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Volatility is on the rise, thanks in no small part to N. Korea and Trumpster exchanging threats of plunging into nuclear war.

Who knows what will happen.

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Long volatility. That is the trade that I like.

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Digital currency Ethereum experienced a “flash crash” on Wednesday, with the price falling from about $US296 to a low of $US0.10 in a matter of minutes.

Almost as quickly as it collapsed, the price bounced back and, at close to 11.25 a.m. BST (6.25 a.m. ET) on Thursday, Ethereum is trading at $US342.02 according to Coindesk.

So what happened?

The price crash appears to stem from GDAX, one of the leading Ethereum exchanges. Adam White, a VP at GDAX, wrote in a blog post on the company’s site that an unusually large sell order caused the crash.

A “multimillion dollar” sell order caused the initial price dip but the real problem was the domino effect that it triggered. The initial fall triggered 800 stop losses — automatic sell orders that are placed once an asset hits a certain price — and margin funding liquidations, which is where investors trading with borrowed money had their positions closed to stop them losing any more money.

Essentially, the large sell order created a flood of other sellers. With not enough buyers to mop up demand, the price collapsed as programmes executing the trades tried to find a price at which buyers would step in and fill the orders.

Here is how the flash crash looked as it happened:
Read more at https://www.businessinsider.com/ethereum-flash-crash-explained-gdax-2017-6#qfDLQmw35M1dCvpx.99

Charles Hayter, CEO and founder of digital currency information provider CryptoCompare, told Business Insider over email: “Thin order books and large trades are the usual culprits in these scenarios. Liquidity that isn’t unified but spread across multiple isolated pools can be vulnerable to large sell orders that drop prices rapidly. This can then trigger panic in the market.

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Never before had we seen a bull market driven by what we refer to as growth counter-cyclical stocks. Bull markets like the 1990s or early 2000s tend to be driven by cyclical stocks. Technology drove the 1990s bull market of course, while financials drove the early 2000s bull market. We call these two sectors “hyper-cyclicals” for this very reason.

We find ourselves in a similar situation today—holding an out-of-consensus viewpoint on a certain segment of the equity market. Specifically, we continue to have a bullish disposition toward the resources sector broadly, and the energy sector in particular. We wish to review our bullish thesis on the energy sector in greater detail.

There are roughly 3,200 companies in the top 85% of the market cap of all 46 developed and emerging market economies. We have created a set of market-cap weighted indexes around this global selection universe that we call the Knowledge Leaders Selection Universe (KLSU) Indexes. These indexes, which are similar to the MSCI Global Indexes, are our starting point for selecting equities for our active portfolios. We’ll be referring to these indexes for the duration of this letter. In particular, we’ll be referring to our developed market KLSU energy indexes, including:

  1. KLSU DM Americas Energy Index, which includes the top 85% of all energy companies in the US and Canada. This index contains 60 companies and has a market cap of $1.64 trillion.
  2. KLSU DM EMEA Energy Index, which includes the top 85% of all energy companies in Europe. There are 26 companies with a market cap of $736 billion.
  3. KLSU DM Asia Energy Index which includes the top 85% of all energy companies in Asia. There are 14 companies with a market cap of $95 billion.

The ideal attributes we look for in a stock are:

  1. It is within a group that has been down for at least 7-9 years. This generally indicates a group where valuations have compressed, sentiment is low and stocks can be accumulated at attractive prices.
  2. Companies have slashed capital expenditures. Generally, when capital expenditures industry-wide decline, the result is improved pricing. This is particularly relevant for an extraction industry like energy. We will get into more details on this shortly.
  3. Companies have a cheap multiple to assets and/or sales. When companies experience secular downturns, flow-based measures like earnings or cash flow can be difficult valuation inputs. We prefer asset-based measures.
  4. Companies are out of fashion among investors. Technicals can be helpful here to quantify the extent to which investors are shunning a certain group. Very often, and we’ll argue now is one of those times, investors engage in capitulation, which can give investors a great chance to buy that cheap company at even cheaper prices.
  5. An opportunity for diversification. Stocks in sectors that have experienced a low correlation to the benchmark have tremendous diversification potential.


The Very High Risk Of Low Volatility

Eight years into an economic recovery, with the S&P 500 having gone from 666 to 2,430, these are very difficult criteria to find in potential investments. Broadly, there is only one sector in the world that fits our criteria, and that is the resources sector. For now, I’ll focus on the energy sector, but most of my comments are applicable to the basic materials sector, particularly metals, both industrial and precious.

With an index base value=100, starting 10 years ago, our KLSU DM America’s Energy Index is down a cumulative 20.25%. From its high of 128.5 on May 20, 2008, the index is down 38% from peak to present. Lows in this index were made in March 2009, with the rest of the market, at 55.5, down 57% from the May 2008 peak. In January 2016, the index hit 64, not taking out the lows from 2009. The current index value of 79.75 is up 25% from the lows last January, but as mentioned earlier, is still net down 38% from 2008 highs.

European energy stocks have fared even worse. With a similar 10 year look back, and index value=100 at the start, European energy stocks are down 41.27%. They peaked in May 2008 at 124, and then fell to an ultimate low of 50.75 in March 2009. That was a 48% drop over 10 months. In contrast to North American energy stocks, the European energy sector did take out 2009 lows in January 2016. On January 20, 2016, the index bottomed at 47.5. The peak-to-trough decline for European energy stocks was 62%. European energy shares have rebounded back to a current index level of 58.75, or some 24% higher.

Asian energy stocks, using the same index methodology as the previous two examples, are down 44.8% over the last 10 years. After peaking at 133.3 in May 2008, Asian energy shares fell to a low in October 2008 at 52.15. The decline was 61% peak-to-trough. Similar to European energy stocks, Asian energy shares did take out the 2008 lows in January 2106. Asian energy stocks ultimately fell to an index level of 42.1 and have since rebounded to an index level of 55.2. From the highs in 2008, Asian energy stocks are down a cumulative 59%.

In relative performance terms, the underperformance of the global energy sector has been even more extreme. From peak values in May 2008, regional energy sectors are down:

  1. North America down 52% relative to MSCI World Index.
  2. Europe down 53% relative to the MSCI World Index.
  3. Asia down 67% relative to the MSCI World Index.

Capital Spending

Next, let’s look at the change in capital spending. Global energy sector capital spending peaked around $500 billion in 2014. Since then, spending has fallen almost $200 billion.

This 35% cut to capex is much larger than anything experienced in the 2008 recession, where capex cuts were less than $100 billion. Capital spending actually increased during the 2001 recession. Capex levels globally are back at 2008 levels, around $325 billion.

In particular, the North American energy sector cut its capex by over $100 billion since the early 2015 peak. North America represents roughly two-thirds of total global energy capital expenditure and over half of the $200 billion cut to capex.

Of the $200 billion drop in energy capex, Europe accounted for about a quarter, or about $60 billion. Capex peaked for European energy companies in 2014 at about $170 billion. Here again, these cuts are 3x larger than what we experienced in the 2008 recession. European energy companies also increased capex during the 2001 recession.

Developed Asia is by far the smallest portion of the energy market, but still capex came down roughly 40%, or $8 billion. It peaked around $20 billion in 2014 and is currently a little more than $12 billion. Asian energy producers did not cut back on capex in the 2008 recession or the 2000 recession.

For energy producers, inventory levels are highly related to capital spending and demand. The commercial inventories in the US represent the lowest-cost storage in the world. The five largest US crude oil storage facilities (and capacity) in the US are: 1) Cushing (82 million barrels), 2) Louisiana Offshore Port (62 million barrels), 3) Houston (36 million barrels), 4) Beaumont-Nederland (30 million barrels), and 5) St. James Louisiana (30 million barrels). These five storage facilities represent roughly half of all US commercial storage. Storing oil on contracted VLCCs (very large crude carriers) would be the most expensive way to store oil.

With respect to inventories, there are some interesting relationships I would highlight. Commercial inventory levels hovered around 350 million barrels from 2011 to 2014. Then in the spring of 2015, they leapt to 450 million barrels before backing down to 425 million in the fall. But, before year-end, 2015 inventories were building again and pushed to 500 million barrels in early 2016. Inventories then fell back to around 475 million barrels by the fall of 2016, but then they surged again to a peak of 530 million barrels by March of 2017.  Since then inventories have backed down about 20 million barrels to about 513 million.

In general, what we saw over this period of inventory building was that when inventories built, the broad US stock market outperformed the MSCI World Index, and when inventories fell, they underperformed. The surge in the NASDAQ in the last few years is the best illustration of this. The global energy sector behaved in the exact opposite manner. When inventories built, global energy stocks underperformed the MSCI World Index, and when they fell the energy sector outperformed. Focusing on US commercial inventories is a great barometer of the global inventory situation since US commercial storage is the cheapest in the world. If there are draws on US inventories, then there are clearly bigger draws on higher cost storage.

The International Energy Agency came out on June 6, 2017 affirming demand projections for 2017 and 2018. Specifically, they see demand increasing by 1.54 million barrels/day in 2017 and 1.62 million barrels/day in 2018. These estimates represent growth rates of roughly 1.7-1.8% for this year and next. This would represent among the higher growth rates that the world has experienced over the last decades.


In cyclical industries like resources, valuations can be tricky. Flows fluctuate wildly. In depressed cyclical industries, we prefer to focus on asset-based valuation metrics like price-to-book value. US energy stocks sell at a premium to the rest of the world, but in all regions, valuations hover near 15 year lows. Over the last 20 years, the highest multiple achieved for North American energy producers was 3.5x our intangible-adjusted book value, where we credit companies with their innovative investments. The low was 1.3x in January of 2016 and is now at 1.7x. Values only dipped below 1.5x book value for one quarter in 2009 and two quarters in 2016.

Using our intangible-adjusted price-to-book value metrics, European energy stocks are selling at a 5% discount to book value. Multiples peaked at 2.7x in 2005 and have been sliding since. Trough valuations in January 2016 were .7x book value.

Asian energy shares are the cheapest in the developed world, currently selling for .8x intangible-adjusted book value. Multiples peaked in 2006 at 2.7x book value and troughed in January 2016 at .65x book value.

As a cross check, we’ll also look at the median stock (the visual middle) in the group. The message is the same, with median valuations hovering near 15 year lows. Median valuations in all regions are in the bottom quartile of the last 15 years.


There are lots of ways to illustrate how out of favor energy stocks are right now. I’ll focus just on the North American energy sector because the statistics are most acute. Only 11% of US energy stocks are above the 200-day moving average. 30% marks oversold in our work. These oversold levels are matched by similar readings in 2009 and 2016.

Over the last 65 days, as energy has slumped, 38% of stocks are making new 65-day lows. Readings in 2008 spiked to almost 100%, and in 2016 they hit 80%, but remember we are 9 years into the liquidation and should expect more modest readings. Nevertheless, 40% marks an oversold reading in our work, so we are about there.

Only 8% of North American energy stocks outperformed the developed world over the last 65 days. This is close to the worst reading we have ever seen. In March, we hit a low of only 2%, which was the most extreme stat we have ever seen for North American energy shares. At the depths of the 2008 sell-off, we only hit a reading of 5%. Capitulation is clear here.

The performance of stocks is a function of: 1) the percent of days that the stock is up, 2) the amount it is up/down each day. Focusing on the first variable, the percent of days that stocks are up or down is highly mean reverting to 50/50. This mean-reverting randomness (like flipping a coin) is the essence of work like Burton Malkiel’s, “A Random Walk Down Wall Street.”  In the real world, stocks go on runs and slumps, periods where the percent of days up are more than 50% or less than 50%. A group experiencing a string of sub-50% readings suggests it’s in a slump. In other words, it represents a group in liquidation. For the better part of the last decade, the percent of up days for the energy sector averaged around 45%. This is illustrative of how long energy stocks have been in a slump. Currently only 40% of the last 88 trading days have been positive days for the North American energy stocks. The lowest readings were registered in 2008 and 2016, when only 35% of trailing 88 trading days were positive.


There are three basic ways to manage the risk of an equity portfolio:

  1. Diversify, by owning a collection of stocks from different industries
  2. Hedge, by shorting stocks or equity futures
  3. Buy insurance, like buying an out-of-the-money put option.

The essence of diversification is choosing assets that are not perfectly correlated with each other. The logic is simple enough: when one asset zigs, another zags. Years ago, finance scholars proved that a portfolio of securities is less risky than an individual holding, and the idea of diversification as a risk management tool was born.

When I survey the landscape of US equities, it appears that there are very few opportunities for diversification. My method is simple: I look at the correlation over the last 10 years of each North American sector in our global Knowledge Leaders Selection Universe (KLSU) Indexes to the S&P 500 Index.

Starting with the consumer sectors, it is amazing to see a 98% correlation. Now the consumer discretionary sector outperformed the broader North American equity market, but I am not so concerned with relative performance in this exercise. Rather, I am interested in identifying sectors that can add diversification to a portfolio. Clearly there is little portfolio diversification owning discretionary shares.

Same story when I look at the consumer staples sector. It has a 97% correlation with the S&P 500 over the last decade. Next is the financial sector, where again we see very high correlations. The North American financial sector has a 92% correlation with the S&P 500. On to health care next. Again, I see little diversification benefit to a sector that is 97% correlated with the S&P 500. The industrial sector takes the cake for the sector with the highest correlation to the S&P 500. It has an amazing 99% correlation over the last decade. No diversification here. Closely behind the industrial sector is the technology sector, with a trailing 10 year correlation of 98%. Even the staid telecom sector has an 85% correlation with the S&P 500 over the last decade.

To highlight the concentration of returns and difficulty finding good diversification opportunities:

  1. 8 out of 10 sectors in North America have a correlation to the S&P 500 over 80%.
  2. 7 out of 10 sectors in North America have a correlation to the S&P 500 over 90%.
  3. 5 out of 10 sectors in North America have a correlation to the S&P 500 over 97%.

For those still pursuing active asset allocation, there are two sectors in North America that should come into focus. The energy sector has the lowest correlation to the S&P 500 of any sector. Over the last decade, the sector only has a 32% correlation with the S&P 500. This means the S&P only explained about 9% of the movements of energy stocks (the r-squared). With correlations this low, energy shares appear to be an effective diversifier.

The sector with the next lowest correlation is the basic materials sector. Here the correlation over the last decade is somewhat higher than energy at 50%, which means the S&P 500 explained about 25% of the returns in the materials sector (r-squared). But still, the sector is really driven by idiosyncratic features like the price of copper or polyethylene.

The take-away for all those for that practice diversification as a risk management tool is that the resources sectors are really the only sources of diversification in the US equity market right now. While a portfolio with a good allocation to resources is likely underperforming the benchmark this year, if/when the US equity market experiences another bout of “risk off,” like it did last Friday, these areas should bolster portfolio performance.


When I put this all together, I see a group:

  1. That has massively underperformed over the last decade,
  2. That has slashed capex to decade lows,
  3. That is in the lowest quartile based on price-to-intangible-adjusted book value,
  4. Appears to be experiencing capitulation, punctuating how out of fashion the group is,
  5. That has a low correlation and thus offers effective diversification.

As I said earlier, the resource sector broadly (energy and materials) are the only groups that meet all the criteria we like to identify. Why this combination of variables? It is generally a set up for a multi-year period of outperformance.

Suffice it say the rest of the equity markets appear as the polar opposite of this set-up. Most other segments of the market have outperformed, capex is near all-time highs, valuations are extended and sentiment is euphoric. I think all the references to FANG (Facebook, Amazon, Netflix and Google) crystallize this. Similar to the early 1970s, where companies that were secular growth stories perceived to deliver consistent earnings (Avon, Polaroid, etc.), the Nifty-Fifty as they were called, promptly fell 50-80% in the 1973-1974 bear market.

From May 2015 to February 2016, the oil stocks were liquidated much worse than the oil price itself. This type of behavior marked the low in energy stocks in December 1987, February 1992 and March 2009. Assets in Rydex energy funds are down to only $26 million, unchanged from the February 2016 low, having fallen from $114 million at the peak in July 2014. Some energy stocks have recently taken out February 2016 lows, but I don’t think they go deeper. We expect a slow multi-year rebound in energy stocks.

We’ve definitely been early on our energy call, but I think the stars are aligning for the energy sector to outperform in the near future.



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Ordinarily, politics and economics influence each other with economics being more of a driver on politics than politics is on economics—e.g., bad economic conditions normally lead to political changes—and normally we don’t need to pay much attention to politics to get the economics and markets right. However, there are times when politics becomes the most important driver. History has shown us that these times are when there is great economic, social, and political polarity within a country and there is the selection of populist leaders to fight for “the common man” in a battle against “the elites.” These conditions exist now. The 1930s were the last time this happened in the developed world and globally.

As we described in the study we did on populism (accessed here), our examination of this phenomenon made clear that the conflicts between the common man and the elites typically take place a) during times of economic stress due to wealth and opportunity disparities, b) when the common man believes that the country’s core values are being threatened by foreigners, and c) when government seems so dysfunctional that radical change is widely believed to be necessary. Those conditions typically lead to a strong-minded, confrontational fighter being brought to power to represent the underserved constituency, typically by pursuing more nationalistic, protectionist, and militaristic policies, which typically leads to more domestic and international conflicts. In some cases it led to democracies becoming dictatorships, and wars.

I am not saying that we are on that path, but I am saying that it has to be watched out for because if it is in the works, it is a really big deal. In watching out for it, in the early stages of a new populist administration, the main thing to look for is whether conflict moves to the point that it is detrimental to the effectiveness of government and the economy. The potential for government to become dysfunctional is unique in democracies because the relatively open checks and balances system (which, under normal conditions, is a strength of the system) and because the free media (which is normally a strength of the system) can operate in a way to incite emotional conflicts rather than encourage orderly resolutions of conflicts through the legal system. Even when it is operating well, the legal system can move very slowly, dragging out the period of conflict rather than leading to the prompt resolution of it. These conditions can reinforce emotional and antagonistic polarity because the goal of beating the opposition supersedes the goal of working together to try to find compromises that are good for the country as a whole, and that can create a self-reinforcing downward spiral. That has to be watched out for because, if it were to occur, it would have profound implications for economies, capital flows, and markets. Right now there is a whiff of it in the air.

In my opinion, the trend toward conflict leading to greater dysfunctionality, leading to greater conflict, in a self-reinforcing way is increasingly apparent in the US and UK. Over the last 24 hours we’ve seen developments in the US (pertaining to the issues surrounding Jim Comey’s testimony) and the UK (concerning no UK party having a ruling majority and the threat of a left populist leader emerging). While in both cases, so far, the political and legal institutions and systems have worked as intended—e.g., Special Counsel appointed and electoral system delivering a rebuke of a sitting government—nonetheless these developments entail the risk that political conflicts will lead to reduced government effectiveness in these two countries at especially challenging times for each of these countries (e.g., for the UK exiting the union and needing to redefine its economic and geopolitical place in the world, and for the US needing to clarify its domestic and international directions).

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Portfolio management involves much more than just an investment idea. For sophisticated investors, it is also about diversification, volatility management, and exposure mitigation.

For a Bridgewater client, the investment process isn’t as much about the hedge fund as it is about the client’s risk management needs. After explaining how the fundamental investment machine works – which operates like a systematic, logic driven machine – Prince explained a portfolio customization method typically reserved for the most sophisticated of algorithmic investors.

While the core investment analysis and “truths” upon which investments are based does not change from investor to investor, the level of volatility and beta benchmark exposure can be adjusted like a dial on an oven. If an investor determines they want 7% volatility, for instance, Bridgewater customizes their investment based on this benchmark by keeping the mix of alphas the same, but adjusting their size. All other performance factors – absolute returns, drawdown, risk exposure – are driven by this volatility dial to various degrees.

The beta benchmark offers investors a method to calibrate the effectiveness of their investment to any one of 22 different benchmarks.

Bridgewater’s strategies are fundamental in nature but driven from a systematic standpoint. In fact, Ray Dalio, the fund’s founder, is credited with being among the early hedge fund leaders to embrace systematic, logic-based algorithms.

The fundamental investment process starts by developing a timeless and universal investment thesis based on “how the world works.” Each performance driver and the logic behind the investment is made entirely transparent to the investment analysis team. Engaging in “radical transparency,” a strong critique and even attack of the investment thesis – modeling through positive and negative market environments – is encouraged. It is not an environment for those who take offense at their ideas or principles being challenged can typically handle. In this respect, it is a survival of the fittest environment to various degrees – and only the strongest uncorrelated ideas rise to the top.

After the research idea makes it through a strong due diligence process, strategies are assessed for goodness and correlation based on a series of qualitative and quantitative metrics. These fundamentally based systems measure the pressure on each market, with pressure dials that scale from fully bullish to fully bearish with smooth gradations in between.

This research, for which Ray Dalio, Bob Prince and Greg Jensen are intimately familiar, is then handed off to the asset management team where portfolio exposure impact is assessed. At each stage of the portfolio management process there are idiosyncratic methods that Bridgewater utilizes to deliver uncorrelated performance improvement.

After asset management, the third and final step of the process is trade execution. Bridgewater doesn’t invest in individual name stocks to the extent of most hedge funds but rather engages in significant derivatives exposure. In large part this is done for the sake of exposure efficiency but also allows the degree of volatility and risk management customization, much of which is done through various leverage adjustments.

Bridgewater is a machine, but a machine based on a systematic logic that is firmly run by humans. The output has been some of the most uncorrelated performance in the history of major hedge funds.

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Volatility tends to drop when market risk is building up and leverage is rising, luring investors into complacency. Indeed, the lower volatility justifies investors taking on more leverage; if volatility has dropped by a third, why not take one and a half times the leverage? This pro-cyclical dynamic arising from lower volatility in times of increasing risk-taking is the volatility paradox. The main take-away from the volatility paradox is that we shouldn’t use shorter-term, contemporary risk measures when they are very low.
But there isn’t really a paradox, and we shouldn’t ignore the low volatility. Unusually low volatility has value, it is just that if it is being viewed as a typical volatility measure it is being looked at in the wrong way. We can rely on short term volatility as a risk indicator, not as an exogenous measure of risk, but rather as endogenous manifestation of the dynamics of the market because low volatility may be telling you that everyone is levered to the hilt and is willing to snap up any asset that moves, that everyone is casting aside negative information with hardly a second thought.
When viewed as endogenously determined by the behavior of the market, the relationship between risk of crisis and unusually low levels of volatility is simple: If people are levered and are at the ready to snap up positions, if they are ready to arbitrage out price differences and make markets oblivious to risk at razor thin margins, then it won’t take much of a price move to find the other side of a trade. If people don’t care about negative information, then the information flows will hardly move prices. The result is low volatility, and this in turn leads to more leverage and then another round of the dynamics that feed the low volatility. The result will be a descending level of volatility that is telling you that the market had been lulled into complacency, or worse, is in full-speed-ahead risk taking fervor, and hence is vulnerable.
Of course even if it is more the latter, it still will be the case that a low volatility derived from recent history will likely reflect low volatility in the near future, because if people are levered and ready to buy anything, if they are at a level of exuberance that leads them to discount anything negative in the market, the odds are high that that the same behavior will persist for the next while. But then suddenly it won’t. There is the chance that the floor will fall out and a crisis will be unleashed, and more than anything else, that is what we need to know for risk management.
We can see this when we think look at things from the other direction: what happens to volatility when the crisis finally hits. At that point no one wants to take on any risk, delevering has led to a reduction in liquidity, and so prices have to move a lot to entice buyers. The market is skittish, and so any news or rumors find everyone scurrying for cover. So for both liquidity and information reasons, prices move a lot more and thus volatility rises to the point that it is again not a useful measure for risk, but for the opposite reasons..
The diversification paradox
Related to the volatility paradox is what we can term the diversification paradox, which I discussed in a post some time back. As with volatility, correlations are low pre-crisis. So as is the case with low volatility, the low correlation and resulting apparent potential for diversification will lull investors into taking more risk. And because of the dynamics that create the low correlation, this in turn will feed into further reductions in correlation, thus adding to pro-cyclicality.
At least this is what will happen if we take the correlations as exogenous – that is if we say “they correlations are what they are, so let’s throw them into our variance-covariance matrix and then let the optimizer rip”. But as with volatility, if we look at the correlations as being endogenous to the dynamics of the market, they give us warning signs. Low correlation tells us that everyone is evaluating the most subtle differences between assets – for example, are the transportation costs for the Ford’s supply chain dropping relative to those of GM’s – and is also searching out opportunities in hinterland, esoteric markets. One asset is being finely differentiated from the other, correlations are therefore low, and investors take more leverage and more exposure because of the apparent potential for risk reduction through diversification.
Of course we all know that when the crisis hits the correlations suddenly rise and the benefits of diversification go out the window. Thus, as I wrote in my earlier post, diversification works all the time, except when it really matters.
When the crisis finally hits, correlations shoot up from the same endogenous dynamics. Suddenly, the only thing that matters is risk, not the subtleties of earnings and the opportunities in Malaysian onyx mines. It is like high energy physics, where matter become an undifferentiated white-hot plasma; assets that are risky are all viewed the same way, all of the risky assets meld together. So correlations rise.
The Paradoxes and Risk Management
There are two points from this discussion of the volatility paradox and the related diversification paradox.
The first and well-known point is that if investors take these measures as exogenous – that is, if the data are treated as a given in the computation of the statistics and the statistics are then applied based on their statistical interpretation – then they will lead to pro-cyclical behavior. Higher leverage and risk taking in general will be apparently justified by the lower volatility of the market and by the greater ability to diversify as indicated by the lower correlations.
The second is that just because the volatility is not a good indicator of the risks lurking in the market doesn’t mean it is not useful. If we recognize that volatility and correlation are endogenous measure that are a manifestation of market dynamics rather than exogenous statistics of market risk to be thrown into our risk management engines, if we dig deeper into the dynamics that are generating them as endogenous parts of the market dynamic, we will find that they actually are telling us far more about the markets.

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