causing financial loss to the non-defaulting party. It will be incurred in the event of default by a counterparty.
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Financial Market Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
17.01.2021
Co-author:
Abstract:
A financial market is a market where people trade financial products. Typical financial markets are the fixed income and interest rate market, the currency market, the equity market, the commodity market and the credit market.
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Standard Initial Margin Model
Social Sciences (Economics)
Tim Xiao
Date of upload:
11.01.2021
Co-author:
Abstract:
Initial Margin (IM) is the amount of collateral required to open a position with a broker or an exchange or a bank. The Standard Initial Margin Model (SIMM) is very likely to become the market standard. It is designed to provide a common methodology for calculating initial margin for uncleared OTC derivatives. Initial margin calculation is counterparty-portfolio-based. Given this standardized approach, counterparties can easily reconcile the results.
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Parametric Value At Risk
Social Sciences (Economics)
Tim Xiao
Date of upload:
24.01.2021
Co-author:
Abstract:
Value at Risk (VaR) is the regulatory measurement for assessing market risk. It reports the maximum likely loss on a portfolio for a given probability defined as x% confidence level over N days. VaR is vital in market risk management and control. Also regulatory and economic capital computation is based on VaR results. Although VaR measure is objective and intuitive, it doesn’t capture tail risk. There are three commonly used methodologies to calculate VaR – parametric, historical simulation and Monte Carlo simulation. This presentation focuses on parametric VaR.
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Historical Value At Risk Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
11.01.2021
Co-author:
Abstract:
Value at Risk (VaR) is the regulatory measurement for assessing market risk. It reports the maximum likely loss on a portfolio for a given probability defined as x% confidence level over N days. VaR is vital in market risk management and control. Also regulatory and economic capital computation is based on VaR results. Although VaR measure is objective and intuitive, it doesn’t capture tail risk. There are three commonly used methodologies to calculate VaR – parametric, historical simulation and Monte Carlo simulation. This presentation focuses on historical VaR.
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Market Risk Economic Capital Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
17.01.2021
Co-author:
Abstract:
Financial business is exposed to many types of risks due to the nature of business. To guard against the risk, financial institutions must hold capital in proportion to the potential risk. Market risk economic capital is intended to capture the value change due to changes in market risk factors. It is an internal capital reserve to cover unexpected loss due to market movement.
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Funding Valuation Adjustment Overview
Social Sciences (Economics)
Tim Xiao
Date of upload:
10.01.2021
Co-author:
Abstract:
Funding Valuation Adjustment (FVA) is introduced to capture the incremental costs of funding uncollateralized derivatives. It can be referred to as the difference between the rate paid for the collateral to the bank’s treasury and rate paid by the clearinghouse. Also FVA can be thought of as a hedging cost or benefit arising from the mismatch between an uncollateralized client trade and a collateralized hedge in the interdealer market. FVA should be also calculated at portfolio level.
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FRTB Standardized Approach
Social Sciences (Economics)
Tim Xiao
Date of upload:
10.01.2021
Co-author:
Abstract:
The Fundamental Review of the Trading Book (FRTB) is a new Basel committee framework for the next generation market risk regulatory capital rules. It is inspired by the undercapitalisation of trading book exposures witnessed during the financial crisis. FRTB aims to address shortcoming of the current Basel 2.5 market risk capital framework.
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Monte Carlo Value At Risk Introduction
Social Sciences (Economics)
Tim Xiao
Date of upload:
24.01.2021
Co-author:
Abstract:
Value at Risk (VaR) is the regulatory measurement for assessing market risk. It reports the maximum likely loss on a portfolio for a given probability defined as x% confidence level over N days. VaR is vital in market risk management and control. Also regulatory and economic capital computation is based on VaR results. Although VaR measure is objective and intuitive, it doesn’t capture tail risk. There are three commonly used methodologies to calculate VaR – parametric, historical simulation and Monte Carlo simulation. This presentation focuses on Monte Carlo VaR.
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