Volatility has many aspects. From a maths standpoint, it is the standard deviation of log returns. For the financial community, it is merely either a gauge of uncertainty (realised volatility) or of risk aversion (implied volatility).
Two factors should be accounted for: surprise supply provided by informational flows (unexpected earning news, exogenous events) and market vulnerability to this surprise (risk exposure, gearing levels). Market vulnerability can be assessed through the degree to which surprises may force investors to amend their positions. For instance correlated bets, short term and momentum trading should increase vulnerability and therefore volatility.
Volatility has reached extremely low levels as evidenced in Fig.1. There are two things worth noting: on the one hand, we have witnessed a simultaneous drop in the volatility of all asset classes, which is quite an unprecedented event. On the other hand, the overall pattern looks cyclical: this could mean that we are at the beginning of a new cycle that would push volatility upwards.In the following sections, we will look into the usual suspects for the decrease in volatility, and determine whether they are relevant.
This expression expresses the fact that all economic agents focus more on stability than growth at all costs. This is obviously true for companies: they have showed better inventory management and hence improved their vision of the business cycle. This is also true for Central Banks: they have added reactivity that helped managing the business cycle in a more effective way. The global economic landscape has hence shifted towards greater stability: since the 80’s, volatility in GDP and inflation data has decreased over time.
Central Banks have also increased transparency in their comments. They now say what they think about the economy and its outlook, what they will do next (hike or cut), and by how many basis points they will move short term interest rates. By doing so, they have not simply gained in credibility, but they have also carved out a substantial chunk of uncertainty from the market. This has resulted in the compression of risk premia.
Liquidity has also been very high in financial markets: equity turnover has doubled in five years in major stock markets, allowing greater flexibility in price formation. Hedge funds have provided a lot of liquidity to the market, and many of them (such as statistical arbitrageurs) have tended to firm up market levels.
Electronic trading and more accurate hedges for institutionals have also accounted for an increased stability. Lastly, international trading and widespread information (thanks to technology) enabled smoother shocks.
Liquidity has always been the dominant explanation when people did not know what actually fuelled a rally in an asset class. Yet, when equities tumbled in May 2006, liquidity levels were extremely high; it did not prevent markets from falling for a short period of time.
Financial innovation has been very vigorous lately. There has been a substantial increase in the number of products traded in credit derivatives (CDOs, CLOs) and volatility derivatives (Variance Swaps, options on Variance). This spectacular growth allowed risks to be spread across a broader community of economic actors, thus pushing volatility lower in most of the asset classes. It has also provided greater liquidity, since position taking and unwinding can now be implemented without trading cash markets. On top of that, investors have developed more efficient management techniques: they have better access to information, they hold more diversified portfolios, and above all they use a set of constraints such as stop-loss or VaR.
Financial innovation has undoubtedly brought liquidity and risk sharing. But complexity in derivatives has also caused volatility. When GM and Ford came close to bankruptcy in 2005, a lot of people had put on some relative value trades in CDO tranches (long equity, short mezzanine). No one had identified any correlation risk, and this situation turned out to cause substantial damage to the investors’ community. Risk sharing has been very helpful for the financial system, but the recent developments of the Bear Stearns funds showed that trouble is not far when leveraging of a risky trade has been undertaken.
VaR and stop-loss constraints, even if they have significantly reduced risks, might also cause volatility to rise if they are triggered massively. This is particularly true when most investors overcrowd the same trade. For instance, a very popular idea lately has been to sell short-dated Variance Swaps: as implied volatility trades to a premium over realised, this trade allowed people to benefit from an interesting carry. A lot of trouble came when markets collapsed in May 2006, and realised volatilities finally dramatically took up.
A useful way to assess the magnitude of unexpected events is to look at JP Morgan Economic Activity Data Surprises Index (EASI) that tracks the net percentage of releases (over a chosen set of US economic activity data) that have come out one standard deviation from consensus. Interestingly, this index does not show any particular sign of decrease in the number of surprises hitting the market.
The rise in corporate profitability (ROE and earnings expectations) has also played a big role in the volatility drop: volatility has nearly always been negatively correlated to expected growth. It is indeed fairly easy to predict results to come in a booming environment than in a recessive one.
It is for this particular reason and because companies communicate more efficiently that the dispersion of analysts’ forecasts is so low: everyone has the same information and is therefore led to the same conclusion. Incidentally, the rating action rate, measuring the proportion of ratings subject to a change (upgrade or downgrade) over a given period, gives us comparable results: it is currently standing at very low levels.
There is also another technical reason for lower volatility: dividend rates have risen dramatically lately, and this is causing uncertainty to edge down through a reduction of duration. In other words, if a company decides to pay a dividend, the investor will feel more comfortable with the money in his pocket right now than betting on future uncertain results.
Volatility remains mainly correlated to business cycles. Let us spend a short moment on cycle theory. The first period comes right after the bubble burst which drives most of the companies into liquidity trouble: their major concern is debt reduction; at this stage, credits are slowly improving, while equities deteriorate. They then enter the second phase, where profits grow faster than debt: both credits and equities improve. In order to deliver more performance for shareholders, companies leverage their balance sheets, thus causing debt to increase faster than profits: hence a rise in equities and a widening of credit spreads. The last phase is the bubble burst: where both credit and equities get whacked.
Studies show that equity volatilities usually take off when the cycle enters its third phase (debt growing faster than profits): this is the environment we are currently witnessing. Not surprisingly, they reach their peak when the cycle hits the last phase (bubble burst). Furthermore, volatility has an asymmetric pattern as it tends to increase more sharply during recessions and revert more slowly during expansions: should it reach higher levels in the future, the increase would be brutal.
Having a look at implied volatilities could prove very meaningful as it shows market anticipations of future realised volatility. Usually implied levels are negatively correlated to equity indices. Interestingly, in 2007, this correlation has not been very obvious: implied volatilities were rising alongside equity indices, just as if fear for the future was materialising. Before this, even if volatilities were low, most of the fear aversion had translated into a higher skew (volatility for out of the money puts fairly more expensive than out of the money calls). Lastly, the upward sloping curve of the term structure shows that market makers truly believe in a significant pick up in realised volatilities.
In order to get more in-depth analysis, we have set up an in-house model. Two variables showed particular relevance when it came to building the model: earnings volatility and credit rating activity. Going further into the quantitative analysis our in-house model attempts to project S&P realised volatility over a chosen set of factors, for example: corporate earnings (IBES 1y-forecasts) volatility, yield curve’s slope (3month to 10year), Moody’s rating action rate, and leverage (interest charges/profits). This regression has a strong explanatory power (R2~82% for the 93-07 period) According to this projection; an increase in IBES earnings forecasts volatility by 1% should raise equity volatility by 3.5%. From this perspective, 12-month realised volatility in the S&P should rise by 1.5 points.
It might also be very interesting to determine an appropriate timing for the pick up in volatility. For this particular pattern, we use real interest rates as the key explanatory factor. What we have seen over the 93-95 period (when volatility had bounced roughly 30 months after the trough in real rates) may suggest that the increase in volatility may not be too far away.
At last, implied volatility is usually negatively correlated to equity indices. Fig.2 illustrates this relationship in different market conditions: two bull markets and one bear market. In 2000, we switched from the black top curve, to the red curve (lower equities associated with higher volatilities). Then we switched in 2003 to the bottom black curve (higher equities and lower volatilities). The grey line shows the current environment: it points out several attempts to switch to a new volatility regime.
As a conclusion, although strong fundamental grounds for low levels of volatility seem to exist, anempirical observation of the current environment suggests that a shift towards more volatility may be next. If this forecast turns out to be right, Dexia Volatility Opportunities will lever Dexia AM’s long dated volatility trading exposure gained in convertible arbitrage management to seize arising investments opportunities in this field
Fabien Dersy is Head of Convertible & Volatility Arbitrage at Dexia Asset Management