Getting Started With Risk

by Peter Harrington

Posted on April 6, 2018, 2:56 p.m.

The term risk can be confusing. If you sit and think about the word risk, and what it means you will come up with some answer along the lines of assigning a probability that a given event will happen. There are risks all around us and consciously or not, we evaluate the risk of an event and take steps to mitigate that risk or decide that the risk (probability) is too small and not worth mitigating. There are risks we could die traveling to work or school, yet we decide that going to work or school is worth the risk. So the same concept exists in money management, and these concepts apply rather you are managing $10K of your own money, or $10B of the money for a pension fund. How do you not loose all your money? Diversify. ("Don't put all your eggs in one basket"). Yet when things are going well in one investment you will wish you had put all your money in that. Andrew Carnegie famous quote: "The way to become rich is to put all your eggs in one basket and then watch that basket." So there exists a tradeoff and balancing that tradeoff is the subject of much work.

Risk Models I think this term is confusing to people new to finance, and new to institutional size money management. Here is an actual conversation I had with a friend (let's call him Mr. E) who worked as a quant at a few large hedge funds:

Peter "FundX had a shitty year and they fired the CIO."

Mr. E "why did they have a bad year?"

Peter "I heard that most of their algorithms were making money off of Short Term Reversion, and they weren't tracking it. Then Short Term Reversion stopped working and they had a drawdown."

Mr. E "I find that hard to believe, when that CIO and I worked at FundY, all the PMs had to report their risk exposures to Gordon Gekko (the founder of the fund with an enormous ego)."

So why did I bring up this conversation? To illustrate what happens in larger funds: there are PMs managing a chunk of money, and there are risk managers who's job it is to make sure the fund does not have all of it's eggs in one basket. They way they can do this is have all the PMs report in a standardized format their "risk exposures". These PMs are making money but if an event happens or a way of making money turns around what is it going to mean for the whole firm?

Risk management may seem like a jumping through hoops for some worry wart ass hole, however usually it's a good idea. The first long/short multifactor strategy I ever traded with my own money was a bit of a wild ride, (I will save that story for another time) however after I applied some risk control it cut the drawdown in half, and only had marginal impact on the returns. That is the tradeoff you should strive for.

Traditional Risk Models I think this term is rather confusing, at least to the person not familiar with portfolio management. When people are talking about the risk models they are talking about using Markowitz minimum variance to adjust return for risk. If you have n assets you want to hold in your portfolio then you will want to maximize μ'w - γw'Σw, or some similar version perhaps with added penalties and constraints. Σ is the asset covariance matrix, which determines how the assets move together. The question is: "what are the risks?", so people spend a lot of time on this. I will go into detail on this in another post with some Python code showing you how to build you own. In Susan Alexander's book "Market Models" she discusses how a firm should have a library of covariance matrices to stress test portfolio's on different events. The key insight here is that co-movement is not stationary: when markets are calm things remain uncorrelated, however when shit goes south everything becomes correlated. Her comment should give us another insight as to why everyone uses a model introduced in 1952: it is a common framework, and has become part of the language. It would be hard for people to understand what we were doing if we used some other model.

Non-Traditional Risk Models As I mentioned in the last paragraph everyone uses the Markowitz mean variance with an off the shelf covariance matrix or their own covariance matrix. They spend time building the matrix, then plug it into a convex solver. Could you maximize risk adjusted return, while meeting constraints another way? Absolutely. In the book "Inside the Black Box" Rishi Narang discusses portfolio construction techniques: "Some quants use machine learning techniques such as supervised learning, or genetic algorithms to help with the problem of optimization. The argument in favor of machine learning techniques in portfolio construction is that mean variance optimization is a form of data mining in that it involves searching many possible portfolios and attempting to find the ones that exhibited the best characteristics, as specified by the objective function of the optimizer. But the field of machine learning aims to do much the same thing, and it is a field that has received more rigorous scientific attention in a wide variety of disciplines than portfolio optimization, which is almost exclusively a financial topic. As such, there may be good arguments for considering machine learning approaches to finding the optimal portfolio, especially due to the quality of those algorithms relative to the mean variance optimization technique"

Hedge funds are the startups of the financial world. One of the reasons startups can innovate is that they are small companies and do not have the constraints of larger companies. Perhaps there is much to be gained from using non-traditional risk models?

Thanks for reading

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Hi this is Peter Harrington's spot for discussing all things related to quantitative finance. Mostly focusing on how to build your own system and strategy. I focus on Long/Short equity and futures, but am open to learning about other assets and strategies.