US Macro shocks and style factors - Important in explaining EM currency moves

 

Emerging market currencies are very much related to the behavior of the US economy. This link is well-known, but the timing of market reactions is often not well-connected. The authors of the recent paper, "Emerging Markets Currency Factors and US High-frequency Macroeconomic Shocks" solve this problem by using higher frequency market data to proxy for growth and money shocks in the US. Their study finds that 40% of the time series variation in an EM currency basket is related to changes in macro shocks or risk premium in the US which is more than double the impact seen with developed markets. 

The paper measures these macro relationships through decomposing the behavior of US stock and bond markets. The financial markets when looked at in concert can decompose drivers of macro shocks into two categories fundamental unanticipated shocks to growth and money and the risk premium of growth and money. 

Instead of using fundamental data which is often delayed, subject to revision, and not produced with any frequency, the authors look at relationships with stock and bond moves. For example, if there is a positive shock to both stock and bonds, markets are signaling a growth shock especially if the bond reaction is in the front-end of the yield curve. If the markets are moving in opposite directions, the markets are signaling a positive money shock which will form expectations of a future tightening. 

The general trends of stocks and bonds moving together or in opposite directions also provide signals on the market risk premium for growth and money, respectively called a common or hedge risk premium. In this case, it is critical to look at the relative move of the back end of the yield curve.

The authors also construct style factor portfolios for emerging market currencies (carry, momentum, macro momentum, value, and high dollar beta) and find that all have exposure to high frequency US macro shocks, but when combined in a portfolio, the macro exposure can eliminated. The authors also find that carry and macro momentum style factor portfolios create positive alpha. 

The conclusion is that emerging markets are US macro sensitive and holding a single style factor will not eliminate the risk from US shocks. This paper provides a simple way of tracking shocks without delay and can serve as an important tool for EM currency trading.

Currency trading making a comeback? The differences across countries say yes

The dollar and currency markets in general have been in a low volatility environment for an extended period. Trading opportunities have been infrequent and have been concentrated over short time periods. Periods of crisis like 2008 and 2020 caused large currency moves, but stability has generally ruled the markets. 

However, currency stability may be coming to end. As Fed QE ends and QT begins, the transition will lead to more volatility as investors adjust to the new monetary world. This process is already starting globally with QT beginning with other central banks. 

The new currency environment will be driven by several changes in global markets:

1. Low rates will be ending. The process may still be early, but rate differentials will widen as central banks reduce their stranglehold on rates.

2. Low and stable inflation environments will be ending. Even if some of the highs in inflation are reversed, inflation around the globe will be elevated and have greater dispersion.

3. Differentials in growth will increase. Growth rate differences have already increased and as policies change, growth differences will continue.

4. Similarity in central bank behavior is ending. Policy differences will increase especially in emerging markets.

5. The volatilities in traditional assets are moving higher.

6. Policy uncertainty is increasing as countries choose different policy paths to meet their circumstances.

Policy and economic differences will raise opportunities across all currency markets. 

 

Debt ceiling is a side show versus the riskiness of Treasury debt as measured by a discounted pricing model

The US has unusual fiscal dynamics with the regular battle over debt ceilings. A fight may occur, but eventually the addiction to debt continues. The drama unfolds; however, the ending is still much the same. Approval for an increase allows for another sequel to the current drama. For investors around the world, this does not make sense. We end with a potential crisis which adds uncertainty that is not necessary. There is still little fiscal prudence. Of course, deficits are necessary but so are surpluses over the long run. 

It is, however, more important to think through the strategic dynamics of debt and not the legislative tactics. In a recent working paper, The US Public Debt Valuation Puzzle, the debt problem is looked at from a different perspective. The authors attempt to price Treasury debt like any other asset using some classic asset pricing models. This provides some fresh perspective but also introduces a puzzle - why are rates so low when Treasuries should be considered a risky asset.  

From an asset price perspective, the market value of government debt should be equal to the present discounted value of fiscal surpluses. If the value of the debt exceeds the priced surplus claim, there is a debt gap. The authors find that the yields on Treasuries are lower than the relevant interest rates that investors should be earning on the risky claims. Hence, there is a puzzle for why there is this different.

The cyclical and long-term dynamics of spending and tax receipts makes for a risky claim. The primary surplus is pro-cyclical like stock dividends. Hence, Treasury debt has substantial business cycle risk and the relevant interest rate for discounting should have a risk premium. Debt occurs at inconvenient times from a consumption perspective. This risk premium is greater than what could be the convenience yield of holding Treasuries. 

The model and arguments presented may not make you money in the short-run, and the dynamics of buyers and sellers and the impact of the central bank are not considered, but the core arguments are useful. As long-term deficits increase and the cyclicality of deficits continue, there is less chance of future surpluses. The discount or appropriate interest rate for pricing these future claims should be higher. There should be a bias to higher real rates based on tax and spend behavior.

Market structure and high commodity prices - The continual pull to scale

There is the adage that the solution to low (high) commodity prices is low (high) prices. Prices change demand and supply, so low (high) prices will cut (raise) supply and raise (cut) demand. Prices will adjust. Market prices cycle between extremes with demand and supply lead to equilibrium adjustments. 

This commodity story is playing out today. Higher prices in oil and natural gas will lead to increased supply and a demand response. The adjustment may not be immediate, but there is clear mean reversion as producers and consumers change their behavior. 

Unfortunately, the story is more than a onetime adjustment in price. There are industry organization effects and restructuring of competitors. Price changes lead to a continual movement to economies of scale. Economies of scale are always lurking in the background when there are consistent price swings. Few industries become more fragmented as a response to market volatility. No firm becomes smaller by design. It is either increasing scale or elimination. 

With high price comes credit constraints that support large firms and hurts small firms. The same cargo that has to be financed for trade because more expensive. Lines of credit have to be increased even with higher value of collateral. Smaller trading firms are often unable to get this added financing at attractive terms. When prices are low, credit again may become scarce as the threat of bankruptcy increases for producers.  Of course, financing issues impact large firms, but the ability to weather these issues is easier when scale is already present. 

Logistical problems are also problems of scale. Failure for delivery, loading delays, and higher transportation cost all hurt the small firm that may not be as diversified as larger firms. Concentration of business makes the cost of logistical failure greater. 

Dispersion in price and logistical issues also lead to greater vertical integration by firms in order to control prices along the supply chain. Again, there is a movement to scale.

The movement to scale means more concentrated trading and less price transparency. This is never good for the investors involved in commodity trading.

China policy uncertainty - Starting to rise, but not like last year

Country risk is associated with policy uncertainty. What makes equity market risk premia differ across countries is the business policy environment which includes tax, regulation, fiscal, trade, and monetary policy. If uncertainty rises in these areas, investors will want to be compensated for policy unknowns. 

Clearly, there are unknowns concerning the disposition of Evergrande where the rule of bankruptcy law is unclear. This event uncertainty is present everyday as news reports and analysis describe the lack of clarity on what will happen with a potential failure that will run into the hundreds of billions.

Evergrande is not an isolated incident of an unknown solution in a world of China policy certainty. The Economic Policy Uncertainty Indices for China show an explosion over the last two years as measured by mainland newspapers and the South China Morning Post SCMP. Uncertainty has fallen from highs over the last two years but is still at elevated levels and may be poised to grow higher. For the more general investor, the uncertainty index is a better measure of the overall business climate than a single company event.

Parrots as economists, "Supply and demand...demand and supply!"

It was once wittily remarked of the early writers on economic problems, “Catch a parrot and teach him to say ‘supply and demand,’ and you have an excellent economist.” Prices, wages, rent, interest, and profits were thought to be fully “explained” by this glib phrase. - Irving Fisher 1907

So, what is wrong with being a parrot? Any story for determining investment returns is simple. Where is demand going? Where is supply? There can be deep narratives with a lot of facts, but it still boils down to simple issues. Can the facts describe a shift in demand and supply? 

If you cannot parrot some conclusions about these shifts, you will not be able to forecast returns. Yet, something so simple can be very difficult to assess in practice. The slopes of the curves have to be addressed. The link between macro and fundamental variables, expectations, as well as capital still must be measured. Of course, for any investment discussion there may be multiple demand and supply curves that should be considered. There is no one perfectly simple chart.

Any market discussion should loop back to supply and demand. Inflation is moving higher. What does that mean for the demand of some financial assets? How will it impact the supply of assets? The Fed will taper at the end of the year. What will happen to the demand for Treasuries? How will supply be affected?  

Give me the good investment parrot and I can be happy. For more on the origins of the parrot phrase see the Quote Investigator.

Evergrande, contagion, and the uncertainty risk of not knowing

During extreme market bull moves, investors think they know too much or don't need to know the details. Everyone has a story for why something should go up. In fact, there is a "don't confuse me with the facts" mentality. I have the story follow the momentum. 

In a bear market, fear of the unknown takes over. We know too little and act accordingly through avoidance. There is a desire for more facts because no detail is too small.  

The Evergrande debt situation is constantly changing not with solutions but with revealing more information on how bad is the situation and where risks are growing.

Some have discussed Evergrande as a Chinese Lehman moment only to dismiss this extreme story. Lehman comparisons are irrelevant. What is critical for most investors is the issue of contagion. An investor may not hold Evergrande but the spillover to other companies will impact my portfolio. There is more than one type of contagion and investors should be aware of the differences. 

One form can be called tangible contagion. A failure of a firm can have an impact in their suppliers, buyers of their services, and banks that lend to the firm. These are direct spillovers. 

A second form of contagion can be associative. There are firms within the same industry which will be affected with negative perception. In this case, other property firms. However, this contagion can have a short life as investors compare and contrast risks. 

The third form of contagion comes from the unknown or intangible risk. There are direct Evergrande risks, but I don't know how this will spill-over to other firms, what will be the rule of law in China, the regulatory environment in China, how dollar debtholders will be treated, the risks for other highly levered firms, or the risks to construction in China. 

All of these are current unknowns that cannot be easily handicapped. This is uncertainty that cannot be measured. Hence, there will be a general exit from any risks that can be tied to the Evergrande environment or ecosystem. This general pullback may not make sense, but it is tangible and will affect global markets. we are seeing this contagion and the process is not over. Some will just call this a risk-off environment. The name does not matter. What matters is that high uncertainty will lead to large rebalancing flows away from risky assets.

The fire triangle metaphor explains the Chinese (New Zealand, Canada, ....) housing bubble

The fire metaphor was used in the book Boom and Bust: A Global History of Financial Bubbles by William Quinn and John Turner perhaps the best historical analysis on extreme market moves over the last 300 years. 

Their framework begins with a metaphor of bubbles as fires that grow based on a classic triangular combination of oxygen, fuel, and heat. With enough of each ingredient, a spark can set off a long-lasting market inferno. We have the metaphor working in excess with respect to global housing. When all of the fire triangle ingredients are in place, the end result will not be pretty for investors.

Quinn and Turner’s analogue to oxygen is marketability, the ease of buying or selling an asset. Marketability includes divisibility, transferability, and the ability to find buyers and sellers at low cost. Assets that lack marketability will never see the broad demand required to create a bubble. Marketability is increased by improvements in market structure, low-cost exchange trading, and the introduction of derivatives. 

A bubble’s fuel is easy money and credit. Without cheap and bountiful funds for investment, there is no opportunity for asset prices to be bid higher. Excessively low interest rates create demand for risky assets as investors reach for yield. 

The final side of the triangle is heat generated by speculation. This is defined as purchasing an asset without regard to its quality or current valuation, solely out of belief that it can be sold in the future at a higher price. 

For this metaphor to work, the market’s catching fire, it must require a catalyst—the proverbial match. There is invariably some cause that creates the strong belief in the prospect of abnormal profits. 

In the case of housing, policies of cheap money with a desire for citizens to acquire ownership created the current environment. Housing is still the number one asset for most households, so governments want to protect this investment for their own political well-being. Investors who funded property companies through bonds or equities have suffered and will continue to feel the pain until markets adjust to normalcy. Bail-outs will occur but not without all parties paying a steep price. 

This is not a new story, but one that has to be relearned. 

Performance versus a benchmark - A better SPIVA year?

 

It is always depressing for active managers to look at the Standard and Poor's Index versus Active (SPIVA) scorecards. Market efficiency lives! However, there are few managers who can beat the benchmarks. For the last year (mid-2020-mid-2021), 42 percent of large cap funds outperformed the SPX. Of course, over the last 10 years, only 17 percent did better. Managers can win in a given year, but time is not friendly to active funds.

In the case of fixed income, 85 percent beat the intermediate government/credit bond index. Managers seem to be better able to beat fixed income benchmarks; however, on a risk-adjusted basis only 10% outperform the benchmark. 

This poor performance explains why the growth for ETF and index businesses have been so strong and why active bets are often placed with hedge fund managers who may have fewer restrictions.

Environmental themes and infrastructure bills - Concentrated risk

Within ESG asset management is the sub-strategy of environmental theme investing. Instead of focusing on the overall ESG rating based on criteria set by any number of firms, investors buy into themes that are associated with key ESG sectors like alternative (clean) energy and technology which includes wind and solar or electric cars and batteries as well as other infrastructure investing. This technology may not be cost efficient until there are better economies of scale. Scale is reached through mass production. The average and marginal cost will fall if there is greater demand often created from subsidies by the government. The subsidies can come in many forms, but all require legislation or regulatory changes. 

Looking at leading clean energy ETFs (KLN, PBW, and FAN) shows the concentration of investment risk around subsidies and government support. After the presidential election, the component stocks of these clean ETFs rose under the expectations of a huge green new deal. As the possibility of an infrastructure deal declined, returns declined to current levels. Investors in the clean energy ETFs are not just buying technology but making a play on the green energy bill that will create scale opportunities. If the stock prices rise, it is signaling a higher probability of legislative success.

Five financial research curses - They cannot be avoided

The above chart is from Marcos Lopez de Prado's insightful powerpoint "Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential" serves as a road map for the important issues that will cause failure in financial research. Every researcher has faced all of these five curses. There is no avoiding them. They are not problems to be solved but cure curses to be lived with whenever quantitative analysis is undertaken. 

Overarching the other four curses is primary problem that economics and finance cannot run experiments like hard sciences, so cause and effect relationships cannot be easily structured. Economists are trying very hard to develop controlled experiments but the work almost non-existent in finance. The curse of non-stationarity makes testing even harder. Even if you could determine cause and effect, the intensity of relationships may change significantly through time. With regime changes and unstable conditions, past relationships cannot easily be extrapolated. The rules of the game are always changing, so nothing is stable. 

Finance is a zero-sum game. One man's research is a lifeblood to future profits and cannot be shared. There often is a limit to the amount of alpha that can be extracted, so research is not always public. Studies have shown that alpha declines as soon as working papers and articles are published by academics. There is value with secrecy.

Complexity is high and the amount of variation that is explained by any model is low. Financial asset prices are discounted expectations which are affected by a wide set of factors that change with firm and macro conditions. Pricing in finance is not a linear problem.

Finally, the sample sizes for many phenomena are small. Think of all the business cycle analysis done by economists when in reality we have only seen eight since 1970. If I did analysis with a sample of 8, most would laugh at my ignorance of significance and power testing. 

So how do we solve these curses? We cannot. All we can do is try and mitigate risks and be aware of the problems. 

The history of evidence-based investing - data and techniques using computing and storage to beat competition

 

The above chart is from Marcos Lopez de Prado powerpoint presentation "Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential". The slide does a good job of dividing the world into four major time periods in the move to evidence-based investing. From technical analysis, fundamental analysis, and market microstructure, we are now in the era of machine learning. I may not agree with the framework, but it provides a good starting place for discussion. 

The ascent of evidence-based investing matches the growing power of computing and data storage. The two cannot be separated. With computing and storage came the development of sophisticated databases that allowed for testing of hypotheses. While academics and a few large firms had access to strong computing power, the ascent of the quants only began with the personnel computer that allowed desktop testing of evidence. 

The explosion of early quants came with using the best data available, prices from exchanges. Technical quant trading grew because the databases and computing power was easy to access. The fundamental data explosion came with the availability of the CRSP database from the University of Chicago which started 60 years ago but exploded in use as computing power improved.

Once data and computing became readily available, the demand for quants and programmers grew in an arms race that has not abetted. The first part of the arms race was focused on more data which again started with prices. If everyone had open, high, low, and close, the next race was for hourly and minute data. The only limitation was storage. The same applied with adding more firms with fundamental data.  

In this arms race, once the existing data usage were exhausted, the focus had to be on techniques to reconfigure data. There was a limit on the number of linear regressions that could be done on any database. The move to data science and machine learning is the continuation of trying to better competition that has the same data and computing power. The second line of work in this competitive battle is the use of alternative databases. Once all the existing data was mined, the search for new data began in earnest. 

The quest to deeper evidence-based investing is not about the quest for knowledge albeit this is a side result, but a drive for profits in one of the most competitive industries in the world. 

The Fed behind the QE curve versus other central banks

The Fed is behind the curve relative to other central banks with QE tapering. Fed QE policy is behind even though the US real economy is recovering better than peers, inflation is higher than other countries, and financial markets are bubbly. Of course, each central bank has a different operating objective, but it is odd that the Fed is having such a hard time making this decision. No other central bank has had as much public handwringing on discussing when to start discussing tapering. Granted that the Fed and the dollar plays a special role in supporting global markets, but the delay does not seem to serve a purpose.

The impact of any tapering announcement will be diminished given this delay. The market will likely only focus on the forward guidance concerning rate moves and not focus on the actual tapering specifics. Nevertheless, the process of returning to normalcy should begin. The real question is whether the markets can accept a return to normalcy when fiscal deficits see no end and markets are in a period of extreme.

The politics of decision-making and the "Johnson Treatment" - Avoid with investment decisions

 

Lyndon Johnson was a politician who got what he wanted. He was persuasive not always through the weight of his arguments but through the weight of his personality. He was known to lean into people to make them uncomfortable enough to be swayed to his point of view. It was the Johnson treatment. The photo above shows Johnson giving the treatment to Senator Ted Green from Rhode Island. Newspaper columnist Mary McGrory described it as “an incredible, potent mixture of persuasion, badgering, flattery, threats, reminders of past favors and future advantages”

Johnson was a master legislator in the US Senate, and he used all his skills to get his way. This heavy-handed behavior was an effective process of him, but that is not an effective way to reach investment decisions. 

You see the treatment in investment committee meetings all the time. The weight from authority, the senior portfolio manager, or the committee members that has the strongest personality will win the day. The Johnson treatment is present, and the best arguments are often lost in the debate. 

Are their ways to solve the politics of decision-making? It is not easy, but there are few ways to improve the situation. First, don't have members present opinions. Focus primarily on the facts, what is known. Second, focus on the link between facts and what is the question to be answered. There is a projection from facts to prediction, and a link between facts and market reaction. This link can isolate what is quantitatively measurable and not focus on the weight of personality. Third, there is a focus on the prediction of each committee members within the context of facts and linkage. Everyone has to have an opinion. Fourth, there is accountability for each individual prediction, so the quality of judgments can be measured. The members of the crowd can be handicapped.

The Johnson treatment may work well in building to a conclusion for a politician but investment decisions must be based on the weight of the evidence and the quality of thinking. The treatment for good investment decisions must flow from facts.

In medias res and investment analysis - Solving problems when thrown in the middle of events

The Latin term in medias res means in the middle of things and often describes a plot device that starts the story in the middle and not the beginning. The term may also describe the situation that most investment analysts face. 

For most investment problems there is no easy well-defined beginning and analysts certainly don’t know the conclusion. Investment situations usually start in the middle of an ongoing story and have to figure out the beginning and how to anticipate the end. 

This is different than the position of the historian who has the beginning, middle, and end of the story and have to focus on interpretations. Adam Tooze, the Columbia University, has had his writing of current event described as an in medias res problem. He is writing about history that is unfolding in real time.

Dropped in the middle of the story, the analyst has to figure the story out with no help of time. There is no repeat, rewind, or reread of current events. The investment has to be lived in real time. 

Is there a way to solve this problem? The burden is on the analysts to resort problems and look for first causes. The investment analysis process has to create a beginning to fit the middle facts and then form an end. The investment narrative creates the full story. 

Narrative and investing - Follow the facts

 

“Ransom Stoddard: You’re not going to use the story, Mr. Scott?
Maxwell Scott: No, sir. This is the West, sir. When the legend becomes fact, print the legend.”

- Ending of the movie "The Man Who Shot Liberty Valance"

If we had to apply this dialogue to investment markets, "when the market narrative becomes fact, print the narrative." Stories dominate the market, facts less so. Narratives concerning employment, inflation, Fed policy all dominate discussion because they provide respectability and plausibility for both the sender and receiver of the message. In a confused world, a good narrative will provide comfort as an explanation that makes sense. For the messenger, telling a story will make you a more attractive talking head than just focusing on the details in facts. Facts that do not fit the narrative make investors uncomfortable and throw investors into a state of uncertainty. 

The goal of quant or data driven analysis is to provide an alternative to narrative-based investing. The difference between fact-based data and narrative is the wedge that quants want to exploit. Data driven funds will reach different portfolio choices. Of course, data and narrative can be aligned. In that case, there is limited opportunity for alpha. 

Follow the facts not the narrative. Nevertheless, even the fact-based story has to be converted into the narrative. The difference is whether the investor starts with facts to develop a narrative or starts with a narrative and then looks for facts.

Thoughts on narrative

Narrative and price - Know the line of causality

Narrative economics - food for thought for quants

Narrative and numbers - You need both for effective forecasting

Is investment management a science - Use the "narrativeness" test

Narrative Economics by Robert Shiller - Storytelling but no predictions

The sobering world of future returns

Tracking expectations of future returns by major wealth managers provide base information on what may be possible over different horizons. Looking at forward returns should help with forming the right asset allocation blend. Over the post-GFC period, the best blend was for risk-on passive equity investing with some fixed income offset. Strong liquidity made it pay to just hold equity indices. The stable environment with negative also made holding bonds the best hedge. Of course, we may not have known that this was best 10 years ago, but traditional passive worked.

Now the numbers should give investors pause. For example, forecasts from UBS Wealth Management tell a sobering tale. UBS is not an exception but the norm. In all cases, fixed income assets are expected to be negative. Equity returns will be modest at best with 4% expected in a growth scenario, but 1% as a base. 

The answer to these low returns is moving to alternative assets. This helps explain the strong growth in private equity. There has been flow into less liquid higher risk assets. The other move will should be to active management that can move between assets. Market efficiency suggests successful active management is difficult to achieve. The number of managers that outperform benchmarks is limited, but in a low return environment it may one of the few ways to achieve excess return.   

Hedge fund start-up - if you are in a cold style, you will have to work harder and have more skill

There is always strong interest in hedge fund start-ups because investors are looking for the next hot manager with extraordinary skill. It is like the drafting of players in professional sports. Existing players are known quantities but drafting from college may get you the next superstar. Of course, many do not work out or realize this potential. More importantly, there is no hedge fund draft. Investors and managers have to find each other through search and have to conduct private due diligence.

A recent paper has focused on the hedge fund start-up market with a novel approach for analysis, see “The Economics of Hedge Fund Start-ups: Theory and Empirical Evidence”. The paper starts with a simple framework where capital is raised through what it refers to as extensive and intensive margin. Capital raised from the extensive margin is gained through the matching of investor and manager through search. Intensive margin is the acquiring of capital by managers through due diligence process and analysis of skill. 

Fund families will arise to mitigate the friction and reduce the probability of failure in raising assets as a function of skill. If a fund family can reduce search frictions to acquire assets through their brand and network, then there may be less emphasis on skill as the determining factor for raising assets. 

The market environment can either be cold or hot for a given strategy. If the market is hot, the focus is on search. There is a bandwagon effect and investors want to hold more suspected winners. When the market is cold, the investor focus is more skill than searching for managers in hot styles. This stylized approach suggests that managers must get or have more skill when they face low investor demand. 

Capital acquisition is more than just simple search. Managers must earn the trust and respect of investors when there is a low demand environment. In a cold market new funds will often outperform existing hedge funds and hot inceptions meeting high demand. Similarly, in a cold market, new managers will outperform all types of managers because they must show skill as the key to asset raising. 

A lot of work has gone into this paper to validate some simple marketing 101 concepts. If you are in a hot sector where there is a lot of investor demand you don't have to be better than style peers and other managers. The market will find you. If you are in a cold market, you better have a better process and performance because investors will not be looking for you and will be more reluctant to provide capital. Better to be style lucky as a new manager. If a manager is not style lucky, he is going to have to work harder and be better. It shows in the numbers. 

Extreme skew in the market - the cost of tail hedges

 

Markets may move higher but that does not mean that invetsors are more optimistic about any equity trend. In fact, current information on risk perceptions shows that there is significantly greater fear of a left tail event as meaasured by the CBOE skew index. Like the VIX index, the CBOE uses prices from equity index options to calculate the skew embedded in market prices across the traded strike for an implicit one month option. The skew number usually falls between 100 and 150 but current values are at extreme levels.

The high skew suggests that buying put ptrotection is more expensive versus any period since the inception of the index. The value is higher than any period associated with business cycle downturns or market crashes. The risk-adjusted probabilities below provide the orders of magnitude for skew.  A 155 reading is off the charts for both a two and three standard deviation event. It is time to be careful given the market's view on risk.