On Risk Adjusted Returns

The notion of risk adjusted returns or economic value added has long been held as the economic correct way to evaluate opportunities in any business. While the details may be complex and hard to implement in practice, the effort to develop measurement systems that cut across risk (usually in the form of capital) and return (expected or historical) is essential to preventing the near sighted herd mentality that has pushed many financial institutions in the west to the brink. The whole fiasco of high levels of remuneration in institutions which have required extensive government funding, could have largely been avoided (at least ameliorated) if remuneration had been tied to long term risk adjusted returns, not simply to absolute profits.

The Curse of Silo based risk management

Most institutions measure their risks in distinct silos. Indeed the discipline of risk management has been cursed (?) by the proliferation of specialists focusing on risks narrowly defined. The reality is that anything that can cause a change in value is a risk, and our traditional buckets, say market risk, or counterparty credit, or operational risk, are purely human conventions, and our institutions and our systems need to rise above them and think holistically about how these risks interact, particularly in times of stress.

Reconciling Tail Risks and Non Tail Risks ...Or Not.

Traditional measures of risk, most obviously sensitivities like the greeks and percentile measures like VaR, capture only selected dimensions of risk. As Nassim Taleb has memorably pointed out, they fail to capture the extreme losses (tail risks) that could derail the organization. Ironically protecting against these major losses is the whole point of risk capital. For example, both credit (Internal Ratings Based approaches) and market risk (Internal Models Approaches) and operational risk (Advanced Measurement Approaches) use the VaR concept as the definition of minimum regulatory capital. Even worse than this, is the fact that careless use of such VaR measures actually makes tail risks more likely, as traders go long exposures in the tail that have little effect on VaR, but potentially subject the institution to devastating downside. Tail risk measures such as conditional VaR and expected shortfall, do help in this regard, but the truth is, no single measure ever completely captures a complete distribution. So the only solution here is more management understanding of what distributions are about – and this means a more sophisticated management audience for risk results.

Systemic Risk - Only Connect

Systemic Risk always reminds me of one of those Rube Goldberg inventions. Take a look at this video and see what i mean...
http://www.youtube.com/watch?v=qybUFnY7Y8w&feature=youtu.be&a

On Basel III and the difficulty of self regulation

With the so-called Basel III regulations, the G20 central banks are looking to upgrade the Basel II accord in the light of the crisis of the last two years. One of the biggest outstanding issues, is the risk weights assigned to sovereign debt, which remains more or less as before. The risk weights for sovereign debt denominated in foreign currency is based on the sovereign credit rating: AAA to AA (0 per cent risk weight), A+ to A- (20 per cent risk weight), BBB+ to BBB- (50 per cent risk weight); and BB+ to B- (100 per cent risk weight).


Now given that

1) credit ratings are no longer the touchstone of fiscal probity they once were and

2) the central banks are themselves looking to raise capital in the global markets, so lower risk weights make that debt more attractive, and

3) it looks like the world will soon be faced with an impending sovereign debt crisis as government deficits become less and less sustainable.

Does this really make sense that Basel III facilitates the governments funding quite so (L)iberally?

Volatility Time and Risk Clock Speed

I have always felt that one of the best ways to manage personal risks is to simply sample less frequently. That’s why I read one news journal just once a week (the economist btw!). Risk managers need to understand what they are trying to manage, and it seems to me that this has multiple levels (like Kondratief cycles) First technology, demographics, geopolitics all these things are changing slowly over time say over years. Then real economics, business cycles, demand, supply are changing over say months or quarters. Market prices are changing almost instantaneously. When a risk manager manages in response to price changes, is he concerned with the price change in itself, or as a reflection of some more fundamental change. My concern with the concept of volatility time is that most of the moves in the market are merely noise carrying no real additional information. If our need is to respond to market changes in themselves then volatility time makes some sense. However if our focus is the information provided in those market moves then faster sampling really adds no value, particularly given the time and costs of processing the information to make an informed decision. I am sure there’s a great paper in this somewhere!

The Limits of Continuous Finance, Network Risk and the Prisoners’ Dilemma

Modern notions of Market Risk are based on the notion of stochastic calculus which is essentially atheoretical about the nature of the shocks that cause changes in asset prices. For example the whole notion of derivatives pricing using techniques such Ito’s Lemma is based on essentially continuous or discrete random changes in asset prices. Enter the Crisis of 2008/2009. The limitations of such models is made clear as the structural relationships between banks comes to the fore in deciding market trading. We are bank A – do we trade with Bank B? We may have exposure to subprime assets through CDOs and other securitized assets. We don’t know how big that exposure is. Bank B may have exposure to subprime assets. B may have exposures to Banks C and D…None of them know what their exposure is. If we transact with Bank B, we take on counterparty risk, which may be much increased by this unknown exposure (credit ratings don’t help much here). Better is to take government money and not take that exposure. Hence interbank funding dries up, and credit spreads rise dramatically. Transaction/Partnering with other members of your network simply becomes too expensive and too risky. This is much like the classic “Prisoners Dilemma” problem where risk aversion and ignorance about others actions produce a suboptimal solution.
What was consequence of this dilemma? High counterparty exposures allow defaults to propagate through the network of counterparties, one by one, the effects increasing as too big/interconnected to fail (TBTF) institutions’ failure are amplified into a failure of the network as a system. Not surprisingly the regulatory response is one of “macroprudential and systemic risk management” and of course call for break up of institutions whose individual failure could cause system wide problems (e.g., the Volcker Rule etc). But let’s go back, it follows that truly understanding systemic effects requires transparency of the underlying exposures (much like having the prisoners in the prisoners’ dilemma be able to communicate). This is one reason (another is reduced settlement risks) for the rise of centralized clearing (CCP), where one entity has transparency into the network and is able to interject using margin requirements, capital injections when the network looks vulnerable. Suddenly risk management for CCPs becomes critical to managing systemic risk for the entire network.