This will be a short post discussing how risk is normally handled in finance and how OTAS uses them. Today I’ve been improving code that deals with them, so I thought I would write a bit about risk models.
The audience for this is either someone from outside finance who isn’t familiar with finance norms, or someone in finance who has not had the chance to study risk models in detail.
The definition of risk
Intuitively, the term ‘risk’ should be something to do with the chances that you will lose an uncomfortable amount of money. In the equities business it is normally defined to be the standard deviation of returns. So if in a given year, your portfolio makes perhaps on average £500k, but fluctuating so that perhaps on a bad year it loses £500k, or on a good year it makes £1.5M, your risk is probably about £1M.
This can catch people out – that the definition that is almost universally used (for equities) includes the risk of making lots of money as well as the risk of losing lots of money. You could make the argument that if the stock can go up by 10%, then it could go down 10% just as easily. You could imagine situations where that’s not true though: If 10 companies were bidding for an amazing contract that only one of them would win, then you’re more likely to make lots of money than lose it (if you buy shares in one company).
In fact, the reasons that standard deviation of returns is used is that it’s simple to calculate. That might sound as if it’s the technical teams making a decision to be lazy in order to make life easy, but actually trying to estimate risk in a better way is nightmarishly difficult – it’s not that the quant team would have to sit and think about the problem for *ages*, it’s that the problem becomes guesswork. Getting the standard deviation of a portfolio’s returns takes a surprisingly large number of data points in finance (because fat tails makes the calculation converge more slowly than expected), but getting a complete picture of how the risk works including outliers, catastrophic events, bidding wars, etc., takes far, far more data.
Since there isn’t enough data out there the missing gaps would have to be filled by guess work. And so most people stick to a plain standard-deviation based risk model.
Having a simple definition means that everyone agrees on what risk numbers are: If someone asks you to keep your risk to less than 5% per year, they and you can look at your portfolio and largely agree that a good estimate of risk would be under the threshold. Then most people can look at the actual returns at the end of the year and say whether or not the risk was under the 5% target.
How risk models work
Let’s accept that risk is modelled in terms of the standard deviation of portfolio returns. To estimate your realised risk, you just take how many dollars you made each day for the last year or so, and take a standard deviation. The risk model, though, is used to make predictions for the future.
The risk model could just contain a table of every possible or probable portfolio and the predicted risk for that portfolio, but it would be a huge table. On the other hand, that is a complete description of what a risk model does: It just tells you the risk for any hypothetical portfolio. We can simplify this a bit by noting that if you double a portfolio’s position, the risk must double, so don’t have to store every portfolio. In fact, similar reasoning means that if we have the standard deviation for N(N-1)/2 portfolios, we can work out the standard deviation for every portfolio.
Another way of saying the same is that all we need is the standard deviation for each stock, and the correlation between every stock and every other: If we know how volatile Vodafone is, and how volatile Apple is, and the correlation between them, then we can work out the volatility of any portfolio just containing Vodafone and Apple.
In the first instance, all you can do to predict the future correlation between two stocks is to look at their history – if they were historically correlated, we can say that they probably will be correlated in the future. However, we can probably do slightly better than that, and simplify the risk model at the same time using the following trick:
We make the assumption that the only reason that two stocks are correlated is that they share some factor in common: If a little paper manufacturer in Canada is highly correlated to a mid-sized management consultancy firm in Australia, we might say that it’s only because they’re both correlated to the market. Basically you have, say, 50 hypothetical influences, (known as “factors”) such as “telecoms”, or “large cap stocks” or “the market”, and you say that stocks can be correlated to those factors. You then ban the risk model from having any other view of correlation: The risk model won’t accept that two stocks are simply correlated to each other – it will only say that they’re both correlated to the same factors.
This actually helps quite a bit because the risk model ends up being much smaller – this step reduces the size of the risk model on the hard drive by up to 100 times, and it also speeds up most calculations that use it. If the factors are chosen carefully, it can also improve accuracy – the approximation that stocks are only correlated via a smallish number of factors can theoretically end up averaging out quite a lot of noise that would otherwise make the risk model less accurate.
What OTAS Tech does with them
OTAS Technologies uses risk models for correlation calculations, and for estimating clients portfolio risk, and for coming up with hedging advice. Risk models are also useful for working out whether a price movement was due to something stock-specific or whether it was to do with a factor move.