# Tag Info

9

One might probably mention Yale's Endowment under David Swensen which generated returns of 13% per annum over the past two decades (as compared to the 8 or 9% average return of college and university endowments). Now, I would not label Swensen's approach to portfolio management with a pure absolute return strategy tag but he definitely uses some insights ...

9

I don't have much to add, but wanted to address the "price of risk" question. APT is kind of "economics"-free and tries to price assets without the utility maximization required in CAPM/ICAPM. Ross's APT observes that groups of assets move together (e.g., tech stocks) and that is the risk you're bearing because the idiosyncratic risk, like the firing of ...

8

The regression requires orthogonalization of factors. However, we need to maintain the interpretation of factors (so PCA and Factor Analysis are out). Also, we could apply an iterative method (indeed this is very common practice) but this will bias the factor loadings on the sequence of factors. Best approach is that of Klein and Chow in their paper ...

8

That's a tough question to answer. The "quant business" is a business. Some quants sell low-grade/low-volatility results, some sell fast-moving/unpredictable results, some sell industry targeted results, etc. It depends on what the buyer wants to buy. There's a market for everything. Haven't we all met people that think they're going to win the ...

6

I believe that beta will be the covariance of the factor with the underlying asset. Is this correct? Close, it's the covariance divided by the variance of the factor. $$\beta_{f,a} = \frac{\sigma_{f,a}}{\sigma^2_f}$$ Also how is the return attributable to a specific factor calculated? Is there a single way this is done ...

6

I believe the reason no one has been able to come up with an example of a quant fund employing the academic factor-based approach with stellar performance is because there aren't any (at least not any with decent sized AUM). For a while, now, there has been a debate amongst institutional investors and quantitative professionals as to whether quant is dead. ...

6

I'll answer by way of example. Suppose I want to buy a stock that is relatively cheap. Firstly, I need to define what is meant by cheap, so I might choose to look at the price-to-earnings ratio. Then I need to define what is meant by relative, so I might compare stocks only within a given sector. This may work well at first, but then I notice that as I try ...

5

+1 for asking an excellent question. I agree with the answers of @Owen and @chrisaycock - I'm late to the party but perhaps this will shed some light. How practitioners or academics answer this question will tell you a lot about their view on the nature and sources of returns and risk. For example, the Fama-French "equilibrium" school of thought would argue ...

4

The first principal component of a large covariance matrix is extremely expensive to replicate in a real portfolio. While it is true principal components provide true (ex post) orthogonal factors, this is not necessarily relevant to the business of risk management. The market index is what most investors are benchmarked by, and is furthermore often ...

4

a) because it does not matter how you weigh each constituents as long as the methodology is publicly accessible and as long as it more or less reflects the original intent. That is why there are market cap weighted indexes but also why there are indexes that apply different weighting methodologies. b) because PCA is computationally way more expensive. Why ...

4

Here is a structured list of your bullet points: covariance, correlation, PCA, factor analysis, Are similar. They are based on Gaussian assumptions (i.e. correlations means dependencies) and try to identify common factors (i.e. a variable in small dimension) explaining the observed relationships. co-integration is more specific in the sense that you ...

4

Jennifer Bender of MSCI Barra has a paper from 2007 entitled: To Beta or Not to Beta: A Comparison of Historical Versus Fundamental Betas for Hedging Market Risk She deals specifically and exclusively with which method is superior for hedging long-only portfolios. Not surprisingly, she finds that Barra's approach is better. She tests long-only and ...

4

I can offer three suggestions: (a) Since any model, however sophisticated, will miss tail cases (such as Oct 2008) I would increase the number of high-frequency factors (eg weekly jobless claims - I don't know if that is a relevant example in your case - but just to give you an idea) in the model. Not only does that make the model more responsive to current ...

3

If you have a series of observations of the return as a vector, $\mathbf{r}$ with corresponding observations of the factor returns in matrix $Z$, then the least squares estimate of the vector of betas is $$\hat{\beta} = \left(X'X\right)^{-1} X'\mathbf{r},$$ where $X$ is the matrix with $Z$ and a column of all ones (for the intercept term). The last ...

3

I'd add: Variance reduction Fraction same sign / Hit rate Additionally, you might look at the relationship between the Q5-Q1 spread itself and the dependent (i.e. are larger/smaller spreads associated with some feature of the dependent). Turnover may also be an issue as slippage and friction come into consideration. Measures such as percent turnover in ...

3

If it was possible to simply pick up some papers and adapt them and produce returns that trade would quickly disappear since it would be an inexpensive way for firms to produce excess returns. If you have a factor that produces alpha you had best not publish it or all returns associated with it will disappear. I have found most of the value in the academic ...

3

Hopefully these ideas open up some solution strategies. A. Calibration approach: In the case of a volatility model such as Axioma's above, you could perform an instantaneous volatility adjustment. Procedure: You build your usual T+H volatility model. You measure the realized volatility and implied volatility of the training set. You measure the ...

3

Most of the credit risk models are some derivative of survival models. Cox Proportional Hazard is one of the early and more popular models, Kaplan-Meier and Logrank tests are others you may have heard of. There are a few ways to go from here. The simplest is to model the sample as binomial with one population as current and the other as in default. A ...

2

The best option is to identify the other missing factors and include them in your analysis. Depending on your data and assumptions, PCA is a good place to start. Your data also shows signs of a time-varying correlation with your factor. Hence, it $may$ also be appropriate to allow for time-varying regression coefficients or some other technique to account ...

1

For corporate credit portfolios, sector, rating, and maturity are the usual suspects that go into the credit portion of risk model structure, which usually also have interest rates and liquidity pieces. This book is slightly outdated but will give you a good general introduction.

1

Wikipedia gives: $\sigma(x,y) = E[xy] - E[x]E[y]$ and $\sigma(ax+by,cz) = ac\, \sigma(x,z) + bc\, \sigma(y,z)$ (paraphrasing the $\sigma(ax+by,cW+dV)$ rule). So $\sigma(I,A) = \sigma([aA+bB+cC+dD],A)$ $\sigma(I,A) = a\,\sigma(A,A) + b\,\sigma(B,A) + c\,\sigma(C,A) + d\,\sigma(D,A)$ \$\sigma(I,A) = a\,\sigma^2(A) + b\,\sigma(B,A) + c\,\sigma(C,A) + ...

1

If you're using R, you might try: http://cran.r-project.org/web/packages/relaimpo/index.html http://stats.stackexchange.com/questions/8918/is-there-a-way-to-optimize-regression-according-to-a-specific-criterion/8932#8932

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