If you are a quant, you are a mathematician with an advanced degree in mathematics or physics. Your work is based on both academic research and professional, peer-reviewed publications. You take your inputs from them, apply your own formidable intelligence to come up with a stochastic pricing model that you think will work exceptionally well for a class of products. You will also need the details of the products. Your output is, of course, a pricing model of your own, or an implementation of a pricing model from the literature. This is your primary work unit.

In order to make use of this pricing model, it will have to be validated. Then a set of products using the pricing model will be defined and submitted for approval. Once approved, with the help of trade inputs and market data, each of the products can be priced and booked into the trading platform. But such activities are outside the sphere of interest and influence of the quant. To them, how a product is instantiated into a trade is pretty irrelevant and trivial. It is merely a question of specifying the trade and market inputs to the pricing model. Even how various products are derived is mechanical, and all the “real” work is done in the pricing model.

This perspective, though accurate and functional for a quant, is pretty far removed from the view of the rest of the bank, which is why quants sometimes have the dubious reputation of being out of touch with the industry. The point is not so much that they have to change their perspective, but they should appreciate that there are other equally valid perspectives held by other business units they interact with, and make an effort to know them.

The last section of this post series is on trade perspectives. In fact, our earlier sections on the static structure of the bank and the temporal evolution of the trade have been in preparation to this last section. In the next couple of posts, we will see how the quants, quantitative developers and the middle office professionals (and the rest) see trades and trading activity. Their views are important and need to be accommodated in the design philosophy of any trading platform.

Where do these perspectives come from and why do we need to know about them? Trade perspectives are based on the work paradigm specific to each business unit. Because of what aspect of the trading activity a group focuses on, they evolve a paradigm, or a mental model, that works best for them.

In order to understand, let’s take a look at how we work with a modern personal computer. The paradigm we are presented with is one of a desk and a filing cabinet. So we have a desktop, folders and files. They have become so natural for us now that we cannot imagine another way of interacting with a computer at all. The Internet, on the other hand, is built on a paradigm of something that hovers over us, which is why we “down”-load stuff from it and “up”-load stuff into it. But the programmers and architects who develop such paradigms often do work with different and less known paradigms as well; for instance we have ports and sockets and streams and so on.

If we do not appreciate the work paradigm, we will find the jargon that comes with it mysterious and incomprehensible. This is especially true if we are to work on projects that cut across multiple business units with differing paradigms.

To illustrate it further with an example from our trading world, let’s look at how we identify a trade. The quants really do not care about the trade identification number; for them, it is the pricing model that is the basic unit that they work with. The quantitative developer, on the other hand, would like the identifier to be something unique per trade. A structurer would like to have one identifying reference for the trade with possible sub IDs for the individual sub trades that make up a structure. While this requirement is easy enough to implement, the software architecture also has to cater to trade cancellation and amendment requirements from Front Office and Middle Office. What happens when a structure is modified or canceled? How do we find and deal withall the related trades? This problem will almost invariably ends up requiring a link ID in the database. Trade number amendments on a live deal create problem for documentation and operations staff as well, who might demand another immutable external reference number attached each trade. Audit will require integrity and indelibility on everything, demanding database record duplication. As we can see, the perspectives and work paradigms of each business unit translate to often conflicting requirements on the program design at a fundamental level. It is for this reason that we will take close look at the trade perspectives in the following posts of this series.

With that we have come to the end of our discussion on trade lifecycle. We talked about pre-trade activities such as the quant work on pricing model, and its validation by an independent team. On a per-trade basis, we have the sales and credit check activities. Once a trade is initiated, it goes through the initial validation work by Middle Office, followed by regular processing by a large number of teams, such as Market Risk Management for limits monitoring and reporting, Product Control for valuation checks and reserve calculations, and trading desks for hedge rebalancing and risk management. During the termination phase of the life of a trade, it is the back office teams that are active in settlements and reporting.

As we saw, most of the activities, especially during the inception and up to the termination of a trade, the trading platform plays a crucial role in mediating the processes and conveying the trade from one business unit to the next. But these different business units work with their own entrenched work paradigms, and their perspective on what a trade is or what their work involves can be radically different from one another. Since the trading platform cuts across multiple teams, it has to cater to these differing perspectives, which is the last section of this series starting the next post.

The last lifecycle event of a trade is, of course, its termination. It can be triggered for a variety of reasons. Whatever the reason may be, when a trade is terminated, it calls for settlements and documentation archival by Back Office. In addition, it may trigger public disclosures (in an aggregate form) by Finance, and incentive adjustments by Human Resources.

The common reasons for trade termination and the workflow it triggers are depicted in the figure below.

• Trade Maturity: When a trade or an option reaches maturity, it gets terminated, which is the most uneventful mode of trade termination.
• Option Exercises: If the bank or its counterparty exercises an option, it gets terminated. Exercises can take place any time during the lifetime of a trade, or only on specific dates, depending on the termsheet description of the product involved.
• Barrier Breaches: Barrier options (or knock-in and knock-out options) may breach the pre-defined barriers and may get terminated generating settlements or new trades.
• Target Triggers: Instruments that accumulate toward a target (such as range accruals or target redemption forwards) may get terminated when the target is reached.
• Trade Novation: Novation is the special process by which the trade counterparty changes. In effect, the original counterparty sells the the trade or the option to another one. When a novation happens, the original trade is terminated and a new one initiated with special characteristics.

Once a trade is booked into the trading platform database, it triggers a whole chorus of validation and daily processing. The validation process is a to-and-fro dance between the trading desks in Front Office and the control units in Middle Office, all mediated by the trading platform. The traders may insert a trade on an experimental basis. Once they are convinced that it is a viable trade, they push it to a confirmed state, which will be picked up by the treasury control unit. If the traders decide to discard the trade, the trade ends up in the trash pile (but never deleted permanently). The control unit typically works in a four-eye, double validation mode. They verify the trade inputs, and control limits such as the number of trades allowed for a particular product. If the trade passes their tests, they set its status to a validated state, which triggers a second level of checking. If the trade fails either level, they are pushed back into a state that allows the traders to either amend it or discard it.

Once the trade is fully validated, the processing part begins. It involves multiple teams and multiple perspectives, starting from how a trade should be identified to what the basic information unit that should be identified.

As shown in the figure above, regular processing takes place in various business units.

• Trading Desks monitor trades for hedging and rebalancing, monitoring profit and loss (P/L), and staying within the risk limits. The senior traders get distilled information from the junior ones through this regular processing and take appropriate actions.
• Middle Office plays a crucial role in regular process. They monitor target and barrier breaches, rate fixings and option exercises, cash flow generation, and spawning other cash trades. They generate (with the help of the trading platform) appropriate accounting triggers for Back Office to act on, in order to perform settlements, trade confirmation, documentation archival etc.
• Product Control is another business unit embedded within the middle office that actively monitor the P/L on a daily basis, with a view to explaining their movements based on the sensitivities and market movements, providing an independent computation of the profitability of the trading activity. Their computations of reserves feed into the finance and human resources departments and affect trader incentives and compensation.
• Market Risk Management also has hordes of staff employed to perform daily monitoring of trading limits (such as notionals, delta-equivalents etc.) as well as VaR computation, Stress VaR tests. In most banks, they also handle compliance reporting to regulatory authorities and provide concise and actionable intelligence to the upper management who decide the trading strategies.

As we shall soon see, the different and specific focus of each business unit demands a unique projection (which we will call a perspective) of the trading information from the trading platform. This requirement is one of the things that make its design and implementation so challenging.

The inception events of a trade can be classified into two categories. The pre-trade activities are those that have to take place even before the first trade is booked. The per-trade inception activities are the ones specific to each trade.

The pre-trade activities are related to new product on-boarding and approval. As we saw, in-house trading platforms are designed to be nimble and responsive. In principle, it should take little time for a new product to be on-boarded. The last system I worked on, for instance, was designed to deploy a new product idea in a matter of minutes. But the architects of such systems tend to forget the human, process-related and control elements involved in it. As the slide above illustrates, a new product idea or a new pricing model originates from the work of a model quant or a structurer in Front Office. But before it gets anywhere near a production system, the pricing model needs to be validated, typically by the analytics team in the Middle Office risk management group. Once validated, the product goes through a tortuous approval process that may take weeks or months, and then a formal deployment process, which may again take weeks or months. When that process is completed, the product is available for trading in the trading platform.

Once available, the product can be instantiated as a trade. Each trade instance goes through its own validation and approval process. The trade request may originate from the sales or structuring team in Front Office. They will also prepare the term sheet and other legal documents. Once these tasks are completed, a trade is booked into the trading platform.

These inception events are depicted in the second slide above. One of the crucial steps in the approval process is the credit control. As we described earlier, the credit risk management team uses a variety of tools to assess the risks involved. With their approval, and with the traders understanding of the market price of the product, a product available in the trading platform becomes a trade in the database. And the lifecycling fun begins.

With the last post, we have reached the end of the second section on the static structure of the bank involved in trading activities. But a trade by itself is a dynamic entity. In this third section, we will look at the evolution of a trade, and see how it flows back and forth between the various business units we described in the last section. We will make the this section and the next into a new series of posts because the first series (on How Does a Bank Work?) has become a bit too long.

As with most dynamic entities, trades also have the three lifecycle stages of inception, existence and termination. What we need to understand clearly is what the processes are around these general stages. What are the business units involved at each of these stages? What do they do? And how do they do it?

We will see that from our perspective, the lifecycle interactions are all mediated by the trading platform. It is not so much because everything is contained within the trading platform, but because we are interested only in that limited set of processes that are. In some sense, the last section was about the physical, spatial description of the bank, and this section is going to be on the temporal evolution and dynamics of how things work on that structure.

We have now completed our discussion on the general structure of a typical investment bank trading arm. We went through the Front-Middle-Back Office divisions and the functional and business units contained within. Note that we looked only at those units that have a bearing on trading and quantitative development activities. Note also that this structure is fluid and may be implemented with different names and hierarchies in different banks depending on their corporate strategies and focus. We presented the trading platform as the enabler or backdrop of most of these activities of the global treasury (where exotics trading activities take place) and the associated business units (that handle various aspects of the trade workflow) mainly because we are looking at the whole thing from the quantitative development perspective.

From this perspective, you see the trading platform as the most important tool (or collection of tools) in the bank. It mediates almost all the interactions among the various business units. Furthermore, as we shall see in future posts, the trading platform defines the trade workflow and lifecycle management. Therefore, it will also become important for the quantitative developers to understand how these business units view trades and the trade booking and management process. Their trade perspectives will have to influence the design of the trading platform.

From the quant and quantitative development perspective, Back Office is a distant entity. Their role is vital in the trade lifecycle, as we shall see later, but they are outside the sphere of influence of the quants and developers.

Back Office concerns itself mainly with trade settlements and accounting. Upon maturity, each trade generates a settlement trigger usually with the help of a vended trading or settlement platform, which will be picked up and acted upon by the Back Office professionals. They also take care of cash and collateral management.

Finance functions are closely related to Back Office operations. Among a host of accounting related operations, they have one critically important task, which is to produce annual reports. These reports get publicly scrutinized and determine everything from the stock price to performance bonuses, salary levels etc. Finance professionals may require quant and analytic help for certain tasks. In one of my previous roles, I was asked to estimate the fair market value of the employee stock options (ESOP) for the purpose of accounting for them in the annual reports.

The process of pricing ESOP is similar to (although a bit more complicated than) normal call option pricing. Among other things, you need the volatility of the underlying stock in order to calculate the price. I used the standard exponentially weighted moving average method to estimate it from the published stock prices over the previous two years or so to compute it because that was all the data I had access to. Before that time, there was some corporate action and stock ticker name had changed (or did not exist, I don’t remember which). In any case, I knew that the impact of adding more data prior to that date would be negligible because of the exponentially diminishing weights; it would be much less that the round off error in quoting the price to four decimal places, for instance. But the accountant who was asked to look at the computation was upset. She came to me with her rulebook and referred me to page 57, paragraph 2, where it was specified that I was supposed to use ten years for the EWMA computation. I tried, in vain, to explain to her that I couldn’t. She kept saying, “Yeah, but page 57, para 2….” I went on to explain why it didn’t really make any difference. She said, “Yeah, but page 57, para 2….”

Accountants and Finance professionals can be that way. They can be a bit “technical” about such things. In hindsight, I guess I was being naive. I could have just used a series of zeros to back-populate the missing eight years of data (after all, if the ticker price was not quoted, it is zero), and redone my ESOP valuation, which would have given an ESOP price identical to what I computed earlier, but this time satisfying both Finance and the quants.

A team which quantitative developers work closely with is Information Technology. They are charged with the IT infrastructure, security, networking, procurement, licensing and everything else related to computing. In fact, quantitative development is, as I portrayed it earlier, a middle layer between IT and pure mathematical work. So it is possible for quantitative developers to find themselves under the IT hierarchy, although it doesn’t work to their advantage. Information Technology is a cost center, as are all other Middle and Back Office functions, while Front Office units connected to trading are profit centers. Profit generators get compensated far better than others, and it is better to be associated with them than IT.