No stress in the markets, but it has a way of sneaking up on investors

Following financial stress indicators is a good way of cutting through the verbal rhetoric and watching what markets are really doing. The current reading shows a low stress environment, not as low as a year ago before the pandemic, but significantly below spring levels. Stress measures have been stable for the last two months.

There are financial stress indicators from brokerage firms and banks. Additionally, they are available from Fed banks and other government institutions. The Office of Financial Research (OFR) calculates a daily financial stress index (FSI) using 33 financial variables divided into five categories: credit, equity valuation, funding, safe assets, and volatility. The variables are all publicly available and the categories values are also published. This FSI also shows stress by region. The index is zero when the average is zero.

The credit category focuses on corporate spread levels which have stabilized since spring. The equity valuation is centered on price to book ratios. The funding section has seven measures of short-term dislocations like 3-month LIBOR-OIS spreads. The safe asset category focuses on demand for assets like Treasury bonds, gold, the dollar, and yen exchange rate. The volatility category includes volatility measures in stock, bonds, currencies, and commodities. 

While the current index levels are showing below average stress, a close look at historical data is that stress indicators shift quickly with limited advanced warning. A switch from negative to positive values should be a concern. A decline in stress is usually associated with Fed action to directly offer stabilization. For the near-term, investors should focus on near-term stress from election concerns and not poll numbers or talking heads. 

 

CSFB Fear Barometer - Why is fear stable?

 

There have been significant discussions about fear and uncertainty in the current marketplace, but it seems that to get a true idea of fear is to look at what is going on with market prices. Talk is cheap. Following action is more valuable. 

An innovative approach to measuring the action from fear is through the CSFB Fear Index which looks at the pricing of a zero cost collar on the SPX index. The index measures the premium of a 10% out of the money call and then finds percentage out of the money strike for a put on the SPX index that would make the combination a zero cost collar. 

If there is more fear, then an investor would only be able to buy a put further out of the money with the call premium. The fear index value would go higher. If the index falls, this fear barometer is telling the market that you can buy protection closer to at the money with the premium from the call writing. Since the index is pricing a zero cost collar, this index will account for any skew in option prices.

The current index levels are surprisingly stable over the last month in spite of the market sell-off. Even with election uncertainty, the fear index has been stable. The index is much lower than summer levels. The maximum fear was in February before the March liquidity crisis. Fear was actually at its lowest levels in March.

The long-term index shows that fear has declined from highs in 2016. The index is still elevated versus pre-GFC, yet the trend has been lower. It is interesting that fear has been growing or high for most of the period of equity gains. Fear and price moves have an interesting link that does require further study. Given the complexity of the relationship between equity index returns and fear, this index has not been given much attention, yet it provides a unique assessment of market opinion. 

Currently, the option collar is saying we have less need for worry. It is unclear why options have this relatively higher optimism.

Thinking through clustering for a different but clearer perspective

There are a couple of visuals that are always used in the money manager's toolbox. One tool for quick comparison is the scatter plot of return and risk across different asset classes. Some analysts will get more sophisticated with this visual by looking at how asset classes have moved through time in risk and return space. Some with better visual dexterity will look at three dimensions and include correlation. 

The visual information of risk and correlation is computed and collected in the covariance matrix which is a core component of any optimization. Unfortunately, the covariance matrix can be difficult to work with as more correlated assets are added to the matrix or if there is instability in covariance through time. The impact of covariance sensitivity is less intuitive on risk measurement and asset weight selection but is a critical part of asset allocation. Any optimization is sensitive to multi-collinearity and the difficulty of inversion. If there are more assets that have similar covariance, small changes in statistical characteristics will lead to significant changes in optimized weights; the optimized asset allocation is unstable. 

A tool that can be useful and that is easily visual for money managers is cluster analysis through the use of principal component factorization. Principal component analysis is a data dimensionality reduction tool. It looks for similarity across data or common feature extraction. By eliminating common features, we can find uniqueness. By grouping common features, we can find clusters which can be graphically displayed. 

When assets cluster around common factors or have similar covariance characteristics, asset allocation becomes more difficult. One, there is less diversification benefit. A cluster of assets around principal components will not offer any benefit to investors. Portfolio risk is not diminished. Two, the covariance matrix becomes less stable and optimization becomes harder. The asset allocation will become sensitive to small changes in the price behavior of any asset in the portfolio.

 

There are quantitative methods for finding clusters and adjusting the covariance matrix, but a first past is to focus on the intuition of cluster analysis. No different than any good data work, plotting the information is a critical first step for analysis. 

As shown in the graph above, there are some well-defined asset clusters and some assets that are unique. Assets in the clusters are going to add little value to the portfolio. Optimization across those clustered assets will shown unstable weights. Assets outside clusters will have strong diversification benefit. Assets outside clusters are more important to a portfolio and should be a place of focused investment effort. Look for commonality of factors and find uniqueness. This process of combination for commonality and uniqueness will always be rewarded.  

CBOE Eurekahedge Tail Risk Hedge Fund Index - A costly downside hedge

 So, what is the price of tail risk investing? Using the CBOE Eurekahedge Tail Risk Index since 2008 as a benchmark shows the cost. The index of leading tail hedge managers would have generated an annual drain of 2.65 percent per year. It is not clear whether this is high or low until you consider the impact on an equity portfolio. 

We compared the SPY returns versus a simple 95/5 allocation between SPY and the Tail Risk Index for the entire history of Tail Risk Index from the end of 2007 through August 2020. The results show that there is no increase in terminal wealth from holding the index as a hedge. There is a slight decrease in annualized return and volatility with the Sharpe ratio only increasing .01. The "insurance" helped during crisis periods, but then the drag continued. Unfortunately, the support was limited given a 5 percent allocation. 

We then compared the stock index and stock/Tail risk portfolio against a 95/5 allocation between SPY and AGG the Barclay Aggregate Index ETF. In this case, the return drag was significantly less than the SPY/Tail Risk combination. The volatility was higher than the SPY/Tail risk combination but lower than the 100% SPY portfolio. 

The AGG has a zero correlation with the SPY benchmark while the correlation of the Tail Risk index was -.48. The volatility of the AGG was less than a quarter of the Tail Risk index. Holding the Tail Risk index provides better diversification because of the negative correlation but at a significant cost. Tail Risk investing as generated through the managers in the CBOE Eurekahedge index is no free lunch and there may be better ways to attack this problem. 

In the case of house insurance, the value is clear; a house destroyed is replaced. It cannot be replaced on its own without the insurance or a new capital infusion. In markets, the stock portfolio will likely increase in value after a recession as long as the firms invested in do not go bankrupt. Needless to say, this is a critical assumption. Insurance is not needed for the long-run but for protecting or smoothing short-term consumption risk. If you have to consume more wealth during a crisis, the hedge adds value. If you are only worried about terminal wealth, the positive impact of a small hedge is limited.

A tail hedge investment has to be considered against a number of factors like the terminal wealth horizon, the size of the hedge, any return or yield drag, and the diversification alternatives.