大浩浩的笔记课堂——FRM考试学习笔记122

Topic 51 Assessing the Quality of Risk Measures

1.Model risk:

⑴definition:

It is the risk of incorrect trading or risk management decisions due to errors in models and model applications,which can lead to trading losses and potential legal,reputational,accounting liquidity,and regulatory risk.

⑵examples:

Errors can be introduced into models through programming bugs,securities valuations or hedging,VaR estimates,and position mappings:

①programming bugs;

②securities valuations:

valuation risk:

A.Valuation errors due to inaccurate models are examples of market risk as well as of operational risk.

B.Model errors can be avoided and valuation risk reduced,by relying on market prices rather than model prices.

③errors in VaR estimates:

Using a VaR implementation that relies on normally without appreciating the deviations of the model from reality.

④incorrect position mappings

2.The variability of VaR estimates:

⑴definition:

Firms use software to compute the risk measures from the data collected using specific formulas,which can be performed in a variety of ways and lead to potential issues.

⑵preparation:

There are 3 risk measurement system data preparation:

①market data

②security master data

③position data

⑶factors:

①Variability in risk measures,including lack of uniformity in the use of confidence intervals and time horizons,can lead to variability in VaR estimates.

②Other factors can also cause variability,including length of time series,ways of estimating moments,mapping techniques,decay factors,and number of simulations.

⑷problems:

There are 2 problems with the use of VaR in practice:

A.There is not much uniformity of practice as to confidence interval and time horizon.

B.Different ways of measuring VaR would lead to different results.

3.Risk factor mapping for VaR calculations:

⑴definition:

It refers to the assignment of risk factors to positions and mapping choices can considerably impact VaR results.

⑵categories:

①cash flow mapping:

It results in greater accuracy of estimates.

②duration mapping:

It requires the use of fewer risk factors and less complex computations such as reducing costs,data errors and model risks.

·特别注意！

·Locating data that addresses specific risk factors may also be difficult.

⑶characteristics:

①Mapping can also have a large impact on VaR results.

②Such mapping problems may merely mirror the real-world difficulties of hedging or expressing some trade ideas:

examples:

A.widespread prior to the subprime crisis

B.convertible bond trading

③For some strategies such as even-driven strategies and dynamic strategies,VaR can be misleading,outcomes are close to binary.

⑷risks:

①liquidity risk:

Liquidity risk arises from large divergences in model and market prices that are difficult to capture with market data,and as a result,VaR estimates based on replicating portfolios can understate risk and create liquidity risk.

②basis risk:

A.Basis risk is the risk that a hedge does not provide the required or expected protection.

B.Basis risk arises when a position or its hedge is mapped to the same set of risk factors.

4.Case studies:

⑴Long-Term Capital Management(LTCM):

①introduction:

It was a U.S. hedge fund that used arbitrage strategies to exploit spread differentials between bonds,including spread differences of European bond,and the spread differences in corporate and government.

②incident＆results:

LTCM´strading strategy relied on arbitrage positions based on market-neutral and relative-value trading.It uses extensive leverage to amplify the predictable,low returns.

③key factors:

A.failure to supplement VaR with a full set of stress test scenarios

B.failure to account for illiquidity of positions during stress

C.leverage was too high

D.too much faith in models(model risk)

④lessons:

LTCM´scollapse highlighted several flaws in its regulatory VaR calculations:

A.The fund´scalculated 10-day VaR period was too short,so a time horizon is needed that is sufficiently long enough to model the time to raise new capital.

B.The fund´sVaR models did not incorporate liquidity assumptions.

C.The fund´srisk models did not incorporate correlation and volatility risks.

⑵London Whale:

risk culture,model risk,and operational risk:

①The London trading desk belonged to JPM´sChief Investment Office(CIO),which was responsible for managing the bank´sexcess deposits.The CIO used the deposits to engage in high-profit potential,high-risk derivatives trading strategies.

②JPM garnered international headlines when in the first half of 2012.It sustained losses in excess of $6 billion due to risky synthetic credit derivatives trades executed by a trader,called the ″London Whale″,in its London office.

③The CIO adopted a new VaR model which lowered its calculated VaR by 50%.

④The losses from the London Whale trade and the subsequent investigations revealed a poor risk culture at JPM.

⑶the 2005 credit correlation episode:

①the background:

the credit market in early 2005:

A.Volatility in credit markets in the spring of 2005 focused by company bankruptcies and losses lead to defaults in the IG3 and IG4 index series of the CDX.NA.IG index,causing large selloffs that resulted in widening spreads.

B.This also resulted in modeling errors from both misinterpretation and incorrect application of models,which lead to trade losses.

②the trade:

A.Selling protection on the equity tranche of the CDX.NA.IG to long credit and credit spread riskon the equity tranche.

B.Buying protection on the junior mezzanine tranche of the CDX.NA.IG to short credit and credit spread risk through mezzanine.

③the motivation:

A.The trade was intended to achieve a positively convex payoff profile.

B.The trade was designed to be default-risk-neutral at initiation.

C.The trade was designed to benefit from credit spread volatilities.

D.The portfolio also had positive carry,it can earn a positive net spread.

④the critical error:

A.The critical error of the trade was that it was setup at a particular value of implied correlations.

B.The critical flaw was that the correlation assumption was static.

C.Stress testing correlation would have revealed the risk.

⑷subprime default models:risk underestimation in 2007 to 2009:

①The 2 significant model errors in the RMBS valuation and risk models led to a significant underestimation of systematic risk in subprime RMBS returns during 2007 to 2009:

2 key assumptions:

A.house price appreciation assumption:

The models assumed positive future house price appreciation rates or at least stay flat,but the eventual decline in house prices starting in 2007 led to a significant underestimation of the potential default rates and systematic risk in RMBS.

B.low(geographical) correlation assumption:

Correlations among regional housing markets were assumed to be low,but when house prices declined,correlations and loan defaults increased.

②additional points:

A.The model errors,and/or inappropriate parameters,led to a substantial underestimation of the degree of systematic risk in subprime RMBS returns.

B.The inaccuracy of rating agency models for subprime RMBS is a complex phenomenon.

大浩浩的笔记课堂之FRM考试学习笔记合集