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Contents:
  1. Rethinking the margin period of risk
  2. IBM Algo One V delivers an integrated approach to financial risk management
  3. Browse more videos

Wide minimum amounts reduces chances of rehypothecated collaterals being called back or substituted with alternatives but increases the likelihood of undercollaterised outstanding exposures. Most of the time, CVA desk hedge trades are collaterised.

By contrast, the uncollateralised - MTM creates a funding benefit i. Defining the funding spread to use in the FVA formula is difficult. Derivatives, due to their dynamic nature and the funding approach of banks, are not term-funded. Traditionally, the funding has been generally considered to be short-term, but regulation e. Overall, this means that a party defining their appropriate funding curve represents a difficult and subjective problem. The first is that funding costs represent credit risk and yet credit risk has already been priced in via CVA.

The second is that the funding cost applied to a derivative transaction should be the incremental one and not the average funding cost of the party in question. Indeed, this is why many assets have no funding costs, since they can be effectively self funded via repo. But the quality of a derivative portfolio is already priced in with the CVA. One important idea is that only the funding liquidity risk premium should be priced into FVA. This is the result of Morini and Prampolini , who state that this liquidity spread or equivalently the CDS—bond basis contributes as a net funding cost to the value of a transaction.

CVA is a risk-neutral conditional expectation actualized losses.

Derivatives (finance)

Risk neutral as it uses current market pricing ProbDefault which discounts for risk premium expectations, conditional as it assumes counterparty defaults at time t, actualised loss with positive expected exposure at time t, ceteris paribus. This meant measure introduces the joint distribution of market factors and the credit factors that drives the potential default of the counterparty.

CVA is not an addictive measure like VaR due to possible netting benefits at portfolio level from the new trade and the existing trades. That said, the sequence of trade execution would optimise the subsequent CVA charge. A favorable sequence would avoid stacking of correlated trades back-to-back. Full simulation-based pricing incorporates all aspects netting, collateral and portfolio effects can only be done accurately with simulation-based approaches that can run an entire group of transactions typically at the counterparty level but potentially at the portfolio level. Practically, this requires a simulation engine that can generate all relevant market variables and compute values of the current transactions and the new transaction in all required scenarios through time.

This is a common requirement for CVA desks to understand the performance of their hedging and the source of any material unhedged moves. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Skip to content. The role of CVA That said, CVA provides the reserve the bank needs to save itself when either one of its large counterparty runs off before footing the bill.

If Yes, use derived credit spread with basis adjustment. Otherwise, is there a single name proxy? Is yes, use proxy possibly with some credit-spread adjustment. Otherwise, map to generic proxy.

Example decision tree in order to map a given counterparty credit spread. Not all clients need CVA charges The handful of black sheeps are typically clients with any OTC derivatives that are long dated, of poor quality and under- or uncollateralised. The dotted lines represent thresholds in the collateral agreement for each party. The origin of funding costs and benefits. A bank trades with a client with no collateral arrangement and hedges with collateralised transactions. Defining the funding spread to use in the FVA formula is difficult Derivatives, due to their dynamic nature and the funding approach of banks, are not term-funded.

One european bank acting as issuer of CDS for another european bank such that insurer ability to insure weakens when the covered company defaults which tends to make the basis negative. Bonds that can be borrowed for short lending are typically sourced from a liquid and short-dated repo market. They usually trade special as they are highly demanded and widely eligible across legal legislation hence making the basis positive.

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Rethinking the margin period of risk

Technical credit events may cause CDS protection to pay out on an event that is not considered a default by bondholders , which tend to make the basis positive i. This would tend to make the basis positive. A delivery squeeze involves a shortage of CDS deliverable debt and would tend to make the basis negative i. Fixed-rate bonds can trade significantly above or below par because of changes in interest rates.

CDS protection is essentially indexed to the par value of a bond and bonds trading above below par will tend to make the basis negative positive. The use of fixed coupon CDS reduces this effect. In the event of default, a bond typically does not pay accrued interest for any coupons owed, whereas a CDS does require protection buyers to pay the accrued premium up to the credit event. The Algo One Capital Management solution not only assists in achieving compliance, but also prepares an institution to meet supervisory expectations, and to manage credit risk and capital more effectively across the enterprise.

IBM Algo One V delivers an integrated approach to financial risk management

It offers:. Algo One Credit Economic Capital Base is a solution designed to help financial institutions manage portfolio credit risk and economic capital. It can scale efficiently to accommodate portfolios of any size and composition, enabling firms to more effectively balance risk appetite and diversification. The solution helps firms meet regulatory reporting requirements and make active asset allocation decisions, with portfolio management tools for optimizing risk-adjusted returns, and advanced stress testing functionality to provide a deeper understanding of the portfolio through explicit shocks to risk factors, as well as detailed scenario and what-if analysis.

Extensive product coverage supports exchange traded and over-the-counter OTC instruments, structured products, and high volume pooling for retail, small and medium enterprise SME , and corporate loan markets. Algo One Credit Economic Capital Advanced Add-on: The advanced add-on for credit economic capital provides full valuation of instruments sensitive to market risk factors which provides a credit loss profile that incorporates both market and credit risk.

This approach can be used for both OTC and exchange traded instruments and allows users to additionally stress market risk factors such as interest rates, foreign exchange rates, equity prices, commodity prices, and credit spreads to see the impact on portfolio credit losses and economic capital.

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The calculation engine can simultaneously calculate risk-weighted asset and other measures required for reporting under multiple home or host multi-jurisdictional regulatory rules. Risk-weight assets are calculated at the most granular transaction level and can be aggregated up to legal entity or business unit level or other aggregation keys. Algo One Credit Regulatory Capital Advanced Add-on: Helps to enable risk-weighted assets and capital requirement calculations under Foundation and Advanced Internal Ratings-based approaches designed to verify the ideal allocation of actions to mitigate the exposures of the bank to minimize the resultant risk-weighted assets and capital requirements.

Algo Portfolio Construction and Risk Management helps asset owners and managers, or their asset servicers, succeed in tough financial markets by providing an integrated risk framework designed to help optimize portfolio performance, achieve better risk oversight, and address increasing client and regulatory demands. This solution is specifically designed to provide access to sophisticated risk management and investment decision support tools to help improve investor confidence and achieve regulatory compliance. Algo One Buy Side Base: Provides the capability to calculate risk on portfolios of financial assets both on an absolute basis or with respect to benchmarks on a relative basis, as well as on an end-of-day or intraday batch frequency.

It allows users to embed the Algo One Risk Application Explorer risk calculations within their applications. Algo One Buy Side Optimization Add-on: Enables users to create replicating portfolios with respect to a benchmark for example, life insurance liabilities and to create portfolios with optimized risk and return characteristics with the Risk Application Explorer. Portfolios can be enhanced on an absolute or relative basis and can take into consideration investment constraints for example, no shorting, asset allocation constraints, duration limits and trading constraints.

Algo One Buy Side Credit Risk Add-on: Provides clients with the ability to perform several types of credit risk analysis including measuring counterparty credit exposures, credit value adjustment CVA , and portfolio credit risk default and migration.


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This solution helps insurers manage balance-sheet risk exposures market and non-market for economic capital, solvency capital, and risk management purposes. The solution includes advanced asset modeling and proxy liability modeling for example, replication portfolios, curve fitting capabilities and is designed to deliver timely and valuable information to risk managers, actuaries, portfolio managers, and senior management to enable tactical and strategic business decision making as well as regulatory compliance. It tracks key data items and approval processes for key modeling decisions.

This add-on ensures reliable, validated, and reproducible risk and capital information that can be used in business decision making. Algo Investment Design for Wealth Management is a powerful investment analytics solution that delivers an innovative engagement model for wealth advisors and investors to provide a personalized and consistent investment experience. Portfolio performance monitoring offers wealth managers the capability to monitor the risk and return profile of client portfolios, and enables exception reporting that deviates from the targeted profile, to enable swift rebalancing.

This monitoring includes Value at Risk VaR , but also extends beyond such standard measures to include more advanced analytics such as Monte Carlo multi-step simulations. Multi-period simulation provides simulation of risk and return profiles over time to support the building of consistent and optimal long-term investment portfolios. The built-in aggregation engine enables simulated data to be aggregated at a portfolio level, for example, providing market views for an overall client portfolio.

Simulated data feeds into proprietary or third-party financial planning systems enable the delivery of the output for compliance and reporting purposes. This is designed to strengthen the wealth manager relationship with clients as the whole financial planning infrastructure data, risk analytics software, and outputs from their own financial planning systems is private-labeled, so that all that is seen from the client perspective is the brand of their wealth manager.

The solution is designed to protect the confidentiality of business-critical information, as wealth managers do not need to disclose client-level data at any point in the process. Instead, simulated data sets cover instruments across all asset classes, requiring only the selection of instruments from the data matching the underlying structure of the specific investor portfolio.


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For details on these solutions and ordering information, refer to Software Announcement , dated October 2, All data is validated. During the data cleansing process, all data changes are tracked and saved for auditing purposes. This helps to ensure adherence to internal or regulatory requirements.

Moreover, private placements or internally structured securities without an ISIN code or CUSIPs can be structured from a client's corresponding term sheets, to be included in the service in a consistent fashion with those securities containing an ISIN code.