Pragmatic Capitalism

Capital for Living a More Practical Life

Long Dated Volatilities – A Regime Change?

By Matin T., Macronomics (This part 1 of a two part post)

“The cost of liberty is less than the price of repression.” – W. E. B. Du Bois, American writer

The recent significant fall in implicit volatilities  means that long dated volatilities (1 year) of most significant equity indices are now testing the frontier level between the post-crisis lows and the ultra low regime of 2004-2007.

Implicit 1 year volatility for the S and P500 (SPX) – source Bloomberg:

Could it be an attractive entry point or more simply a clear indication of regime change? We have to agree with our-good cross-asset friend that we have a hard time believing in the regime change when taking into account the fundamental macro picture. Could it simply be the broader impact of financial repression? One has to wonder.

Financial liberalization, for instance in Emerging Markets, has been a good way to attract foreign investments. It has often led to a rise in volatility given investors had been reaping in the process higher daily return rates. When equity market becomes more open, there are increases in stock return volatility (on the subject see the study realised by Vuong Thanh Long, Department of Economic Development and Policies at the Vietnam Development Forum – Tokyo Presentation – August 2007).

Regime switches also lead to potentially large consequences for investors’ optimal portfolio choice, hence the importance of the subject.

In relation to Regime Changes and Financial Markets, Andrew Ang, from Columbia University and NBER, and Allan Timmermann from the University of California, San Diego, made an interesting study in June 2011-

Regime Changes and Financial Markets:

“When applied to financial series, regimes identified by econometric methods often correspond to different periods in regulation, policy, and other secular changes. For example, interest rate behavior markedly changed from 1979 through 1982, during which the Federal Reserve changed its operating procedure to targeting monetary aggregates. Other regimes identified in interest rates correspond to the tenure of different Federal Reserve Chairs (see, for example, Sims and Zha, 2006). In equities, different regimes correspond to periods of high and low volatility, and long bull and bear market periods. Thus, regime switching models can match narrative stories of changing fundamentals that sometimes can only be interpreted ex post, but in a way that can be used for ex-ante real-time forecasting, optimal portfolio choice, and other economic applications.

Second, regime switching models parsimouniously capture stylized behavior of many financial series including fat tails, persistently occurring periods of turbulence followed by periods of low volatility (ARCH effects), skewness, and time-varying correlations. By appropriately mixing conditional normal (or other types of) distributions, large amounts of non-linear effects can be generated. Even when the true model is unknown, regime switching models can provide a good approximation for more complicated processes driving security returns. Regime switching models also nest as a special case jump models, since a jump is a regime which is immediately exited next period and, when the number of regime is large, the dynamics of a regime switching model approximates the behavior of time-varying parameter models where the continuous state space of the parameter is appropriately discretized.

Finally, another attractive feature of regime switching models is that they are able to capture nonlinear stylized dynamics of asset returns in a framework based on linear specifications, or conditionally normal or log-normal distributions, within a regime. This makes asset pricing under regime switching analytically tractable. In particular, regimes introduced into linear asset pricing models can often be solved in closed form because conditional on the underlying regime, normality (or log-normality) is recovered. This makes incorporating regime dynamics in affine models straight forward.

The notion of regimes is closely linked to the familiar concept of good and bad states or states with low versus high risk, but surprising and somewhat counterintuitive results can be obtained from equilibrium asset pricing models with regime changes. Conventional linear asset pricing models imply a positive and monotonic risk-return relation (e.g., Merton, 1973). In contrast, changes between discrete regimes with different consumption growth rates can lead to increasing, decreasing, flat or non-monotonic risk/return relations as shown by, e.g., Backus and Gregory (1993), Whitelaw (2000), and Ang and Liu (2007). Intuitively, non-monotonic patterns arise because “good” and “bad” regimes, characterized by high and low growth in fundamentals and asset price levels, respectively, may also be associated with higher uncertainty about future prospects than more stable, “normal” regimes which are likely to last longer. The possibility of switching across regimes, even if it occurs relatively rarely, induces an important additional source of uncertainty that investors want to hedge against. Inverse risk-return trade-offs can result in some regimes because the market portfolio hedges against adverse future consumption shocks even though the level of uncertainty (return volatility) is high in these regimes. Further non-linearities can be generated as a result of investors’ learning about unobserved regimes.”

As highlighted above, the importance of regime change is paramount to asset allocation given:

“-The relation between the investor horizon of a buy-and-hold strategy and the optimal portfolio varies considerably from one regime to the other. 
-For example, in a bear regime, stocks are less favored and short-term investors allocate a smaller part of their portfolio to stocks.* 
-On the contrary, in the longer run, there is a high probability to switch to a better regime and long-term investors dedicate a larger part of their portfolio to stocks. 
-In a bear regime the share allocated to stocks increases with the investor’s horizon.”
– source The Princeton Club of New-York, 27th of April 2012, EDHEC-PRINCETON Institutional Money Management Conference.

*Hence the reason why retail investors have been net sellers of stocks since 2007.
“Since they started selling in April 2007, eight months before the start of the Great Recession, individual investors have pulled at least $380 billion from U.S. stock funds, a category that includes both mutual funds and exchange-traded funds, according to estimates by the AP. That is the equivalent of all the money they put into the market in the previous five years.” (“AP IMPACT: Ordinary folks losing faith in stocks”, AP News)

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