What are the steps to perform properly a risk factor analysis on a portfolio?
What are the steps to perform properly a risk factor analysis on a portfolio?
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SRKX · External communityPost link
External question — Quantitative Finance Stack Exchange
Author: SRKX
Original post: https://quant.stackexchange.com/questions/3552
License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/
Adaptation: HTML converted to plain text; contact email addresses removed.
I have been asked to perform a factor analysis on a given portfolio, assume it's a Swiss portfolio in CHF.
First step
, I chose which factors I would like to see in my analysis.
The first factors I would add are components of the portfolio (and used the hedged performance)
Performance of a global equity index
Performance of a global fixed income index
Performance of gold
Performance of a commodity index
Then I would like to have the forex factors so I add
EURCHF performance
USDCHF performance
Finally, I would like to have some macro-economics indicators:
Change in GDP of Switzerland
Inflation Rate
Unemployment rate.
For example.
Si I gave a large bunch of factors, my first question is, some time series have larger values in magnitude than others and I was wondering whether I should normalize them before going further?
Do you think it makes sense to split "pure" stock performance and forex components?
Second Step
I will eventually be looking to do the following:
$$Y_t = \alpha + \sum_{i=1}^k \beta_i {F_i}_t + \varepsilon_t$$
where $F_i, \quad 0<i \leq k$ is the i-th factor and $y_t$ is the return of the portfolio at time $t$.
The problem is that for this to be meaningful we need the different $F_i$ to be independent.
Is there a general accepted method in our field to use to get a set of independent factors? (I asked the question
here
but I could not come up with a straight answer).
Third step
Once this filter is done we have $l\leq k$ independent factors. I was thinking about running the regression over the remaining $l$ factors, and then look at their p-values to see which ones are significant and hence I want to keep. Is there a better usually used in factor analysis?
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Quoted from Forex.com.bd-Editorial External question — Quantitative Finance Stack Exchange Author: SRKX Source score (net votes, not local likes): 10 Original post: https://quant.stackexchange.com/questions/3552 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I have been asked to perform a factor analysis on a given portfolio, assume it's a Swiss portfolio in CHF. First step , I chose which factors I would like to see in my analysis. The first factors I would add are components of the portfolio (and used the hedged performance) Performance of a global equity index Performance of a global fixed income index Performance of gold Performance of a commodity index Then I would like to have the forex factors so I add EURCHF performance USDCHF performance Finally, I would like to have some macro-economics indicators: Change in GDP of Switzerland Inflation Rate Unemployment rate. For example. Si I gave a large bunch of factors, my first question is, some time series have larger values in magnitude than others and I was wondering whether I should normalize them before going further? Do you think it makes sense to split "pure" stock performance and forex components? Second Step I will eventually be looking to do the following: $$Y_t = \alpha + \sum_{i=1}^k \beta_i {F_i}_t + \varepsilon_t$$ where $F_i, \quad 0<i \leq k$ is the i-th factor and $y_t$ is the return of the portfolio at time $t$. The problem is that for this to be meaningful we need the different $F_i$ to be independent. Is there a general accepted method in our field to use to get a set of independent factors? (I asked the question here but I could not come up with a straight answer). Third step Once this filter is done we have $l\leq k$ independent factors. I was thinking about running the regression over the remaining $l$ factors, and then look at their p-values to see which ones are significant and hence I want to keep. Is there a better usually used in factor analysis?
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