I note comments by El-Erian over Central Bank’s inability to suppress volatility as a bigger risk than China and I would agree although I would qualify this in terms of the immediate asset price risk. Asset focussed money supply growth and asset price relatives (relative to GDP growth and income growth as well as its distribution) are all deeply negative for asset markets when liquidity dynamics, amongst others, change.
Recent commentary by Zero Hedge on the winding up of Nevsky Capital is also worth reading: the Nevsky Capital report suggested that a disciplined structure can no longer be counted on to realistically manage risk and return given the uncertainty of an increasingly skewed distribution of possible outcomes in an environment worryingly distanced from fundamentals/exposed to unconventional monetary policy; liquidity dynamics in the market place also impacted.
Another interesting piece of data shows the 10 year rolling returns on commodities that I found in a tweet from @zatapatique.
I have written on the issues of excess asset focused money supply and liquidity for some time (relevant posts of mine).
Many portfolio management structures depend on expected return/correlation/standard deviation assumptions that would be very much exposed to a break in the direction of money flows towards assets. All statistical measures of risk and co variance are drawn from the impact of monetary demand flows for assets. In an environment where monetary policy is accentuating flows to asset classes as well as expanding the quantity of money and reducing the supply of certain asset classes the natural flow response to risk and return in the environment are muted. As unconventional monetary policy recedes, additions to the quantity of asset focussed money and interest rate support reverses, the natural flows not only start to reassert but the prior excess flows adjust to the new environment. We get a break out of trading ranges and covariances.
This is all incredibly risk and uncertain for those dependent on traditional statistical measures of asset price sensitivities and covariances.
I have blogged on fundamental liquidity issues recently and one point that I want to bring out is that the greater the divergence between asset values and GDP and the greater the divergence between broad MS growth and GDP growth, especially in slower growth frames, the “fatter the tail of the distribution”.
Volatility at one level is a measure of the sensitivity of an asset’s price to new information, shocks to the system/de facto changes in the energy of the system. It reflects changes in demand flows for assets which can reflect changes in risk preferences and risk/return expectations. In a general equilibrium volatility is meant to be a static physical characteristic reflecting the fundamental nature of the asset and its relationships, but we do not currently have general equilibrium relationships and volatility is not a stable measure of anything.
Essentially when we have excess asset focussed money supply growth (EAFMS) amidst a slowing growth frame the “accumulated liquidity in” decisions exceed the “present value of future liquidity out” (PVLO) decisions. In a sense liquidity (at its heart a function of the relationship between asset allocation decisions and C/S/I/P decisions) becomes more sensitive to short term changes in demand flows and risk/return expectations, risk preferences and other factors. As the ratio of EAFMS to PVLO rises so does the natural volatility of the system.
Why the tail? Why not volatility at 1 standard deviation? During periods of excess monetary flows demand changes are not in totality covariance issues (ie. relative attractiveness of one asset to another) but absolute flows that suppress relative price reaction. In other words we see a fall in volatility throughout most of the distribution. All the while the system due to EAFMS/PVLO imbalances becomes more sensitive to changes in flows, preferences, expectations and shocks.
Given that the system because of its imbalances becomes more sensitive to small changes in any one factor, the bigger the divergence noted in paragraph A the greater the probability of an extreme risk event. The greater the accumulated liquidity in to PVLO the larger the tail: the risk event and its probability increase.
In reality, from a given point on, we can effectively discount the rest of the distribution in any analysis as a dynamically widening tail is merely a statistical constraint on the way we should be viewing risk. We are only exposed to the wider risk distribution if forces suppressing risk remain influential.
In a recent tweet I made the comment “Not a paradox: ratio of MS to assets & of asset prices to GDP, and hence to GDP functions C/I/S/P out of synch”.
Given the sensitivity of markets to even small changes in demand should anyone stand ready to provide liquidity at the onset of the tail of a distribution?
If a liquidity decision eventually exits, then there is a maximum amount of divergence which any given financial system can accommodate before the dynamics of the reverse flow overwhelm any attempt to keep it afloat. We must bear this in mind.
A recent article by Nouriel Roubini “The Liquidity Time Bomb”, to which the tweet responded, commented on the apparent paradox between vast amounts of financial stimulus and monetary expansion alongside a decline in market liquidity for assets.
Why do we need liquidity in the market place? There are a variety of fundamental economic reasons. Entities wishing to purchase assets (from savings out of income or from loans) in either new/existing issues, entities who may be dissaving and wish to sell assets in exchange for cash for either consumption (debt repayment) or to purchase higher yielding assets, businesses that wish to raise capital and other entities like governments that wish to borrow. That is markets function as an important medium for saving, consumption, investment and production decisions…they facilitate the allocation, pricing, accumulation (and the reverse) and transfer of capital ownership rights.