VARIANTPERCEPTION UNDERSTANDING Volatility and forest fires · VARIANTPERCEPTION UNDERSTANDING November 2016 Central bank suppression of volatility is like trying to prevent forest

VARIANTPERCEPTION UNDERSTANDING

November 2016

Central bank suppression of volatility is like trying to prevent forest fires by indiscriminately putting out all fires no matter how small. Although in the short-run there are no big fires, this often leads to denser trees and debris in many forests, which enables unusually large wildfires to burn.

In this report, we show graphically the dampening impact of central banks and short volatility strategies on financial markets. While the effects of volatility compression have been clear, we believe the underlying mechanisms by which volatility is generated have not been changed.

We update and revisit our volatility framework, which we first described in our August 2014 thematic report “Understanding Volatility”. Our structural volatility framework continues to focus on leading indicators of the credit and economic cycle. Our tactical volatility framework incorporates fund flows, positioning and the volatility yield.

It is extremely important to point out that low volatility in and of itself is not a problem. Volatility is serially correlated, and periods of low volatility follow periods of low volatility. Likewise periods of high volatility follow periods of high volatility. This is very much like one of the most basic methods of weather prediction. The rule that tomorrow’s weather will be like today’s is generally right. It is only wrong when the weather changes. In this report we outline the tools we use to identify the changing winds of volatility.

MACRO THEMES

> The best long-term predictor of volatility is the credit cycle. Surges in corporate leverage often lead to volatility as the credit cycle matures. The lagged growth in corporate credit, the shape of the yield curve and real lending rates together reliably predict volatility regime shifts.

Volatility and forest fires

Contents

3 The death and resurrection of volatility7 Structural drivers of volatility7 Structural drivers of volatility: the credit cycle9 Structural drivers of volatility: the economic cycle12 The chase for yield: We’re all vol sellers now16 Tactical drivers of volatility: the VP Correction Signal and global liquidity18 When bad news is priced into markets

UNDERSTANDINGVARIANTPERCEPTION

> The second big structural driver of market volatility is economic volatility. Periods of high market volatility have generally been correlated with periods of high economic volatility. This is true in general, but it is particularly true during recessions when we see large spikes in the VIX and steep drawdowns in financial markets.

> Herding in financial markets predicts crashes. The two best predictors we have found for sharp market corrections or crashes are 1) rising volatility across asset classes at the same time as widening credit spreads, money market and interbank lending rates and 2) herding in financial markets as measured by cross-asset class correlations.

> Changes to excess liquidity are good predictor of spikes and falls in volatility. When there is a lot of excess liquidity, financial markets tend to do well, but when excess liquidity contracts, markets tend to suffer.

> In a financially-repressed environment dominated by central banks, the chase for yield forces investors to sell optionality and volatility to boost investment yields. The structural short in volatility has been building for the past 4 years and is skewing future return distributions towards fatter tails.


Page 3 | 20 Charts Source: Bloomberg, Macrobond and Variant Perception

THE DEATH AND RESURRECTION OF VOLATILITY

Today the world remains in a low volatility regime, but volatility has rebounded slightly from the lows seen in the summer of 2014.

In hindsight our original August 2014 report actually marked the lows of cross-asset volatility in this cycle. At the time we noted famous newspaper headlines proclaiming the death of volatility as the ultimate contrarian indicator, just like the famous BusinessWeek cover proclaiming “The Death of Equities” back in 1979.

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Standardised Cross Asset Implied Volatility 2002-2016 (Equal-weighted average of equity vol, FX vol, fixed income vol, gold vol)



We cannot deny the temporary impact central banks have had on financial markets in helping to suppress volatility, but we do not believe central banks have altered the mechanism in which volatility is generated. As central banks step back, volatility will return. The charts below illustrate visually the impact of central banks on asset markets. The charts show the distribution of S&P 500 weekly returns since 2010, broken out for the period of QE2 and QE3. We can see very clearly that the return distribution had much smaller tails during periods of QE than outside.

We also see a similar pattern in Europe for weekly Eurostoxx returns. Again since the start of QE in Europe, the tails of the return distribution have become much smaller.

It is extremely important to point out that low volatility in and of itself is not a problem. Volatility is serially correlated, and periods of low volatility follow periods of low volatility. Likewise periods of high volatility follow periods of high volatility. This is very much like one of the most basic methods of weather prediction. The rule that tomorrow’s weather will be like today’s is generally right. It is only wrong when the weather changes. Furthermore, prolonged periods of low volatility generally correspond with large equity rallies. The 1992-97 rally was based on low volatility. The 2004-2007 rally happened during low volatility. Likewise, the 2012-2015 rally has happened with low volatility. These were long stretches of time during which volatility stayed low and stocks rose.

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S&P 5 Day Returns 2010 - 2016 Excluding QE2 & QE3

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What is different now and why are we worried about rising volatility? Today we are seeing signs both of fundamental structural drivers of volatility and tactical drivers of volatility aligning, which points to a potential regime shift towards higher volatility.

All things we discuss in this piece are just as relevant from a top-down perspective to credit spreads. Given that equity has the lowest claim within the capital structure, drivers of equity volatility will also naturally drive credit spreads and vice-versa.

We believe that forest fires offer an apt analogy to volatility today. Professor Didier Sornette has cited comparisons of wildfires in Southern California and Baja California to show the counter-intuitive effects of aggressive suppression of small wild fires. Southern California has had aggressive fire suppression policies since 1900, whereas Baja California (north of Mexico) has essentially a “let-burn strategy” with no active management.

As a result, Baja California tends to experience numerous small fires, which helps to avoid too dense a build-up of forest, which actually prevents very large wildfires. Conversely, in Southern California, aggressive micro-management of small fires actually resulted in growth of dense underbrush that results in occasional very large fires.

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Low Volatility vs S&P Performance

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VIX vs High Yield Spreads

High Yield Spreads (RHS) VIX (4wma)



[Source: https://www1.ethz.ch/ ]

Today the systematic suppression of volatility by central banks and short volatility strategies are creating conditions that make a surge in volatility more likely. It appears that ultimately, irrespective of timing, a forest will burn - the only question is whether it burns wildly or with some control.

Source: https://www1.ethz.ch/

Source: news.mongabay.com/



STRUCTURAL DRIVERS OF VOLATILITY

There are two key fundamental inputs that drive changes in volatility:

> Rising leverage / falling leverage. This is the credit cycle. Lagged increases in leverage lead equity volatility and credit spreads by three years.

> Economic contraction / economic expansion. This is the economic cycle. Market volatility is highly correlated with economic volatility, for example the VIX is highly correlated to the ISM. Plunges in the ISM correspond to rises in the VIX. Recessions are always associated with high equity volatility.

Sometimes these two cycles overlap, and sometimes they don’t. For example, there was growth with rising corporate leverage and volatility from 1996-2000. From 2003-06 there was growth with falling corporate leverage and volatility. Within these regimes, there are distinct episodes of volatility spikes and falls. Let’s look at both regimes by turn.

STRUCTURAL DRIVERS OF VOLATILITY: THE CREDIT CYCLE

The best structural predictor of volatility regimes and credit spreads is the lagged growth in corporate leverage. Credit cycles impact equity volatility and credit spreads with a three-year lag on average. The equity of a firm is a perpetual option on the solvency of the firm. Any increase in leverage increases liabilities and reduces or stresses shareholder equity (Assets - Liabilities = Shareholder Equity).

Typically very large surges in lending are associated with loosening lending standards and optimistic forecasts. It is usually very difficult for lending growth to accelerate rapidly without loosening standards. Such credit surges typically have the seeds of their own destruction embedded within them, because as the credit cycle matures, many of the credit extended on the back of looser lending standards cannot be re-payed. This is why we typically start by looking at the growth rate of lending as a leading indicator of volatility.

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C&I Lending YoY (Advanced 3 years) vs VIX Index

VIX, 3mma C&I Loans YoY, 3y fwd

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Net % Banks Tightening C&I Lending Standards Large Firms vs Small Firms

Large/Medium Firms Small Firms



Although the average lead over history has been about three years from the credit surge to delinquencies picking up and volatility rising, the lead time is clearly not constant and depends on the length of the credit cycle. The average lag from the relationship has been about 3 years as this is similar to the average duration of corporate debt issued. One caveat in the current environment, which has had an extended period of low rates and forward guidance, is that the average duration of debt may be higher, and thus the lag would be slightly longer.

To help us determine when the credit cycle is maturing, we can use the real fed funds rate and yield curves to proxy for slowing growth and rising debt service ratios. Higher real rates correspond to higher corporate debt payments. This tends to reduce net income and eat up more of corporate cash flow.

We can see that the real fed funds rate gives a good 2 year lead on volatility, while the inverted yield curve (a proxy for future economic growth) gives a good 3 year lead. The VP Yield Curve Index looks at all maturities and not just very short vs very long yields.

As slowing growth and rising debt service costs start to feed into company cash flow statements, we would expect equity, with the lowest claim on company assets, to experience the most stress. We can see this in the left chart below, where falling corporate cashflows relative to debt outstanding leads volatility higher by about a year. The corporate financing gap, which is a proxy for the difference between capital expenditures and internal funds, also offers a 9 month lead on volatility, again showing that when companies experience cashflow problems relative to their capex needs, the equity will come under pressure.

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Real Feds Fund Rate, 24m fwd Vix, 3mma



Ultimately as charge-off rates and defaults pick up, we will see spikes in volatility.

STRUCTURAL DRIVERS OF VOLATILITY: THE ECONOMIC CYCLE

The second big driver of market volatility is economic volatility. Periods of high market volatility have generally been correlated with periods of high economic volatility.

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US Corporate Cashflows / Debt (Advanced 1 Year)vs High Yield Spreads

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US Corporate Financing Gap / Nominal GDP (Advanced 1 Year)

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Market Volatility and Economic Volatility

Economic Volatility (LEI 12m Standard Deviation)Market Volatility (S&P 500 12m Standard Deviaton)



This is true in general, but it is particularly true during recessions when we see large spikes in the VIX and steep drawdowns in financial markets.

We can see that volatility tracks the prevalence of news stories about recessions. The VP Recession Model, reassuringly, provides a good lead on recessions and spikes in recession stories, which should help us to warn clients about imminent recessions and big spikes in volatility.

At Variant Perception we focus on leading economic indicators because they tell us about the future rather than about the past. Leading economic indicators are most closely aligned with changes to conditions in the financial markets.

Our leading indicator for the US economy (published monthly in our Leading Indicator Watch) provides a 6 month lead on US growth. As the left chart below shows, the indicator also gives a good 6 months lead on charge-off rates, which are expected to rise over the next 6 months. Similarly the right hand chart shows the VP Stress Index, which looks for signs of stress across leading indicators for different sectors of the US economy. This also gives a good 12 month lead on charge off rates.

This insight is further confirmed if you look at the VIX vs the ISM. Falls in industrial production are highly correlated to changes in volatility. This is particularly true when

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VP US Short-leading (Advanced 6 Months, inverted) vs C&I Charge-Off Rates

VP US Short Leading Indicator (6 months forward)

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declines in economic activity follow periods of high corporate leverage.

Volatility tends to move from regimes of low volatility to regimes of high volatility and back. The shift from one regime to another is driven by increases or decreases in corporate leverage and the economic cycle.

All structural indicators we look at are pointing to higher volatility ahead and shift in volatility regime. So why have we not seen more volatility so far?

So far in this cycle, historically reliable relationships between fundamental economic data and volatility are diverging. As you can see below, volatility has remained much lower than anticipated by our fundamental indicators.

A number of other historically reliable leading relationships between volatility and fundamental data have also broken down in the current cycle. For example, the chart below shows the Kalecki decomposition of profits, which historically offered a good two-year lead on volatility. The idea being that as households and governments save more and retrench from spending, corporate profits come under pressure, which leads to stress for companies.

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VIX Volatility Index vsVP Volatility Leading Index (Advanced 3 Years)

VP Leading Index for VIX, 36m fwd Vix, 3mma



We would also point out that the average maturity of debt (we use C&I loans as a proxy) has risen in this cycle, facilitated by ultra-accommodative central banks. If the average maturity of debt has risen, then the length of time between loans being extended and the corporate stress and volatility associated with repayment and renewal may also have risen.

We believe that changes in market structure help to explain the divergence of volatility from fundamental data. For all the reasons laid out above, we do not believe that the mechanisms by which volatility is generated have fundamentally changed. Central banks can elongate and affect the credit cycle and the economic cycle, but they cannot fundamentally alter how economic agents function in a market economy.

In the next section, we review changes in market structure that we believe are the main reasons for why volatility has remained low relative to fundamental indicators.

THE CHASE FOR YIELD: WE’RE ALL VOL SELLERS NOW

The legacy of this cycle will be central-bank intervention and balance-sheet expansion. As we discussed in our Feb 2015 thematic report “Understanding QE”, a key channel through which QE works is the portfolio rebalancing channel. By reducing the availability of “risk-free” assets, central banks force investors further along the risk and duration curves, lowering the risk premiums embedded in asset prices.

This makes selling volatility an attractive option to get yield. As the chart below shows, the implied yield from selling out of the money put options on the S&P 500 for 1yr offers very attractive returns relative to similar duration options in the fixed income markets. As we showed on page 4, central banks have also had a significant impact on market volatility by suppressing the fat tails in the market, making it even more attractive to sell tail risk protection to pick up yield.

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Government + Household Savings (Advanced 2 Years) vs VIX Volatility Index

VIX (3m MA)

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Since the financial crisis, it has generally worked out well for investors to aggressively sell spikes in volatility to capture extra yield. The chart below on the right shows that short volatility strategies have outperformed tail-risk hedging and long volatility strategies since 2010. Spikes in VIX have been contained as short volatility strategies act as a natural mechanism to suppress volatility.

The next chart shows that writing puts has been one of the more common ways of trying to generate alpha since the crisis. The CBOE put-writing index has a correlation to the HFRX hedge fund aggregate index of above 95%. Correlation is of course not causation, but this has been a well-known strategy for hedge funds.

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c-07

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y-0

8O

ct-0

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ar-

09

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-09

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-11

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4Ja

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Yield from selling volatility vs Fixed income yield

1y T-Bills US High Yield US Corporate 1-3 Yr S&P 500 Volatility Yield (1yr 80% OTM Put/Notional)

05101520253035404550

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Short Volatility Strategies vs VIX

CBOE Short Vol Hedge Fund Index VIX

60708090

100110120130140150

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7

Hedge Fund Volatility Strategies

CBOE Long Vol Hedge Fund Index CBOE Tail Risk Hedge Fund IndexCBOE Short Vol Hedge Fund Index



Another key trend since the crisis has been the increasing popularity of VIX futures. The explosion in VIX futures open interest has been driven in part by the rising AUM of volatility based ETFs, which are trading instruments rather than buy-and-hold instruments.

We can convert the AUM of these ETFs into a USD vega equivalent after adjusting for leverage and short interest. As we can see from the left chart below, the vega from these ETFs is often worth 30-40% of the entire open interest in the futures market, making them a very significant driver of volatility at present. The right chart shows the impact of the ETFs on the VIX futures term structure. ETFs have to constantly roll their VIX futures positions from the front month to the second month (the two most liquid contracts). Therefore, when ETFs are very long volatility, they exert steepening pressure on the VIX futures curve, as they keep selling the front month contracts to buy the next month ones. Similarly when the ETFs are net short volatility, they exert flattening pressure.

8090

100110120130140150160170180

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Aggregate Hedge Fund Performance vs Selling Puts (95.8% Correlation)

CBOE S&P 500 PutWrite Index HFRX Aggregate Index

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VIX Futures Total Open Interest

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17

Total Assets Under Management for Volatility ETFs

Long Vol ETFs Levered Long Vol ETFs Short Vol ETFs



We can see that ETF activity has had a suppressive effect on volatility in general. Although ETF investors tend to be generally long volatility, any spikes in volatility are treated as opportunities to get short. We can see how quickly net positioning flips from long to short during the vol spikes in summer 2011, end of 2014 and summer 2015. We believe this reflects the wider mentality of selling volatility to pick up extra yield that we discussed above in this section.

Reviewing the speculative net positioning of VIX futures using the commitment of traders report, we can see that in vega terms, speculative short positioning reached an all-time low back in September. Although this has eased a little, overall speculators still remain very short volatility.

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Net Equivalent USD Vega Notional Exposure of Volatility ETFs Bns vs 2m/1m VIX Futures Premium(XIV, SVXY, VXX, TVIX, UVXY, VIXY, ZIV)

Net Equivalent USD Vega Notional Exposure ETFs 2m/1m VIX Futures Premium

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Net Equivalent USD Vega Notional Exposure of Volatility ETFs Bns as % of Total VIX Futures Open Interest (XIV, SVXY, VXX, TVIX, UVXY, VIXY, ZIV)

As percentage of VIX Futures Open Interest

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Net Equivalent USD Vega Notional Exposure of Volatility ETFs Bns vs VIX(XIV, SVXY, VXX, TVIX, UVXY, VIXY, ZIV)

Net Equivalent USD Vega Notional Exposure ETFs VIX



TACTICAL DRIVERS OF VOLATILITY: THE VP CORRECTION SIGNAL AND GLOBAL LIQUIDITY

Volatility is not an academic question, and the only reason we care about forecasting changes to volatility regimes and specific spikes in volatility is to make money. We have built a variety of tools at VP to predict crashes and surges in volatility. In the following section we outline tools that we use on a daily basis.

The two best predictors we have found for crashes are 1) rising volatility across asset classes at the same time as widening credit spreads, money market and interbank lending rates and 2) herding in financial markets as measured by cross asset class correlations. These tools for forecasting crashes work on a daily basis.

Since we last wrote our August 2014 report, we have simplified our original correction/crash signal to be more robust to capturing shifts in market regimes. The idea is still the same: to capture simultaneous stress across different asset classes.

Our correction indicator does not necessarily forecast market corrections, but it tells you when the conditions for a correction are present. This year we have warned clients twice of potential market corrections on January 5th before an almost 10% market sell-off and on June 15th before Brexit.

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VIX vs Vega USD Notional Implied by Net Speculative Position(assume 1,000 USD vega per contract)

Net Speculative Vega Notional, mn USD (LHS) VIX Future (RHS)



Whenever the signal triggers, it offers compelling risk-reward to buy out of the money puts, to capture both spikes in volatility as well the underlying move in the S&P 500.

Investors should also be buyers of volatility when there is herding in financial markets. In the following chart, you can see our signal for rapidly rising cross-asset class correlation. Spikes in cross asset correlations tend to precede large rises in volatility.

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Variant Perception Strong Correction Signal

Strong Correction S&P

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Strong Correction Signal

Strong Correction VIX

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Global Cross Asset Correlations vs VIX

VIX Global Cross Asset Correlation



When all asset classes become correlated, this is a classic sign of herding in financial markets. Investors all begin to imitate each other and take their cues from prices rather than fundamentals. In this situation, most traders will attempt to sell at the same time, and volatility will spike.

WHEN BAD NEWS IS PRICED INTO MARKETS

Volatility is serially correlated, but extreme spikes in volatility are hard to sustain. Generally central banks react, and markets price in extremely bad news. This makes global liquidity conditions another good predictor of spikes and falls in volatility. We can proxy global liquidity by using money growth or excess liquidity. When there is a lot of liquidity, financial markets tend to do well, but when liquidity contracts, markets tend to suffer.

As you can see from the chart below, rising liquidity tends to lead to lower volatility and falling liquidity tends to lead to higher volatility.

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DM Real M1 (GDP-Weighted, Local Currency) Advanced 9 Months vs MSCI World

MSCI World (DM) YoY, 3mma (LHS) DM Real M1 YoY, 3mma, 9m fwd (RHS)

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DM Real M1 (GDP-Weighted, Local Currency) Advanced 9 Months vs VIX Volatility Index

VIX (LHS) DM Real M1 YoY, inverted, 9m fwd (RHS)



It is when markets have effectively priced in the bad news that volatility tends to collapse. At Variant Perception, we have developed a number of tactical signals that try to capture ‘peak panic’ in the market to generate buy signals for the market. These signals do not go off very often and when they do, many signals tend to go off at the same time.

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Volatility And Credit Post-Crash Relief Rally

Strong Post-Crash Buy S&P

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S&P 500 Modified Zweig Thrust Buy Signal

Modified Zweig Thrust Buy Signal S&P 500



RECIPIENTS ARE NOT PERMITTED TO FORWARD THIS PUBLICATION WITHOUT THE EXPRESS WRITTEN CONSENT OF VARI-ANT PERCEPTION®. VARIANT PERCEPTION DISTRIBUTES ITS PUBLICATIONS ON A PAID SUBSCRIPTION BASIS ONLY.© Copyright 2017 by VP Research, Inc.

VARIANT PERCEPTION is a federally registered trademark of VP Research, Inc.It is a violation of US federal and international copyright laws to reproduce all or part of this publication by email, xerography, facsimile or any other means. The Copyright Act imposes liability of $100,000 per issue for such infringement. The publica-tions of Variant Perception are provided to subscribers on a paid subscription basis. If you are not a paid subscriber of the reports sent out by Variant Perception and receive emailed, faxed or copied versions of the reports from a source other than Variant Perception you are violating the Copyright Act. This document is not for attribution in any publication, and should not be disseminated, distributed or copied without the explicit written consent of Variant Perception.Disclaimer: Variant Perception’s publications are prepared for and are the property of Variant Perception and are circulated for informational and educational purposes only. The content of this report is intended for institutions and professional advisers only. This report is not intended for use by private clients. Recipients should consult their own financial and tax advisors before making any investment decisions. This report is not an offer to sell or a solicitation to buy any investment security. Variant Perception’s reports are based on proprietary analysis and public information that is believed to be accurate, but no representations are made concerning the accuracy of the data. The views herein are solely those of Variant Perception and are subject to change without notice. Variant Perception’s principals may have a position in any security mentioned in this report.

All data is sourced from Bloomberg unless otherwise stated.

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