Explanation
Choice 'b' is the correct answer. All other answers are incorrect.
Refer to the detailed loss event type classification under Basel II (see Annex 9 of the accord). You should know the exact names of all loss event types, and examples of each.

Explanation
The first two formulae describe component VaR. The last formula is the formula for Marginal VaR. Therefore I and II is the correct answer.
Component VaR is a VaR decomposition technique that allows the total VaR for a portfolio to be broken down and attributed to the components of a portfolio. The total of the component VaR for each constituent of a portfolio is equal to the VaR for the portfolio. This property is extremely useful as opposed to the standalone VaR for each constituent taken alone as it can be used for allocating trading budgets.

Explanation
This question is different in that it uses terminology you will not find in the PRMIA handbook. Yet it is important to understand these as there may be a question based on this slightly different terminology. (It is only the terminology that is different, the concepts are the same.) If you read the PRMIA handbook, there are three methods of calculating VaR: Analytical or parametric, historical simulation and Monte Carlo simulations. There is one more way of categorizing the methods of calculating VaR, and these are as follows:
1. Local valuation: This refers to analytical or parametric VaR. This relies upon a neat statistical formula to calculate VaR and assumes a normal distribution. It also relies upon a known covariance matrix between the different components of VaR. Local valuation based VaR is further subdivided into two types:
a. Linear VaR: Linear VaR is calculated assuming the portfolio is linear, and its value changes just based upon the delta of the portfolio. In such cases, once a change (eg, in stock values) is known, that change is multiplied by the delta alone to get the VaR. Second order effects, such as gamma or convexity are ignored.
b. Non-linear VaR: Non linear analytical VaR is calculated using both delta and the second derivative, ie gamma or the convexity. This is more accurate if the portfolio is non-linear.
The key thing about 'local revaluation' VaR is that it does not require us to reprice or completely value all instruments in the portfolio. All we have to know is the delta (or the gamma and convexity as well) and multiply that with the number of standard deviations of change in the risk factor that we are interested in. So if we are considering a bond, we don't have to recalculate the new value of the bond as we can just use the delta.
This can be a significant computational advantage for a large financial institution where there may be a large number of positions.
2. Full revaluation: This refers to a VaR method where the asset in question is fully repriced based on the new value of the risk factor - and this includes both historical and Monte Carlo based VaR methods.
Local revaluation, or analytical method based VaR is computationally easier to calculate, specially if based on just the delta-normal method (ie ignoring second order effects from convexity or gamma). But it will give incorrect results if the portfolio includes substantial non-linearity or other complexities. The full revaluation methods will always give the correct results, but they can be computationally difficult to arrive at.
Statement I is completely inaccurate - local revaluation methods do take correlations into account through the correlation or covariance matrices. Statement IV is false too - the 'delta normal' VaR refers to Var calculations based upon just the delta and do not account for the convexity or optionality. Statements II and III are correct.
Therefore Choice 'c' is the correct answer.

Explanation
The persistence parameter, , is the coefficient of the prior day's variance in EWMA calculations. A higher value of the persistence parameter tends to 'persist' the prior value of variance for longer. Consider an extreme example - if the persistence parameter is equal to 1, the variance under EWMA will never change in response to returns.
1 - is the coefficient of recent market returns. As is lowered, 1 - increases, giving a greater weight to recent market returns or shocks. Therefore, as is lowered, the model will react faster to market shocks and give higher weights to recent returns, and at the same time reduce the weight on prior variance which will tend to persist for a shorter period.

Explanation
Choice 'a' (The amplification of smaller initial shocks to one risk factor creating larger subsequent shocks through system-wide interactions between other risks, creating self-perpetuating downward stresses in the markets) is the most comprehensive description of 'feedback effects', as described in the BCBS document on stress testing. Choice 'c' is one manifestation of feedback effects, but does not describe the entire effect.
Choice 'b' is not a description of 'feedback effects', but one of the various weaknesses in stress testing that was seen during the crisis. Choice 'd' is plain nonsensical.
The BCBS paper provides a good and succinct description of feedback effect: how mortgage default shocks led to a deterioration of market prices of CDOs, followed by a drying up of the liquidity in these markets. This led to banks having to hold on to assets they intended to securitize (securitization and warehousing risk), and given the absence of transparency on who was exposed to what, banks refusing to lend to each other and a drying up of the wholesale funding market as well. All of this was additionally accompanied by a general flight to quality, households withdrawing money from money market funds creating a crisis in that market as well. At each stage, the initial shock was amplified and fed back into the system through interactions that had not been imagined by any market participant or regulator, leave alone risk managers.