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.

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.

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.

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.

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.

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 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.

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.

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.

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 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.

Those in sales tend to be personable, extroverted and persuasive people. A good salesman can sell a refrigerator to an eskimo, I’m told. IN the context of the front office in the global treasury or global markets, sales people are tasked with sniffing out trading needs from their customers and offer compelling products from the trading and structuring desks to fulfill them.

The life of a sales professional is a tough one. They need to meet progressively more aggressive targets in sales volumes to stay in the game. The moment they meet the target one year, it jumps the next year to an even more unattainable level. The sales staff, therefor, find themselves under constant (or even increasing) pressure. Knowledge of mathematical finance, while useful, is not a pre-requisite for sales jobs. In fact, mathematically inclined folks tend to be reserved and introverted, and tend not to have the qualities that make for a good salesman. In other words, if you are a quant, your most ideal role is not in sales, although the structuring side may offer some interesting opportunities.