Market Risk in Banking

When compared to funds the risk that banks have are slightly different. Namely :

–      Their loan book (Assets) may be quite illiquid

–      Funding sources (call deposits) can be outside the banks control

Identification

We talked previously about the trading book and banking book. Assets held in the trading book are subject to capital charges for market risk. The assets help in the banking book are generally not subject to any capital charges for market risk.

It is quite common for risks to be broken up according to the risk factor – that is to say interest rates, equity markets, foreign exchange or commodity. There is also the concept of primary and secondary risks. The primary risk is deemed to be the actual price risk on the outright security. Secondary risk will be factors that are considered less important. An example would be when putting on a pair trade – the primary risk is the outright risk from the long and the short pairs. However there is a secondary risk of the spread (and correlation) between the pairs changing significantly.

Risks are also distinguished between specific and general / systematic risks. General risks that is the sensitivity of a portfolio to a benchmark or an index may seem more obvious to control. In cases where there is inadequate diversification however the specific risk factors can be as important as the general risks.

As derivatives have become more and more important they also bring on new types of risks that need to be identified. Volatility and correlation risks are very important and when not properly identified can lead to large trading losses. When looking at derivatives positions it is also quite often necessary to validate and test models that are used for the valuation of these instruments.

It should also be noted that despite the fact that positions in the banking book are not marked at fair value (mark to market), there are market risks that may be lurking. Assets that are marked at par may be worth far less and can cause significant capital problems (as seen in the 2008 and onwards credit crunch). A good example is a client with a line of credit from a bank over a given spread above Libor. It naturally follows that this line of credit will have greater likelihood of being used as the clients credit worthiness declines.

Assessment

The assessment of risks that have been identified can be done as follows:

–      Statistical models that draw heavily upon probability to model price uncertainty.

–      Models relating the prices of instruments to some key underlying market risk factors. Black Scholes is a very good example of this.

–      Risk aggregation models that look at the uncertainly of future values of financial instruments. Value at risk models try and achieve exactly this when predicting maximum loss within certain confidence intervals.

The choice of model is a delicate matter and needs to be thoroughly understood and tested before it is approved by the regulatory authorities. This fairly obvious to see why – models feed into the capital requirements and therefore need to be reasonably accurate, traders should not be able to game them (most models are open to some form of gaming).

The choice of the model should try and explain as wide a range of risk factors as possible. This may not be ideal and models are always open to debate, but in the absence of anything better , they serve to model the financial markets better than anything else we have at present.

Pricing models as discussed are very important for derivatives pricing as their prices are usually marked to model. However even for instruments such as bonds banks will often require models to be able to carry out more sophisticated analysis on how interest rate shocks and changes in the yield curve will affect the prices over time. Most models will be fairly standard and accepted by a large part of the market, and more than likely be backed up a body of academic research. The challenge is usually in the actual implementation of the model and to ensure that this is in fact correctly implemented. The other major factor that is important is to ensure that the data and market parameters that are fed into the model are accurate and representative of the underlying market dynamics. This can actually be far more challenging a task than it sounds in paper and if you put in garbage data into a model you will get garbage out.

When banks look at calculating VaR there are a lot of simplifications involved. Most banking trading books are far too dynamic to be modeled accurately. This is where a model like VaR makes a lot of assumtions and simplications to be able to calculate the maximum loss figure at a certain probability over a given time frame. This is usually calculated at the 99% confidence interval level over a 1 day horizon. This number is then used to calculate capital requirements. The VaR model will be back tested against actual P&L to ensure that the model remains valid and reasonably accurate of normal market conditions.

Control / Mitigation

Although the experience of the last few years since 2008 proves otherwise, banks are largely accepted to have the expertise , knowledge and competence to be able to manage their market risks effectively. The theory is that they can manage their risks effectively as they can control the products they are willing to offer in the medium term to their customers. In the short term they can hedge the risks that are present by using derivatives instruments. The problem of course is when these hedges themselves go wrong or the banks are offering products the don’t fully understand (again 2008 credit crisis and CDO’s).

Derivatives are an important hedging instrument. However there is a big debate about their risks. As at the end of 2012 the total outstanding gross notional size of the derivatives market was $639 trillion. This is a huge number many time the GDP of entire world. However in terms of net outstanding market value of the actual derivatives this was only $25 trillion. Break this down further and the actual credit exposure after netting was $3.7 trillion. By no means trivial but a lot more palatable than $639 trillion.

This huge explosion has been created due to the fact that derivatives are cheap to trade and offer a reasonable way to hedge risks, speculate or super charge portfolio returns. However as we saw in the 2008 credit crisis when things go wrong it can get very messy. The counterparty risk of derivatives contracts cannot be overstated. The unwinding of contracts when banks go belly up can be very dangerous and threaten to bring down the whole economic system (as nearly happened during the Lehman’s crisis). Ultimately disaster was only averted through the backstops put up by sovereign governments. Derivatives will not go away , they are here to stay that is clear. However the credit crisis has opened the way for a deeper look at OTC market and better ways of trading and controlling the risks that they present. This is still a work in progress, but expect more transparency and greater use of collateral as this market changes.

Coming back to the more micro task of risk management within a bank , the task of the risk manager is to allocate capital efficiently and optimize risk adjusted performance. This will often entail efficient allocation of capital (as defined by limits) between various divisions and desks of the bank. More capital will genrally be allocated to those divisions where the bank has some natural or historical advantage. It must also be stated that the incentives of the front office must be clearly understood – that is to say front office bonuses will be driven by their risk taking behavior. Thus any incentive scheme should also take into account the risk that ahs been taken to generate the profits.

The hedging od risks in derivatives desk are also complicated by the non-linear nature of the risks. There is also a fine balance to consider between hedging all risk and leaving some risk to actually make some rewards. The main take away point here is that hedging is definitely not a science – to a large extent it remains an art that is informed by macro views and strategies.

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