- Last resort kind of method used by many financial risk managers for getting rough idea of the maximum potential loss. It is obtained by simply multiplying the cashflow by risk factors such as daily volatility.
- Its weakness comes from its ignoring the cross correlation between each asset classes, therefore over estimating the actual risk exposure.

- It is an improvement of the former method by incorporating covariance matrix (correlation effects between each asset classes) primarily developed by J.P.Morgan Risk Management Advisory Group in 1996. It is often called Risk Matrics Methodology.
- Major method used by many financial organizations due to its simplicity (Uses only delta).
- Many 3rd party financial software vendors supports this method using industry standard RiskMatrics (TM) Daily Data sets.
- Reasonably good method for portfolio with no option type products
- Thus far, the computationally fastest method known today.
- Not suited for portfolio with major option type financial products.

- Improvement of the previous Variance-Covariance Method by incorporating the Gamma & Theta effects.
- Answers some questions of portfolio with some optionality, but not quite well suited if any of the option is extremely near expiration (The Gamma/Theta approximation not good enough to capture the extremely non-linearity near the expiration)
- Considered as the add-on for the typical Variance-covariance Method
- Not widely used yet due to lack of industry consensus

- Uses the historical market prices/rates to simulate the differences in MTM (Mark-to-Market)
- Works for virtually any portfolio.
- Not widely used due to the lack of industry support.

- Actually generating the entire MTM (Mark-to-Market) any thousands of times from correlated random numbers.
- Considered as the industry standard for simulation method.
- Works for virtually any portfolio.
- Problem is its slow computational speed, and also subject to bias if not enough sample are run.

- Improvement of the above method by using mathematically much faster techniques such as variance reduction methods. Therefore, this method is computationally much faster, and yet still gives the identical result theoretically as the above method.
- Subject of much intense research. Many researchers (including myself) are investigating this methodology.
- Gaining market support due to its flexibility & ability to combine customized distribution with fat tails (rather than traditional correlated Gaussian distribution) applied to virtually all portfolio types (whether linear or non-linear instruments) .

For the past 10 years, the issue of credit risk has been getting many attentions. Many firms such as J.P.Morgan (CreditMatrix, Credit manager), CS First Boston (CreditRisk+), and KMV (EDF- Expected Default Frequency Method) have come up with their own ways of quantifying the credit risk. However, there seems to be no consensus on how one can incorporating credit risk with market risk. Credit risk is an very important risk one must quantify given the recent Asian crisis (Japan, China), and emerging markets crisis which plagued many financial institution.

This name is more like my own creation than anything else. To my best knowledge, there is no word "TVAR" in financial literatures. I will explain this terminology in the coming update

Copyright (c) 1998 Kazuhiro Iwasawa ---------All Right Reserved. |