# Tag Info

## New answers tagged risk

1

For the first, people regularly compute VaR or CVaR over time and plot the results. For two and three, the documentation for the ETL function says that you can either calculate it using a Gaussian approach or Cornish-Fisher expansion. These are both analytical methods. The Gaussian approach uses only the mean and variance (effectively assuming that the ...

2

Well, well ... First of all, i doubt people would think of hedge fund strategies in the way you are thinking. If I were to classify, the first order of a super high-level classification would be, for example, equity stock selection (e.g. say Apple vs Google, etc.), macro selection (e.g. currencies, commodities, country bets via stock indices, etc.), or ...

0

I think what you are missing is simply the Vega-Gamma relation in the Black-Scholes model. Namely: $$Vega = \frac{\partial v}{\partial \sigma} = \sigma(T-t)S^2 \frac{\partial^2 v}{\partial S^2} = \sigma \tau S^2 \Gamma$$ Plugging this into your coverage error, you get its expression in terms of the Vega which is the most natural measurement of your ...

0

this is related to the concept of Jensen inequality. basically, $\frac{f(x-|\delta|)+f(x+|\delta|)}{2}\ne f(x)$, for convex functions it's $>f(x)$, and for concave ones $<f(x)$. risk averse guys have concave utilities, that's the relation you need to look at

0

Here it is: "Rebonato, R., Jackel, P. The most general methodology to create a valid correlation matrix for risk management and option pricing purposes." Recall: a covariance matrix will be the same as a correlation matrix if scale is removed. I used this method for ensuring positive definite correlations matrices.

1

Sure, the variance of the total wealth can be expressed in terms of the variances and covariances of the prices of the assets. If $$W = \sum_{i} \pi_i P_i$$ where $\pi_i$ is the total dollar amount invested in asset $i$ with price $P_i$. The variance of total wealth is then  Var(W) = \sum_i \pi_i Var(P_i) + \sum_i \sum_{j, j\neq i} \pi_i \pi_j Cov(P_i, ...

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