Tag Archives: quantitative finance

Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine, Work and Life

Group Dynamics

When researchers and academicians move to quantitative finance, they have to grapple with some culture shock. Not only does the field of finance operate at a faster pace, it also puts great emphasis on team work. It cuts wide rather than deep. Quick results that have immediate and widespread impact are better than perfect and elegant solutions that may take time to forge. We want it done quick rather than right. Academicians are just the opposite. They want to take years to mull over deep problems, often single-handedly, and come up with solutions elegant and perfect.

Coupled with this perfectionism, there is a curious tendency among academic researchers toward creating a “wow” factor with their results, as opposed to finance professionals who are quite content with the “wow” factor in their bonuses. This subtle mismatch generates interesting manifestations. Academics who make the mid-career switch to finance tend to work either alone or in small groups, trying to perfect an impressive prototype. Banking professionals, on the other hand, try to leverage on each other (at times taking credit for other people’s work) and roll out potentially incomplete solutions as early as possible. The intellectual need for a “wow” may be a factor holding back at least some quant deliverables.

Columns, Coming Soon, Corporate Life, Quantitative Finance, The Wilmott Magazine

Philosophy of Money

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Underlying all financial activity are transactions involving money. The term “transactions” means something philosophically different in economics. It stands for exchanges of goods and services. Money, in economic transactions, has only a transactional value. It plays the role of a medium facilitating the exchanges. In financial transactions, however, money becomes the entity that is being transacted. Financial systems essentially move money from savings and transforms it into capital. Thus money takes on an investment value, in addition to its intrinsic transactional value. This investment value is the basis of interest.

Given that the investment value is also measured and returned in terms of money, we get the notion of compound interest and “putting money to work.” Those who have money demand returns based on the investment risk they are willing to assume. And the role of modern financial system becomes one of balancing this risk-reward equation.

We should keep in mind that this signification of money as investment entity is indeed a philosophical choice that we have made over the past few centuries. Other choices do exist — Islamic banking springs to mind, although its practice has be diluted by the more widely held view of money as possessing an investment value. It is fascinating to study the history and philosophy of money, but it is a topic that calls for a full-length book on its own right. Understanding money at its most fundamental level may in fact enhance our productivity — which is again measured in terms of the bottom line, consistent with the philosophy of money that enjoys currency.

Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine

Slippery Slopes

But, this dictum of denying bonus to the whole firm during bad times doesn’t work quite right either, for a variety of interesting reasons. First, let’s look at the case of the AIG EVP. AIG is a big firm, with business units that operate independently of each other, almost like distinct financial institutions. If I argued that AIG guys should get no bonus because the firm performed abysmally, one could point out that the financial markets as a whole did badly as well. Does it mean that no staff in any of the banks should make any bonus even if their particular bank did okay? And why stop there? The whole economy is doing badly. So, should we even out all performance incentives? Once we start going down that road, we end up on a slippery slope toward socialism. And we all know that that idea didn’t pan out so well.

Another point about the current bonus scheme is that it already conceals in it the same time segmentation that I ridiculed in my earlier post. True, the time segmentation is by the year, rather than by the month. If a trader or an executive does well in one year, he reaps the rewards as huge bonus. If he messes up the next year, sure, he doesn’t get any bonus, but he still has his basic salary till the time he is let go. It is like a free call option implied in all high-flying banking jobs.

Such free call options exist in all our time-segmented views of life. If you are a fraudulent, Ponzi-scheme billionaire, all you have to do is to escape detection till you die. The bane of capitalism is that fraud is a sin only when discovered, and until then, you enjoy a rich life. This time element paves the way for another slippery slope towards fraud and corruption. Again, it is something like a call option with unlimited upside and a downside that is somehow floored, both in duration and intensity.

There must be a happy equilibrium between these two slippery slopes — one toward dysfunctional socialism, and the other toward cannibalistic corruption. It looks to me like the whole financial system was precariously perched on a meta-stable equilibrium between these two. It just slipped on to one of the slopes last year, and we are all trying to rope it back on to the perching point. In my romantic fancy, I imagine a happier and more stable equilibrium existed thirty or forty years ago. Was it in the opposing economic ideals of the cold war? Or was it in the welfare state concepts of Europe, where governments firmly controlled the commanding heights of their economies? If so, can we expect China (or India, or Latin America) to bring about a much needed counterweight?

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Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine

Profit Sharing

Among all the arguments for hefty bonuses, the most convincing is the one on profit generation and sharing. Profit for the customers and stakeholders, if generated by a particular executive, should be shared with him. What is wrong with that?

The last argument for bonus incentives we will look at is this one in terms of profit (and therefore shareholder value) generation. Well, shareholder value in the current financial turmoil has taken such a beating that no sane bank executive would present it as an argument. What is left then is a rather narrow definition of profit. Here it gets tricky. The profits for most financial institutes were abysmal. The argument from the AIG executive is that he and his team had nothing to do with the loss making activities, and they should receive the promised bonus. They distance themselves from the debacle and carve out their tiny niche that didn’t contribute to it. Such segmentation, although it sounds like a logical stance, is not quite right. To see its fallacy, let’s try a time segmentation. Let’s say a trader did extremely well for a few months making huge profits, and messed up during the rest of the year ending up with an overall loss. Now, suppose he argues, “Well, I did well for January, March and August. Give me my 300% for those months.” Nobody is going to buy that argument. I think what applies to time should also apply to space (sorry, business units or asset classes, I mean). If the firm performs poorly, perhaps all bonuses should disappear.

As we will see in the last post of the series, this argument for and against hefty incentives is a tricky one with some surprising implications.

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Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine

Talent Retention

Even after we discount hard work and inherent intelligence as the basis of generous compensation packages, we are not quite done yet.

The next argument in favour of hefty bonuses presents incentives as a means of retaining the afore-mentioned talent. Looking at the state of affairs of the financial markets, the general public may understandably quip, “What talent?” and wonder why anybody would want to retain it. That implied criticism notwithstanding, talent retention is a good argument.

As a friend of mine illustrated it with an example, suppose you have a great restaurant thanks mainly to a superlative chef. Everything is going honky dory. Then, out of the blue, an idiot cook of yours burns down the whole establishment. You, of course, sack the cook’s rear end, but would perhaps like to retain the chef on your payroll so that you have a chance of making it big again once the dust settles. True, you don’t have a restaurant to run, but you don’t want your competitor to get his hands on your ace chef. Good argument. My friend further conceded that once you took public funding, the equation changed. You probably no longer had any say over payables, because the money was not yours.

I think the equation changes for another reason as well. When all the restaurants in town are pretty much burned down, where is your precious chef going to go? Perhaps it doesn’t take huge bonuses to retain him now.

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Columns, Corporate Life, Environment, Humor, Quantitative Finance, Science, The Wilmott Magazine, Work and Life

Talent and Intelligence

In the last post, I argued that how hard we work has nothing much to do with how much reward we should reap. After all, there are taxi drivers who work longer and harder, and even more unfortunate souls in the slums of India and other poor countries.

But, I am threading on real thin ice when I compare, however obliquely, senior executives to cabbies and slum dogs. They are (the executives, that is) clearly a lot more talented, which brings me to the famous talent argument for bonuses. What is this talent thing? Is it intelligence and articulation? I once met a taxi driver in Bangalore who was fluent in more than a dozen languages as disparate as English and Arabic. I discovered his hidden talent by accident when he cracked up at something my father said to me — a private joke in our vernacular, which I have seldom found a non-native speaker attempt. I couldn’t help thinking then — given another place and another time, this cabbie would have been a professor in linguistics or something. Talent may be a necessary condition for success (and bonus), but it certainly is not a sufficient one. Even among slum dogs, we might find ample talent, if the Oscar-winning movie is anything to go by. Although, the protagonist in the movie does make his million dollar bonus, but it was only fiction.

In real life, however, lucky accidents of circumstances play a more critical role than talent in putting us on the right side of the income divide. To me, it seems silly to claim a right to the rewards based on any perception of talent or intelligence. Heck, intelligence itself, however we define it, is nothing but a happy genetic accident.

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Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine, Work and Life

Hard Work

One argument for big bonuses is that the executives work hard for it and earn it fair and square. It is true that some of these executives spend enormous amount of time (up to 10 to 14 hours a day, according the AIG executive under the spotlight here). But, do long hours and hard work automatically make us “those who deserve the best in life,” as Tracy Chapman puts it?

I have met taxi drivers in Singapore who ply the streets hour after owl-shift hour before they can break even. Apparently the rentals the cabbies have to pay are quite high, and they end up working consistently longer than most executives. Farther beyond our moral horizon, human slum dogs forage garbage dumps for scraps they can eat or sell. Back-breaking labour, I imagine. Long hours, terrible working conditions, and hard-hard work — but no bonus.

It looks to me as though hard work has very little correlation with what one is entitled to. We have to look elsewhere to find justifications to what we consider our due.

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Columns, Corporate Life, Quantitative Finance, The Wilmott Magazine

Bonus Plans of Mice and Men

Our best-laid plans often go awry. We see it all the time at a personal level — accidents (both good and bad), deaths (both of loved ones and rich uncles), births, and lotteries all conspire to reshuffle our priorities and render our plans null and void. In fact, there is nothing like a solid misfortune to get us to put things in perspective. This opportunity may be the proverbial silver lining we are constantly advised to see. What is true at a personal level holds true also at a larger scale. The industry-wide financial meltdown has imparted a philosophical clarity to our profession — a clarity that we might have been too busy to notice, but for the dire straits we are in right now.

This philosophical clarity inspires analyses (and columns, of course) that are at times self-serving and at times soul-searching. We now worry about the moral rectitude behind the insane bonus expectations of yesteryears, for instance. The case in point is Jake DeSantis, the AIG executive vice president who resigned rather publicly on the New York Times, and donated his relatively modest bonus of a million dollars to charity. The reasons behind the resignation are interesting, and fodder to this series of posts.

Before I go any further, let me state it outright. I am going to try to shred his arguments the best I can. I am sure I would have sung a totally different tune if they had given me a million dollar bonus. Or if anybody had the temerity to suggest that I part with my own bonus, paltry as it may seem in comparison. I will keep that possibility beyond the scope of this column, ignoring the moral inconsistency others might maliciously perceive therein. I will talk only about other people’s bonuses. After all, we are best in dealing with other people’s money. And it is always easier to risk and sacrifice something that doesn’t belong to us.

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Corporate Life, Quantitative Finance, Work and Life

How Much is Your Time Worth?

I recently got a crazy idea. Suppose I tell you, “I will give you a ten-million-dollar job for a month. But I will have to kill you in two months.” Of course, you will have to know that I am serious. Let’s say I am an eccentric billionaire. Will you take the ten million dollars?

I am certain that most people will not take this job offer. In fact, there is a movie with Johnny Depp and Marlon Brando (IMDb tells me that it is The Brave) where Depp’s character actually takes up such an offer. Twenty-five thousand, I believe, was the price that he agreed upon for the rest of his life. For some of us, the price may be higher, but it is possible that there is a price that we will agree upon.

To me, my price is infinite — I wouldn’t trade the rest of my life for any amount of money. What does it help me to have all the money in the world if I don’t have the time to spend it? But, this stance of mine is neither consistent with what I do, nor fully devoid of hypocrisy. Hardly anything in real life is. If we say we won’t trade time for money, then how come we happily sell our time to our employers? Is it just that we don’t appreciate what we are doing? Or that our time is limited?

I guess the trade off between time and money is not straight forward. It is not a linear scale. If we have no money, then our time is worth nothing. We are willing to sell it for almost nothing. The reason is clear — it takes money to keep body and soul together. Without a bare minimum of money, there indeed is no time left to sell. As we make a bit of money, a bit more than the bare minimum, we begin to value time more. But as we make more money, we realize that we can make even more by selling more time, because the time is worth more now! This implicit vicious circle may be what is driving this crazy rat race that we see all around us.

Selling time is an interesting concept. We clearly do sell our time to those who pay us. Employees sell time to their employers. Entrepreneurs sell their time to the customers, and in deploying their businesses. But there is a fundamental difference between these two modes of selling. While employees sell their time once, businessmen sell their time multiple times. So do authors and actors. They spend a certain amount of time doing whatever they do, but the products they create (book, business, movies, Windows XP, songs etc.) are sold over and over again. That is why they can make their millions and billions while those who work for somebody else find it is very difficult to get really rich.

Quantitative Finance, The Wilmott Magazine

A New Kind of Binomial Tree

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We can port even more complicated problems from mathematics directly to a functional language. For an example closer to home, let us consider a binomial pricing model, illustrating that the ease and elegance with which Haskell handles factorial do indeed extend to real-life quantitative finance problems as well.

The binomial tree pricing model works by assuming that the price of an underlying asset can only move up or down by constant factors u and d during a small time interval \delta t. Stringing together many such time intervals, we make up the expiration time of the derivative instrument we are trying to price. The derivative defined as a function of the price of the underlying at any point in time.

Figure 1
Figure 1. Binomial tree pricing model. On the X axis, labeled i, we have the time steps. The Y axis represents the price of the underlying, labeled j. The only difference from the standard binomial tree is that we have let j be both positive and negative, which is mathematically natural, and hence simplifies the notation in a functional language.

We can visualize the binomial tree as shown in Fig. 1. At time t = 0, we have the asset price S(0) = S_0. At t = \delta t (with the maturity T = N\delta t). we have two possible asset values S_0 u and S_0 d = S_0 / u, where we have chosen d = 1/u. In general, at time i\delta t, at the asset price node level j, we have

S_{ij} = S_0 u^j

By choosing the sizes of the up and down price movements the same, we have created a recombinant binomial tree, which is why we have only 2i+1 price nodes at any time step i\delta t. In order to price the derivative, we have to assign risk-neutral probabilities to the up and down price movements. The risk-neutral probability for an upward movement of u is denoted by p. With these notations, we can write down the fair value of an American call option (of expiry T, underlying asset price S_0, strike price K, risk free interest rate r, asset price volatility \sigma and number of time steps in the binomial tree N) using the binomial tree pricing model as follows:

\textrm{OptionPrice}(T, S_0, K, r, \sigma, N) = f_{00}

where f_{ij} denotes the fair value of the option at any the node i in time and j in price (referring to Fig. 1).

f_{ij} = \left{\begin{array}{ll}\textrm{Max}(S_{ij} - K, 0) & \textrm{if } i = N \\textrm{Max}(S_{ij} - 0, e^{-\delta tr}\left(p f_{i+1, j+1} + (1-p) f_{i+1, j-1}\right)) & \textrm {otherwise}\end{array}\right

At maturity, i = N and i\delta t = T, where we exercise the option if it is in the money, which is what the first Max function denotes. The last term in the express above represents the risk neutral backward propagation of the option price from the time layer at (i+1)\delta t to i\delta t. At each node, if the option price is less than the intrinsic value, we exercise the option, which is the second Max function.

The common choice for the upward price movement depends on the volatility of the underlying asset. u = e^{\sigma\sqrt{\delta t}} and the downward movement is chosen to be the same d = 1/u to ensure that we have a recombinant tree. For risk neutrality, we have the probability defined as:

p = \frac{ e^{r\delta t} - d}{u - d}

For the purpose of illustrating how it translates to the functional programming language of Haskell, let us put all these equations together once more.

\textrm{OptionPrice}(T, S_0, K, r, \sigma, N) = f_{00}
where
&f_{ij} =& \left\{\begin{array}{ll}\textrm{Max}(S_{ij} - K, 0) & \textrm{if } i = N \\\textrm{Max}(S_{ij} - 0, e^{-\delta tr}\left(p f_{i+1\, j+1} + (1-p) f_{i+1\, j-1}\right)\quad \quad& \textrm{otherwise}\end{array}\right.
S_{ij} = S_0 u^j
u = e^{\sigma\sqrt{\delta t}}
d = 1/u
\delta t = T/N
p = \frac{ e^{r\delta t} - d}{u - d}

Now, let us look at the code in Haskell.

optionPrice t s0 k r sigma n = f 0 0
    where
      f i j =
          if i == n
          then max ((s i j) - k) 0
          else max ((s i j) - k)
                    (exp(-r*dt) * (p * f(i+1)(j+1) +
                    (1-p) * f(i+1)(j-1)))
      s i j = s0 * u**j
      u = exp(sigma * sqrt dt)
      d = 1 / u
      dt = t / n
      p = (exp(r*dt)-d) / (u-d)

As we can see, it is a near-verbatim rendition of the mathematical statements, nothing more. This code snippet actually runs as it is, and produces the result.

*Main> optionPrice 1 100 110 0.05 0.3 20
10.10369526959085

Looking at the remarkable similarity between the mathematical equations and the code in Haskell, we can understand why mathematicians love the idea of functional programming. This particular implementation of the binomial pricing model may not be the most computationally efficient, but it certainly is one of great elegance and brevity.

While a functional programming language may not be appropriate for a full-fledged implementation of a trading platform, many of its underlying principles, such as type abstractions and strict purity, may prove invaluable in programs we use in quantitative finance where heavy mathematics and number crunching are involved. The mathematical rigor enables us to employ complex functional manipulations at the program level. The religious adherence to the notion of statelessness in functional programming has another great benefit. It helps in parallel and grid enabling the computations with almost no extra work.

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