It has been a while since, I am coming back with another instalment on the series. The intent remains same; study the building blocks that form the basis of a scientific study of Risk. Risk is a subject that differs from many we know of. The principal reasons being its heterogeneity. The subject borrows extensively from mathematics, statistics, physical sciences, market finances, economics, …….
And Common Sense!!
It’s often said that common sense is the most ’uncommon’ of all things. We are driven, as humans, through a mix of logic derived by observance and pure empirical study. As our quest for knowledge progresses and proliferates, we develop the means through tools and models to reinforce our derived knowledge with an amalgam of future expectations. Future expectations and economic studies through behavioural aspects of man have been captured in the fields of quantitative Economics, Game Theory which I already discussed briefly in my previous article(s) and the systematic study of the theory of Probability. There have been many tools in the making but the science of Risk Management has relied heavily on mathematics and statistics, probability being in the forefront.
We are not here to discuss the means, neither the ’ends’ if any. As an evolving science, and art perhaps, Risk Management has always been in the centerstage, and will be, as a curious conundrum ready to be deciphered from multiple angles. We tweak our processes as we learn more, seek to challenge what we already established and enforced to an extent to suit the requirement of the time. This makes it so very fascinating, as you will learn, as students of finance and risk that you have to unlearn. Almost always!
As I pen down this instalment, the world is plunged into another crisis. Russia invading Ukraine, who is defending its territory is a grim reminder that history will keep on repeating itself. The battle between two unequal nations is interspersed with international concerns and actions. Economic sanctions, and counter sanctions resonate the economic landscapes with political rhetorics; businesses huddle together seeking hedges and protections from the governments. Technologies that are the best guarantees of business successes are re-redeployed and fine tuned as scarce resources. As markets tank, commodities surge and uncertainties loom, governments and business alike seek, other than protection, information…..as to ’what next’!
This very concept of ’next’ is the pivot of the principles and practice of Risk Management!
I was narrating in my previous article the powerful concept of the Capital Asset Pricing Model and where it fits or does not. As I endeavour to build a readable, simple anecdote of modern risk management, without recourse of mathematics and technical jargons, I try to focus on extremely key concepts that have been the drivers along the path we have taken so far. We mentioned, and I request you all to please re-read my previous article(s) to get that continuity, that the systematic risk, defined by the Greek alphabet beta, is a key factor when we diversify portfolios to maximise returns….or minimise losses? Where does this lead to? Key questions emerge that have been baffling the markets. What is this exact dimension of the beta? As I showed using just a line of equation, it looks like some sort of an adjustment for the portfolio. Does adding more stocks (again taking care of the correlations) solve this problem. We have two immediate issues here; Returns (maximise) and Risk (minimise). I would drill down to the following immediate questions:
— What is that unknown that determines the risk and returns as I showed above?
— What do I need to know to determine this unknown?
My very simplistic, almost naive questions have a deeper layer. It is the ’’Mathematical’’ problem.
As I had shown in my previous article, a portfolio can be defined by two numbers; the Expected Return and the Variance. Now, dependencies on these two numbers is perfectly justified if, and only if, the security returns are ’’normally distributed’’, meaning Gaussian Bell Curve (I will elaborate a bit on it later if needed). Outliers are not permitted; and the array of results on either side of the curve, or Mean (the statistical average) must be symmetrically distributed. Does it happen in real life? Well if you ask me the honest answer, it is NO. But then, as I always said, in Risk we do not seek the perfect answer but an answer that leads to perfect results at that point in time. Risk is a time dependent and judgemental approach, backed up by data and intelligent analyses.
Now when data is not normally distributed, the variance may be insufficient to reflect 100% of the uncertainties in the portfolio. Since in real world, there is nothing called absolute certainty, this is a genuine issue. Again this ’’normalisation’’, as many of you know in the real world is a divided concept (who knows it better than our HR folks while doing review of your performances!). For some it is good, for some it is not. For the latter such imperfections in portfolio decisions and risk calculations open up new avenues of strategy creations that we will see later.
As we arrive at a critical juncture, on the Variance, I will tell you an amusing story that I read somewhere:
A group of hikers in the wilderness came upon a bridge that would greatly shorten their return to the base camp. Noting that the bridge was high, narrow, and rickety they fitted themselves out with ropes, harnesses, and other advanced, well thought out safeguards before starting to cross. After a great deal of effort and applying ’sophisticated safety’ measures, they reached the other side, and found a hungry mountain lion patiently waiting their arrival!!
Welcome to the world of Volatility!!
I do have a feeling that Harry Markowitz with his sharp focus on volatility would have been taken by surprise by that mountain lion!
Volatility, which is always the basis for the kind of risk we know is that Variance in the model. For decades this Volatility has an intuitive appeal as a proxy for Risk. As we study deeper and deeper into volatilities, and perhaps end up more in mess, we simply make out that when prices ’’jump’’ too much around the mean, the value (of the asset) almost surely would fall. To contain such ’jumps’’ sophisticated hedging tools like Derivatives came into being, though it never remained within the confinement of the containment of the jumps or what we would like to call as a ’hedge’!
In my next article, I will spill more beans on this lion for sure. Let me know how you feel so far.
Finance & Risk Demystified 