Manufacturing and Managing Customer-Driven Derivatives. Qu Dong

Figure 2.1 Key Pillars of Structured Derivatives Business

      The pillars consist of:

      • Trading: It is much more than just buying and selling. It is about understanding the risk characteristics of derivative products and hedging the risks. Risk management should be part of its DNA. In the process of risk managing the positions, trading is putting fingers onto the market pulses and making appropriate hedging decisions accordingly.

      • Hedging derivatives requires deep understanding of potential pitfalls. “Gamma trap” is a classic example. To hedge short gamma and short volatility positions, traders have to buy the underlying when it goes up and sell when it goes down. If volatility suddenly spikes, the underlying will move rapidly against them and the dynamic hedging can exacerbate the underlying movement, in particular during the downward spiral. This type of “chasing own tails” hedging can lead to sharp V-shaped underlying movement known as “Gamma trap”, which can amplify losses of the short gamma positions. If large numbers of institutions hold similar short gamma positions and perform similar hedging, the “Gamma trap” can cause wider market distortion and stress, as observed many times in the past.

      • Quanting: Quantitative modelling is the core part of derivative products development. In the modern day and age, the models need to be consistent front to back for pricing and risks, to ensure consistency and transparency across the value chain. Quants should play a driving role in the development of trading and risk infrastructures;

      • Marketing: This client-facing function includes structuring and sales. Client-driven product design, distribution process and channels are constantly evolving. Clients and market feedbacks play important roles in the new products development and manufacture process.

      • Risk controlling: This is a four-eyes principle-based independent risk management function that should work with the front office functions very closely, to identify and control risks including market, credit and operational risks.

      • Trading/pricing/risk systems underpin day-to-day derivative activities. It is actually scandalous that the derivative industry has wasted many billions on IT systems spending due to lack of integrated vision and lack of coherent business and technological management.

      The pillars illustrated in Figure 2.1 are fundamental to the structured derivatives business. The whole business value chain needs to function efficiently and coherently, to ensure an effective and safe business. The widely acclaimed IPD (Integrated Product Development) management philosophy can and should apply to the derivative products development, taking into account the business as a whole.

      Model and Product Development Process

      Financial derivative products are not tangible, and ultimately they are based on models. Quantitative analysts (quants) must be a risk-conscious business group. Its roles encompass developing derivative pricing and hedging models, providing quantitative supports, formulating and developing derivatives model-related trading and risk systems. Quants should be one of the drivers along the industrialized production line for derivatives.

      Derivative pricing models are vital in the structured derivatives and risk management business. Many client-driven derivative products have no direct traded markets for bench-marking, and they will have to be marked to the models, although the vanilla markets are used for calibration. In such a (de facto) marking-to-model business environment, the quality of the models is paramount, as it not only impacts P&L, but also the day-to-day hedging and risk managing activities. Banks must establish and standardize a process for developing quality pricing and hedging models, as a key part of the efficient and reliable production line which can also minimize the model risks.

      Principles of Model and Product Development

      Model specification, its numerical implementation and development testing need to follow a number of critical principles. Independent model validation is also an essential part of the model and product development process.

      Model Specification

      Speculation is human, hedge is divine. The central part of the non-arbitrage derivative pricing framework rests on the divine principle of hedge. The model specification must comply with this general framework. The model mathematical formulation, scope, applicability range and any limitations should be clearly specified. Any model assumptions deviating from those defined in the model framework should be thoroughly assessed with the business. Assumptions and potential implications of hedging should be explicitly explained. The bank must seek to eliminate or minimize the model mis-specification risks at source.

      It is very important that the models are specified and implemented as close as possible to the real world, and they are suitable for day-to-day business usage. Quants should be aware of the common and best market practices, remembering that the models are not only used for pricing, but also for risk analysis and hedging. As a general guideline, good model specifications aim to achieve the following qualities:

      • capturing market risks which matter from a hedging perspective;

      • calibrating reliably to the markets to enable reliable hedging;

      • numerical stability for pricing and computing risk sensitivities (Greeks);

      • computationally efficient for front office pricing as well as downstream risk calculations.

      Model Implementation Process

      Model implementation is an interactive process among quants, traders, IT and risk managers. It entails the following stages:

      • Quants develop pricing models including all the necessary calibration routines in a quant library. It is vital that the quant library is structurally well-designed and object-oriented.

      • Quants, working with IT, develop system and user interfaces for the trading and risk systems.

      • Quants conduct model development testing to examine the validity and implementation of the model. The model test scopes as well as the results should be documented.

      • IT develops the downstream applications, including the relevant Risk and Back Office requirements.

      • Risk conducts independent model validation.

A typical model/product implementation flow chart is shown in Figure 2.2.

Figure 2.2 Typical model/product implementation flow chart

      Note that the model trading/risk system integration should be accomplished during the model development stage as a parallel task, rather than after. This is because most of the model integration and interfacing works are not specific to a particular model. In a well-designed object-oriented quant library, the permitted parallel approach can greatly enhance the overall model and product development efficiency.

      Model Testing

      Quants model testing is to ensure that the model is implemented properly. It is aimed to minimize the implementation risks, which constitute a very large portion of model risks occurred in real life. Model testing should include development testing and system testing.

      Development testing checks the fundamental mathematical and numerical implementation. Whenever possible, an alternative model should be developed for comparison purposes. The comparison between the models can reveal differences and deficiencies. Differences should be thoroughly examined and understood; some are due to legitimate differences in numerical methods and some due to implementation bugs.

      Model system testing is to ensure that the implemented model performs as expected in the production environment. The testing within the trading and risk systems enables an assessment on model's pricing stability and its capability in generating sensible risk sensitivities, for hedging over a wide range of real market data at portfolio level. It is beneficial to run system