Category Archives: Topical

Includes posts on physics, philosophy, sciences, quantitative finance, economics, environment etc.

Books, Corporate Life, Quantitative Finance

Market Risk Management and Analytics

If you play in the market, you run the risk that it may move against you. This risk is, of course, market risk and we have a Middle Office team to manage it. Market Risk Management (MRM) ensures that the risk limits on the volumes and types of products traded are set in accordance with the risk appetite prescribed by the senior management. It also ensures, through regular processing and monitoring, that these limits are adhered to.

MRM

What is monitored are risk measures such as the Greeks and Value at Risk (VaR). The Greeks are the first and second order derivatives of the price of a security with respect to various market variables such as the price of the underlying, interest rates, volatility as well as trade specific entities like the time to maturity. The VaR is a statistical end point measure estimating the amount of loss at a given confidence level in the case of an adverse market movements, and is typically computed using the historical market movements over the past year or so. These risk measures are aggregated, sliced and diced in various ways to make it easy to monitor them, and reported to senior management, risk control committees, trading desks etc. The MRM team is also responsible for reporting to regulatory agencies, both in the form of regular compliance reports as well as ad hoc reports in response to drastic market moves.

Quants can find opportunities in the Analytics team embedded within MRM. This team is in charge of pricing model validation, which is the process of ensuring that the mathematical models deployed in trading systems and other valuations engines are both appropriate and correctly implemented. There is a significant overlap between the work that MRM analytics quants do and their Front Office counter parts (whom we called pricing or model quants). The Analytics team also takes care of any other quantitative tools needed in MRM or risk management in general. Such tools could include potential future exposures (PFE) for credit risk management, liquidity modelling for Assets and Liability (AML) etc.

Life and Death, Philosophy

Life: East vs. West

In the last post we examined life from the perspective of evolutionary biology. Now let’s move on to philosophy. There is an important philosophical difference between the perspectives on life in the East and the West. These views form the backdrop to the rules of life, which shape everything from our familial and societal patterns to our hopes and prayers. How these rules (which depend on where you come from) do it is not merely interesting, but necessary to appreciate in today’s world of global interactions. In one of his lectures, Yale philosophy professor Shelly Kagan made a remark that the basic stance vis-a-vis life (and death) in the West is that life is a good thing to have; it is a gift. Our job is to fill it with as much happiness, accomplishments and glory as possible.

The Eastern view is just the opposite – the first of the four noble truths of Buddhism is that life is suffering. Hinduism, which gave birth to Buddhism, says things like this world and the cycle of life are very difficult (Iha Samsare Bahu Dustare in Bhaja Govindam, for instance). Our job is to ensure that we don’t get too attached to the illusory things that life has to offer, including happiness. When we pray for our dead, we pray that they be relieved of the cycle of life and death. Deliverance is non-existence.

Of course, I am vastly oversimplifying. (Let me rephrase that — this oversimplified version is all I know. I am very ignorant, but I plan to do something about it very soon.) Viewed in the light of these divergent stances against the conundrum of life, we see why westerners place such a premium on personal happiness and glory, while their eastern counterparts tend to be fatalistic and harp on the virtues of self sacrifice and lack of ambition (or its first cousin, greed).

To an ambitious westerner, any chance at an incremental increase in personal happiness (through a divorce and remarriage, for instance) is too good an opportunity to pass up. On the other side of the globe, to one brought up in the Hindu way of life, happiness is just another illusory manifestation not to be tempted by. Those caught in between these two sets of rules of life may find it all very confusing and ultimately frustrating. That too is a macro level pattern regimented by the micro level rules of the game.

Books, Corporate Life, Quantitative Finance

Credit Risk Management

Risk management is a critical function of Middle Office. Credit risk is the risk that somebody who owes you money may not be able or willing to honor their obligation. In other words, they may default on their credit obligation. This risk is managed in a bank using a variety of statistical tools.

Middle Office

When a bank issues you a credit card, it takes on credit risk that you may not pay up. You pay an insanely high interest rate on your outstanding balance precisely because of this credit risk. The risk is not secured. A mortgage or an auto loan, on the other hand, is secured by the equity of your property, and you pay a significantly lower interest because of the collateral.

The Middle Office team of Credit Risk Management (CRM) operates using the same two paradigms. Much the same way as you have a credit limit on your credit card or line of credit, each counterparty that the bank trades with has a certain credit limit based on their credit rating as published by credit rating agencies such as Moody’s or Standard & Poor. The problem with this mode of managing credit risk is that the bank has no way of knowing how much credit is loaded against a counterparty’s rating in other banks. Nor does it have a means of finding out how many credit cards you have. In Singapore, the regulatory authority, MAS, tries to minimize the risk of people going bust be requiring that their credit limit be twice their monthly salary. Bt they may get as many credit cards as they want from different banks against the same limit, effectively nullifying the good intention behind the requirement.

This overloading against credit rating is avoided when the risk is managed using collaterals. Much like you cannot take two mortgage loans on the same property (not without adequate equity, any way), counterparties in trading also cannot use the same collateral for multiple trades. Banks and counterparties typically use bonds as collaterals and physically exchange them during secured transactions.

Before the Front Office trader can enter into a trading agreement with a counterparty, they will need to get approval from the credit controllers who will assess exposures and check them against predefined limits. The exposure assessment uses techniques such as potential future exposure (PFE) based on a large number of simulations of potential future markets.

In addition to the risk of counterparties defaulting during the life time of a trade, CRM professionals worry about the potential for default during the delay in settlement — after the maturity of a trade (where the bank is in the money) and its settlement. This risk is aptly called the settlement risk.

Life and Death, Philosophy

Game of Life

We started this series with chess and then moved on to the socio-political topology of a typical corporate landscape. Both could be understood, in some vague and generous sense, in terms of a simple set of rules. If I managed to convince you of that satement, it is thanks to my writing prowess, rather than the logical cohesion of my argument. I am about to extend that shaky logic to the game of life; and you should be wary. But I can at least promise you a good read.

Okay, with that reservation stated and out of the way, let’s approach the problem systematically. My thesis in this series of posts is that the macro-level patterns of a dynamic system (like a chess game, corporate office, or life itself) can be sort of predicted or understood in terms of the rules of engagement in it. In chess, we saw that general pattern of any game (viz. structured beginning, messy mid-game, clean endgame with a win, lose or draw) is what the rules prescribe. In this last post, we are going to deal with life. In a trivial analogy to chess, we can describe the pattern like this: we are all born somewhere and some point in time, we make our play for a few years, and we bow out with varying amount of grace, regardless of how high we soar and how low we sink during our years. But this pattern, though more rigorously followed than our chess pattern, is a bit too trivial. What are the salient features or patterns of human life that we are trying to understand? Human life is so complex with so many aspects of existence and dimensions of interactions among them that we can only hope to understand a limited projection of a couple of its patterns. Let’s choose the pattern of family units first.

The basic set of rules in human life comes from evolutionary biology. As a famous man put it, nothing in biology (or life itself, I would think) makes sense except in the light of evolution. On the other hand, everything from gender politics to nuclear family units makes perfect sense as the expressions of the genetic commands encoded in our DNA, although we may be stretching the hypothesis to fit the facts (which is always possible to do) when we view it that way. Let’s look at the patterns of gender relations in family units, with the preamble that I am a total believer in gender equality, at least, my own brand of it.

Evolutionary biology tells us that the instruction encoded in our genes is very simple — just live a little longer, which is at the root of our instincts for self preservation and reproduction. In the end, this instruction expresses itself as a man’s hidden antipathy toward monogamy and a woman’s overt defense of its virtues. Although this oft-repeated argument can be seen as a feeble attempt at justifying the errant and philandering behavior of man, it has simplicity on its side. It makes sense. The argument goes like this: in order to ensure the continued survival of his genes, a man has to mate with as many partners as possible, as often as possible. On the other hand, given the long gestation period, a woman optimizes the survival chances of her genes by choosing the best possible specimen as her mate and tying him down for undivided attention and for future use. Monogamy indeed is virtuous from her perspective, but too cruel a rule in a man’s view. To the extent that most of the world has now adopted monogamy and the associated nuclear family system as their preferred patterns, we can say that women have won the gender war. Why else would I feel scared to post this article? Weaker sex, indeed!

Evolutionary biology is only one way of looking at life. Another interesting set of rules comes from spiritual and religious philosophy, which we will look at in the next post.

Corporate Life, Humor, Philosophy, Work and Life

Art of Corporate War

A more complex example of how the rules shape the patterns on the ground is the corporate game. The usual metaphor is to portray employees as cogs in the relentless wheel of the corporate machinery, or as powerless pawns in other people’s power plays. But we can also think of all of them as active players with their own resources engaged in tiny power plays of their own. So they end up with a corporate life full of office politics, smoke and mirrors, and pettiness and backstabbing. When they take these things personally and love or hate their co-workers, they do themselves an injustice, I think. They should realize that all these features are the end result of the rules by which they play the corporate game. The office politics that we see in any modern workspace is the topology expected of the rules of the game.

What are these famous rules I keep harping on? You would expect them to be much more complex that those of a simple chess game, given that you have a large number of players with varying agendas. But I’m a big fan of simplicity and Occam’s Razor as any true scientist should be (which is an oblique and wishful assertion that I am still one, of course), and I believe the rules of the corporate game are surprisingly simple. As far as I can see, there are just two — one is that the career progression opportunities are of a pyramid shape in that it gets progressively more difficult to bubble to the top. The other rule is that at every level, there is a pot of rewards (such as the bonus pool, for instance) that needs to be shared among the co-workers. From these rules, you can easily see that one does better when others do badly. Backstabbing follows naturally.

In order to be a perfect player in this game, you have to do more than backstabbing. You have to develop an honest-to-john faith in your superiority as well. Hypocrisy doesn’t work. I have a colleague who insists that he could do assembly-level programming before he left kindergarten. I don’t think he is lying per-se; he honestly believes that he could, as far as I can tell. Now, this colleague of mine is pretty smart. However, after graduating from an IIT and working at CERN, I’m used to superior intelligences and geniuses. And he ain’t it. But that doesn’t matter; his undying conviction of his own superiority is going to tide him over such minor obstacles as reality checks. I see stock options in his future. If he stabs someone in the back, he does it guiltlessly, almost innocently. It is to that level of virtuosity that you have to aspire, if you want to excel in the corporate game.

Almost every feature of the modern corporate office, from politics to promotions, and backstabbing to bonuses, is a result of the simple rules of the game that we play it by. (Sorry about the weak attempt at the first letter rhyme.) The next expansion of this idea, of course, is the game of life. We all want to win, but ultimately, it is a game where we will all lose, because the game of life is also the game of death.

Books, Corporate Life, Quantitative Finance

Middle Office

The structure of Middle Office in a typical bank is depicted in the slide below. The functional units within Middle Office work hand in hand with those in Front Office to handle the inception approvals and regular processing of trades.

Middle Office

Middle Office is different from Front OFfice in that it has little interaction with the external world. Its primary (and perhaps only) customers are the Front Office traders and teams. As usual, most of the interactions among the teams within Middle Office and Front Office take place via the trading platform, which acts like the boundary interface between the two Offices, as shown in the slide.

In later posts, we will go through the functions of each of the business units described as a box in the picture. For now, as a general summary, we can see that the Middle Office functions are of two kinds: those related to trade approvals based on projected risks and limits, and those related to regular trade monitoring. But these functions are vast in their scope, and require large systems, data flows and an army of professionals to carry them out. They are organized under the business units with names like Product Control, Trade Control (or Treasury or Business Control) Unit, Market, Credit and Operational Risk Management), Limits Monitoring, Rates Management, Compliance and Regulatory Reporting, Analytics, Asset and Liability Management etc. Again, keep in mind that this description of Middle Office is from the perspective of quantitative development relevant to structured products trading.

Philosophy, Physics, Work and Life

Rules of the Game

Richard FeynmanRichard Feynman used to employ the game of chess as a metaphor for the pursuit of physics. Physicists are like uninitiated spectators at a chess match, and they are trying figure out the rules of the game. (He also used sex, but that’s another story.) They observe the moves and try figure out the rules that govern them. Most of the easy ones are soon discovered, but the infrequent and complex ones (such as castling, to use Feynman’s example) are harder to decipher. The chess board is the universe and the players are presumably the Gods. So when Albert Einstein’s Albert Einstein said that he wanted to know God’s thoughts, and that the rest were details, he probably meant he wanted to know the rules and the strategies based on them. Not the actual pattern on the board at any point in time, which was a mere detail.

A remarkable Indian writer and thinker, O. V. Vijayan, also used the metaphor of a chess game to describe the armed strife between India and her sibling neighbor. He said that our too countries were mere pawns in a grand chess game between giant players of the cold war. The players have stopped playing at some point, but the pawns still fight on. What made it eerie (in a Dr. Strangelove sort of way) is the fact that the pawns had huge armies and nuclear weapons. When I first read this article by O. V. Vijayan, his clarity of perspective impressed me tremendously because I knew how difficult it was to see these things even-handedly without the advantage of being outside the country — the media and their public relations tricks make it very difficult, if not impossible. It is all very obvious from the outside, but it takes a genius to see it from within. But O. V. Vijayan’s genius had impressed me even before that, and I have a short story and a thought snippet by him translated and posted on this blog.

Chess is a good metaphor for almost everything in life, with its clear and unbending rules. But it is not the rules themselves that I want to focus on; it is the topology or the pattern that the rules generate. Even before we start a game, we know that there will be an outcome — it is going to be a win, loss or a draw. 1-0, 0-1 or 0.5-0.5. How the game will evolve and who will win is all unknown, but that it will evolve from an opening of four neat rows through a messy mid game and a clear endgame is pretty much given. The topology is pre-ordained by the rules of the game.

A similar set of rules and a consequent topology exists in the corporate world as well. That is the topic of the next post.

Books, Corporate Life, Quantitative Finance

Quantitative Developers

If Quantitative Developers look like the heart of everything that goes on in the Front Office (according to the following slide, that is), there is a good reason for it. This series is written from the perspective of Quantitative Development. After all, the series, the talk, and the book are all titled “Principles of Quantitative Development.” From that vantage point, sure, we are at the center of the universe.

Quantitative Developers

To be fair, in structured products trading, quantitative development and quantitative mathematics play a crucial role. As we will see in later posts, almost all the aspects of trade lifecycle management are mediated by the end product of these quantitative professionals, which is the trading platform. Crucially, the trading platform defines the interface between Front Office and Middle Office. Within Front Office, quantitative developers act as the conduit of integrating the pricing models developed by quants into the platform, thereby making them accessible for profit making by trading desks. Because of this buffering role that the quantitative developers play, they have to field almost all of the support requests from trading desks and sales personnel in Front Office, as well as from anyone who uses the trading platform.

In the corporate organization, quantitative developers may find themselves under the information technology department, supporting the trading platform from afar. From a career perspective, this organization is less than ideal for them because IT is a cost center, not a profit generator and the compensation and remuneration schemes reflect that fact. Besides, IT tends to be considered as being outside the core business of the bank. Far better for them would be to find themselves embedded within the Front Office setting, where the quantitative developers can offer direct support to the stakeholders from within and enjoy the prominence and prestige that comes with the critical role of managing the vital in-house trading platform.

Science, Work and Life

Average Beauty

If you have migrated multiple times in your life, you may have noticed a strange thing. The first time you end up in a new place, most people around you look positively weird. Ugly even. But slowly, after a year or two, you begin to find them more attractive. This effect is more pronounced if the places you are migrating from and to have different racial predominance. For example, if you migrate from the US to Japan, or from India to China. As usual, I have a theory about this strange phenomenon. Well, actually, it is more than a theory. Let me begin at the beginning.

About fifteen years ago, I visited a Japanese research institute that did all kinds of strange studies. One of the researchers there showed me his study on averaging facial features. For this study, he took a large number of Japanese faces, and averaged them (which meant he normalized the image size and orientation, digitally took the mean on a pixel-by-pixel basis). So he had an average Japanese male face and an average Japanese female face. He even created a set of hybrids by making linear combinations of the two with different weighting factors. He then showed the results to a large number of people and recorded their preference in terms of the attractiveness of the face. The strange thing was that the average face looked more pleasant and attractive to the Japanese eye than any one of the individual ones. In fact, the most attractive male face was the one that had a bit of female features in it. That is to say, it was the one with 90% average male and 10% average female (or some such combination, I don’t remember the exact weights).

The researcher went one step further, and created an average caucasian face as well. He then took the difference between that and an average Japanese face, and then superimposed the difference on an average face with exaggerated weights. The result was a grotesque caricature, which he postulated, was probably the way a Japanese person would see a caucasian for the first time.

This reminded me of the time when I visited my housemate’s farm in a small town in Pennsylvania – a town so small that the street in front the farm was named after him! I went with his parents to the local grocery store, and there was this little girl sitting in a shopping cart who went wide-eyed when she saw me. She couldn’t take her eyes off me after that. May be, seeing an Indian face for the first time in her life, she saw a similar caricature and got scared.

Anyway, my conjecture is that an averaging similar to what the Japanese researcher did happens in all of us when we migrate. First our minds see grotesque and exaggerated difference caricatures between the faces we encounter and the ones we were used to, in our previous land. Soon, our baseline average changes as we get more used to the faces around us. And the difference between what we see and our baseline ceases to be big, and we end up liking the faces more and more as they move progressively closer to the average, normal face.

Here are the average male and female faces by race or country. Notice how each one of them is a remarkably handsome or beautiful specimen. If you find some of them not so remarkable, you should move to that country and spend a few years there so that they also become remarkable! And, if you find the faces from a particular country especially attractive, with no prolonged expsosure to such faces, I would like to hear your thoughts. Please leave your comments.

  
[I couldn’t trace the original sources of these images. If you know them, please let me know — I would like to get copyright permissions and add attributions.]

There is more to this story than I outlined here. May be I will add my take on it as a comment below. However, the moral of the story is that if you consider yourself average, you are probably more attractive than you think you are. Than again, what do I know, I’m just an average guy. 🙂

If you found this post interesting, you may also enjoy:

  1. Why is seeing not quite believing?
  2. Sophistication
Books, Corporate Life, Quantitative Finance

Trading Desks

At the heart of Front Office are the trading desks. In terms of prestige and power, they really are the reason for the whole infrastructure of Front Office, including economists, sales, structuring, quants, quantitative developers etc. After all, they make the profits. And consequently, the vocal and volatile traders hold enormous sway. At their beck and call, quantitative developers provide instant service on trading platform issues; quants develop pricing models based on their requirements.

Trading Desks

Trading desks interact with the external world of brokers and counterparties. They take their input on market moves from highly responsive market data providers and base their positional views on staff economists. They have an army of trading assistants (junior traders themselves) who help them book and monitor their trades with the help of risk management professionals associated with the desks.

Their interaction with the rest of the bank is mainly mediated by the trading platform. When the book a trade, for instance, it goes into the trading platform and ends up with some middle office professional who will decide whether to accept it or bounce it back for further modifications. Various risk management staff from the middle office also will take a hard look at the trade, as we shall see later in the trade lifecycle.

The desk risk management team get their cues also from the Middle Office risk management, in terms of approved limits and daily marked-to-market and sensitivity reports. All these channels of communication need to be facilitated in the trading platform.