# Tag Info

18

Sharpe ratio is defined as $\frac{(x - r)}{\sigma}$ where $x$ is return, $r$ is the risk free rate and $\sigma$ is volatility. Now levering up $n$ times multiplies both the return and volatility by $n$. But shouldn't the ratio change since $r$ stays the same? Ah, but remember, leverage isn't free. You have to fund leverage, and that cuts out of your return. ...

8

The textbook academic answer is that Sharpe ratio is not impacted by leverage as explained by other answers. However, reality tells a different tale entirely: Imagine you lever up your investments by such amount that your future performance will critically hinge on the following conditions: That those who extended credit to you will not re-call their ...

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Generally no. Sharpe ratio should vary linearly. Use leverage: the return increases, but so does volatility. De-lever" the return decreases but, so does volatility.

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"It's compliated" because the trading strategy performance will depend on the data which is most likely serially correlated. So you want to look into bootstrap approaches for time series such as the block bootstrap, or the wild bootstrap. Another approach would be to look into 'random portfolios' or an approximation thereof. The basic idea is to test how ...

4

As a short summary and adaption of the question: You better redefine $\hat{r}_i= \frac{S_{i-1}}{S_1}-1$ and $\hat{S}_i = (1+\hat{r}_i)S_0$. The above definition of $\hat{S}_i$ yields a sample of potential values for $S$ for the future day. This approach is usually applied in historical simulation. The aim here is to use information of the past about the ...

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Hint: If these 2 stocks have perfect negative correlation (correlation: -1), then you can construct a risk free portfolio. What would the return on that risk free portfolio be?

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You can look at the contents of the CFA institute : https://blogs.cfainstitute.org/insideinvesting/2013/01/23/how-much-does-apple-make-a-dupont-analysis/ . As there are more and more candidates and CFA charteholders, we could say that their views are becoming or are already the mainstream views. I would add that accounting and corporate finance is not a "...

3

TL;DR: the test statistic's distribution is $N(0,1)$ A bit more information about the Automatic Variance Ratio Test: $H_0$: ${\Delta}r_t$ is serially uncorrelated (where ${\Delta}r_t=r_t-r_{t-1}$) $H_1$: ${\Delta}r_t$ is serially correlated The test statistic is $VR=\sqrt{T/l}[\hat{VR}(l)-1]/\sqrt{2} \quad {\xrightarrow{d}} \quad N(0,1)$ The $d$ over ...

3

Most of the papers concern CDS spreads which you will need to convert to a PD. Paper using country specific fundamentals: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2517018 This paper uses leverage: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2361872 Another one that decomposes them against peer groups: http://papers.ssrn.com/sol3/papers....

2

It might help to think of the two as special cases of $$S_{i+1}-S_i = \sigma (c+S_i)^\beta \epsilon$$ which looks like a Constant Elasticity of Variance extension. Taking squares of both sides and then logs will (nearly) linearise it, allowing you to carry some basic estimation using OLS. The parameter $c$ will control the lower bound and can impose some ...

2

I am also not aware of any papers in this area. But having developed many such models, I can list the important steps: Decide on the target variable: usual choices are historical default data, agency ratings and expert rankings Create a sample containing the possible predictors Reduce the list with the help of some expert, e.g. exclude all the predictors ...

2

David Addison's discussion of log versus simple(arithmetic) returns in his answer is correct, but this particular calculation has nothing to do with arithmetic versus log returns. Up Capture is defined by Bacon(2004), p. 47 as: $$Up Capture = \frac{\bar{r+}}{\bar{b+}}$$ (mean of the asset returns over mean of the benchmark return) So simple versus log ...

2

I am not familiar with that R package, but I've written a few performance tracking libraries in my past life, so I might be able to add some insight. While it is indeed true that logarithmic returns may be added and subtracted, all non-quant investors and hedge funds present their performances in percent returns. The reason one can't simply add and subtract ...

2

Sure! Sharpe ratio must be defined as the return per unit risk on a zero-cost position. The notion you are referring to achieves this by assuming borrowing at a risk-free rate before investing, so refinancing risks should matter. On a side note, the Sharpe ratio of any ForEx strategy would implicitly have the stuff you mention accounted for.

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You can define information ratio on ex-ante basis, so you will be using the expected values, and this definition is called alpha omega: $IR=\frac{\alpha}{\omega}$ Let’s represent the risk reversion by $\lambda$ then the value add is: $VA=\alpha-\lambda \omega^2$ Substituting for alpha: $VA=IR \omega -\lambda \omega^2$ Now the value add is maximised ...

2

Having reviewed the documentation sent by Noob2 and rechecking everything, I came to the following conclusion: ((6044−2002)/2002)^1/20.38=5.57% is absolutely wrong. If one does the calculations for this you get 1.035 (I have no idea how I managed do come up with 5.57% in the first place). Thus this resolves the question where I am confused about the ...

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For anyone looking for this, I ended up calculating average_win and average_loss and then calculating the ratio as: R = average_win / average_loss

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You should use average monthly return over stddev.

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There is no such thing as number of independent bets when one is betting on a common random factor as we quants usually do. Grinold & Kahn’s formula is only relevant when the factor payoff is a constant over time. This is not interesting in practice. When the factor payoff is random, then Ding and Martin The Fundamental Law of Active Management: Redux (...

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I have automated downloading of various price data with simple C# console applications that I write with Visual Studio Express (which is free). You are looking for a very specialized task, so I don't think you can get away from programming. I recommend Microsoft .Net for programming. Just create a new console application and use .Net's WebRequest and ...

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Wouldn't you just weight the p/l equation the same way your position is weighted? So: (P(StockA)*0.8)/(P(StockB)*0.2) = Net % change in the position

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It is a measure of profitability commonly used in the Banking industry. We can exclude the other 3 answers: it does not take into account the bank's leverage (i.e. it is a measure of pre-leverage profitability), it has nothing to do with asset composition (since it is based on total assets, irrespective of their composition), and it has nothing to do with ...

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Bloomberg has a Default Risk model, which is similar to what you are querying. You can see a screenshot in this PDF. There you can also see the kind of variables they use. You can access it by typing DRSK at the CDS screen is Bloomberg. (If the screenshot in the PDF is not clear enough, let me know and I can post one with better resolution from Bbg) This ...

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