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1

Instead of thinking "at the margin", I've opted to conduct an attribution of sorts, by running the copula with the empirical Kendall's tau-b correlation matrix and again with a zero matrix. The difference between the two scenarios represents correlation risk.

2

There are a few reasons the authors may have only looked at risky assets. First, they are trying to find a faster way to solve a mean-CVaR optimization through relaxations. Therefore, they probably saw handling the risk (CVaR aka ES) as the most interesting part of the problem. Granted, doing so completely ignores that they should be looking at excess ...

1

As a starting point, you might want to read "TAIL RISK HEDGING: Creating Robust Portfolios for Volatile Markets", by Vineer Bhansali (2013). The author runs a tail risk fund (LongTail Alpha) so you will at least get a practitioner's perspective. [EDIT: I avoided saying more because your questions are very broad. If you want a "multi-asset ...

1

They just want to apply their technique to risky assets that actually have volatility. The risk-free asset has a volatility of $0$ so allocation towards it is treated as an after-thought since it's pretty much in an asset class if its own, whereas the risky assets on the risky side of the portfolio might have to be allocated from various risky asset classes ...

5

I'll add some comments, recognizing that 1) they are highly opinionated, and 2) they don't actually offer any real solutions. Hopefully more thoughtful and useful answers will emerge. First of all, purely from a philosophical perspective, I have to admit that I sometimes find these discussions on strategic asset allocation (SAA) "strange." ...

4

Many pension funds use projected asset class returns (capital market assumptions or CMAs) and backward-looking estimates of volatilities and correlations to set the strategic asset allocation. A 10-year period for the return projection is typical. The determination of actual weights is more or less an exercise in constrained mean-variance optimization. ...

1

Have the implications (if any) actually been implemented in actual asset allocation shifts for such funds? Yes - or at least a shift is being considered. See this story https://www.cnbc.com/2020/09/16/singapore-summit-cppib-ceo-on-zero-bound-interest-rates.html

-1

First, Fama French is not a model. It is a falsification of the Capital Asset Pricing Model. The APT is not an extension of the CAPM. It is conceptually similar, but it is laid upon a different conceptual framework. Let us begin with the Fama-French. It is not a model. It does not describe the behavior of humans. The Capital Asset Pricing Model does ...

2

It sounds like the P&L's you are given are not really the historical P&L's. Rather, you have some portfolio and market data currently; you have 260 days of historical market data changes; and you calculate what the P&L of the present portfolio would have been if the market moved as it did on that historical date from the current market data. You'...

4

If you have a covariance matrix, $Q$ the VaR is a measure of the standard deviation of the portfolio, ie. $$VaR, V \propto \sqrt{S^T Q S}$$ and, $$\frac{\partial V}{\partial S} = \frac{QS}{V}$$ Suppose you had 3 assets, with large positions in the first two assets, and small position in the third, AND that the first two were perfectly negatively correlated,...

3

One way to look at answering this question is VAR Contribution. Evaluate VAR of the Portfolio, and then evaluate VAR of the Portfolio without the asset. The largest difference of VAR with the asset - VAR of the portfolio without the asset would be the asset which is contributing the most to VAR. You may want to correct the size of the portfolio for each ...

0

VaR is a loss function calculated from what's available in step 1, whose value is a magnitude, and whose sign indicates whether there is a portfolio loss, or a negative loss (which is actually a gain, given that VaR, as a loss, is ordinarily reported as a negative number). So to ask which asset, whose returns are available in step 2, is driving this loss ...

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