Can the SKEW/VIX predict market corrections?

Over the past few years, we have developed a series of indicators of market complacency in order to prevent investors of a sudden spike in price volatility after a long period of calm. One of them looks at the divergence between the Economic Policy Uncertainty EPU index (Baker et al., 2016), a measure of economic uncertainty based on newspaper coverage frequency, and the VIX. Figure 1 (right frame) shows that over the past few years, the EPU index has been displaying a much higher risk level that would be inferred from the options market. Some also watch the activity in the TED spread, which has been distorted since the financial crisis due to a tightening up of regulations and changes in money market funds (figure 1, right frame), and notice when it starts to stir.

Figure 1

Fig1 New

Source: Eikon Reuters

An interesting one looks at the behavior of the SKEW index relative to the VIX. As we previously mentioned, since the crash of October 1987 (Black Monday, DJ down 22.6%), investors have realized that S&P500 tail risk (returns that are under 2 or more standard deviations below the mean) is significantly greater than under a log-normal distribution. Therefore, the ‘skew’ measures the perceived tail risk of the market via the pricing of OTM options. A rise in skew indicates that ‘crash protection’ is in demand among institutional investors. A ‘low VIX/high skew’ combination says that the market is complacent, however the ‘big players’ perceive far more tail risk than usually. Therefore, a surprise increase in realized volatility may not be too far away.

Figure 2 shows the times series of the VIX, the SKEW and the weekly change in the SP500. As you can see, a sustain period of falling VIX and rising SKEW is generally followed by a sharp spike in implied volatility. For instance, while the VIX was approaching the 10 level in the summer of 2014, the SKEW index had been on a rise in the year preceding that period, rising from 113 in July 2013 to 146 in September 2014. In addition, the Fed stepped out of the bond market in October 2014 (end of QE3) and therefore exacerbated investors’ concern on the market. We then saw huge moves in both equities and bonds in the middle of that month and the 18-month period that followed was basically a flat equity market with some significant drawdowns (October 2014, August 2015 and December 2015 / January 2016). The second period we highlighted was in 2016 / 2017, which was marked by an extremely low volatility and a rising SKEW. We saw that things reverted drastically in the February VIX-termination event. Following this event, we eventually had another period of falling VIX and rising SKEW in the next months before the October sell-off. We can notice in the chart that the SKEW does not stay above the 150 threshold for too long, hence a VIX trading at around 12 and a SKEW at 150 were last summer were indicating a potential market turmoil.

Figure 2

Fig2 New (1)

Source: Eikon Reuters

The SKEW/VIX behavior does not predict a market correction all the time (i.e. the SKEW had been falling for months prior the August 2015 sell-off), however we think that investors should remain cautious when the SKEW starts to rise above 140 and the VIX remains low. While a falling VIX would push investors to increase their leverage (target vol strategies or risk parity funds), we think that looking at the two variables for portfolio construction could help reduce the potential drawdowns.

Crude Oil (WTI) and the Loonie

We realize that after all these years looking at the market, our approach to currencies and global macro has remained quite simple and cyclical. We usually start our day by looking at the USDJPY (and AUDJPY), USDCNH and CSI 300 index charts that [kind of] describe us the overnight session. If we see huge moves in those charts, we know something important has happened in the ‘Est’ during the night that must be read and understood.

Since the beginning of this commodity meltdown (that analysts named ‘The End of the Super-Cycle’), each [bad] news coming from giant China usually had an impact on commodity prices, bringing down commodity currencies and especially the dollar-bloc ones (CAD, AUD and NZD). In today’s article, we focus on the Canadian Dollar (CAD or Loonie) and how it has reacted to the Oil prices decline over the past year. Since the beginning of 2014, USDCAD (orange line) has appreciated by 33% as the Canadian dollar has been dramatically impacted by the falling prices of oil (WTI, white line) now trading at $32.80 per bbl.

However, as you can see it on the chart below, even though the two underlying assets have been moving ‘together’ [most of the time] over the past year (i.e. lower oil prices implies CAD depreciation versus the US Dollar), the correlation can change over time. For instance, the 5-day correlation between USD and WTI stands now at -90.18%, but have also higher and even positive during small periods of time (mid-January or early December last year).


(Source: Bloomberg)

The reason why we like to watch correlation between assets classes is for the risk management and FX and commodity positioning. We have to admit that since the Fed started to consider shifting towards a tightening monetary policy cycle (i.e. raising interest rates), correlations have been much stronger and being diversified (i.e. not too much exposure to the US Dollar) can be difficult sometimes.


The chart below shows a quick analysis of EURUSD and VIX Index over the past six months. As you can see, the 20-day correlation between the two underlying assets has switched from a negative 80 in Mid-March to a positive 80.6% today. If you are a global macro trader, I personally believe that it is important to notice those changes between different asset classes, so you can see how a particular currency will react in case of a volatile day.

During the ‘Black Monday’ session this year (August 24th), the VIX Index soared above 40 and one of the surprising assets rallying was the Euro. On that day, EURUSD surged above the 1.17 level, up 350 pips in a few hours. Sell-side research started to call it the New Safe-Haven Currency, therefore reviewing its 3-month and 6-month to the upside.

Keep a small long EURUSD in your book ahead of the FOMC

In my opinion, I think it could be good to keep a long position on EURUSD ahead of the FOMC meeting this evening in case we see a bit of volatility.

Based on the macro situation in the US, a persistent moderate nominal growth and a poor core PCE deflator at 1% (Bloomberg PCE MBXYH Index), I think a no-hike scenario will make more sense. However, a 25bps is still in the game and wouldn’t have dramatic consequences for the market; but in that case, we could see a bit of equity sell-off, a higher VIX and therefore a higher EURUSD. An interesting level on the upside will be 1.1380; a break out could potentially bring EURUSD to 1.1450. On the downside, 1.1220 is the key level where I should potentially keep a safe stop.


(Source: Bloomberg)

Introducing the TechCrunch Bubble Index

Today, I thought it could be interesting to introduce the TCB Index that has been making the ‘headlines’ lately. First of all, what does the Index tell us? The TCB index counts the number of headlines on TechCrunch (blog: see over the past 90 days relating to startups raising money (‘startup fundraise’ means that the amount raised was at least $100K and less than $150mio). Therefore, the higher the index, the better the fundraising environment.

For instance, if we have a look at the chart below (source: Todd Schneider’s website), we can see that the startups business has been going through a difficult time for the past few months. On November 16, 2014, the TCB Index was at 209, which means there were 209 TechCrunch headlines about startup fundraise in the 90 days preceding that (roughly 2.3 per day), down from a high of 346 in April this year.


 (Source: Todd Schneider)

Quick thoughts on Twitter’s IPO and the dotcom bubble 2.0

It makes me think the way I felt when I saw the headline ‘Twitter files for IPO’ last year. As a reminder, the company sold 70mio shares on the IPO (November 6, 2013) at $26, raising $1.82bn in its Initial Public Offer. In addition, I was asking myself how a company, that wasn’t profitable at the time it went public, could be valued over 10 billion dollars?

In its first public financial statement, Twitter reported $79.4mio in losses for the year 2012 (after a negative net incomes of $67.32mio in 2010 and 128.3mio in 2011), and was predicting even steeper losses for 2013 (guess what: losses reached 645.32mio that year).

I concluded that we were in a second dotcom bubble. Below I added a chart from the Wall Street Journal (which sums up briefly what I just said).


(Source: Wall Street Journal)

 Is it just the beginning?

Today, as the TCB Index shows us, there is less money in the startups business and we are starting to see some weaknesses. For instance, we heard lately that Fab (a design-focused commerce company), a once-to-be Silicon Valley’s darling valued $1bn back in June 2013, is about to sell for $15mio according to some sources (the acquire: PCH International) as it had struggle to sustain its growth. With the Fed considering starting raising rates for the first time since 2009, are we going to experience more of those cases?

 ‘A thing is worth only as much as it can be sold for.’

Publilius Syrus

Introducing the Swaptions (and IRS)

Today, let’s expand our finance knowledge and study what HF portfolio managers and IB traders ‘constantly’ look at: swaptions and the implied interest rate volatility. A swaption, as you may know, is an option to enter an IRS (interest rate swap) with a specified rate at no cost on a future date.

For those who are not familiar with swaps, let’s review quickly the structure of a ‘vanilla’ IRS.

An IRS is a bilateral agreement to swap a fixed rate of interest for a floating rate of interest. It is a derivative contracts (traded OTC) and it involves two counterparties (at least), the fixed receiver (receives a fix rate) and the fixed payer (floating rate). Unlike currency swaps, principal amounts are not exchange in an IRS ‘vanilla’ contract, and only the difference between the fixed and the floating rate is paid/received. In order to trade (hedging/speculating), you need four parameters: the date, the notional amount, fixed rate and the floating rate.

At the inception of the swap, the Net Present Value or the sum of expected PnL should add up to zero. If you type IRS on Bloomberg, you get to the swap manager page that you can see below.

Irs page

(Source: Bloomberg)

This contract is a 5-year IRS contract, 10Mio USD nominal between Leg 1 ‘Receiver’ and Leg 2 ‘Payer’. Therefore, with a fixed coupon of 1.796627% and October 10th as the effective date (date when interest begins to accrue, the first fixed payment will occur 6 months after that date (on April 10 2014) totalling an amount of 89,831.35 USD.

Fixed rate payment = Fixed rate * (Nb Days / 360 basis) * Notional

Nb of Days = 180, therefore Fixed Payment rate = 89,831.35 USD

On the other side, floating payments will occur every quarter, using the 3-month LIBOR as a benchmark (USD0003M Index). With a 3-month LIBOR trading at 0.23110% at the moment, the first floating-rate payment will occur on January 12 2014 (94 days) totalling an amount of 6,034.28 USD (same computation as the Fixed –rate payment replacing Fixed rate by floating rate). On page 9 (Cashflow, see appendix), you will see all the future payment details.

As all the future payment of Leg 1(Fixed Receiver) will rely on the evolution of the forward LIBOR curve, the swap valuation changes over time and therefore existing swaps become off-market swaps. For the curious ones, you can easily find the math equation on Internet, but the important thing to remember is that the payer (Leg 2) will start to lose money if interest rate started to fall unexpectedly.

Here we are now, back to swaptions and the 1Y10Y implied volatility that we like to watch quite a bit. There are two kinds of swaptions, a payer swaption (option to pay fixed-rate, eq. to call option with PnL rising if rates are rising) and a receiver swaption (option to receive fixed-rate, eq. to a put option with PnL rising if rates are falling).  If you buy a 1Y10Y 2% receiver swaption, it basically means that you have the right to receive a 2-percent rate on a 10 year basis starting in 1 year. Therefore, as we use the VIX in order to measure the market expectations of near-term volatility in the US stock market (S&P500), we use the 1Y10Y to ‘measure the temperature’ of the interest rate market. Quants use generally the Black’s model, a derived version of the Black and Scholes model (used for calls and puts), as a standard way of quoting prices on swaptions (two other methods of stochastic interpolation to model LIBOR forward rates are CEV and SABR.

If we have a look at 1Y10Y JPY implied volatility back in April/May 2013, we saw a surge in JPY volatility after the BoJ announced its QE plan which consists in doubling its monetary base within the next 2 fiscal years. As you can see it on the graph below, when the IR volatility (white/blue line) rose more than 60% in May, the ten-year JGB yield doubled and touched 1% (May 29th), while Japanese stocks dropped 7% the same day with a USDJPY down 3 figures.

Vol irs

(Source: Bloomberg)

Investors are still concerned about the volatility of the bond market which would force domestic financial institutions to reduce their JGB holdings. As a reminder, more than 90% of the Japanese government debt is hold by domestic ‘investors’, and 95% of this amount is held by institutional investors (GPIF, Japan Post Bank.. and of course the BoJ). Domestic banks and small/midsize financial institutions account for more or less 29% now, and still remember the ‘VaR shock’ of summer 2003 when 10 JGB yield tripled from 0.5% to 1.6% in June.

According to a study done by JP Morgan [a little while ago], a rise in the ‘JGB volatility’ increasing interest rate by 100bps would cause a loss of 10Tr Yen for Japanese banks. Therefore, if you are holding a LT position on USDJPY or any other asset (bonds, equities), you should pay close attention to the forward curve and the 1Y10Y implied volatility we just presented you.

Appendix: Cash Flows of the IRS

Cash flows

(Source: Bloomberg)

The VIX/VXV Ratio

Last time, we talked about the convergence and divergence between the VIX and SKEW and what sort of information we could get from that. Today, let me introduce you to the VIX/VXV ratio combined with an application on the US Stock market.
But first, let’s start with the definitions of all the indexes:

– As a reminder, the ‘SKEW’ is an indicator that computes the implied volatility of the S&P500 from OTM the options and therefore ‘measures fat tails’ and investors fear.

– The VIX index, introduced in 1993 by the Chicago Board Options Exchange (CBOE), measures the 30-day volatility implied by the ATM S&P500 option prices. The components of the VIX are basically near/next – term put and call options.

– The VXV index (that you can also find in Bloomberg) is designed to be a constant measure of 3-month implied volatility of the S&P 500. It uses the same methodology and generalized formula as the VIX index.

If you are familiar with the term structure, investors and traders can use the historical data of the last two indexes (VIX and VXV) in order to gain a better understanding of the market’s expectations of the future volatility. As you can see it on the graph below, for the past few years (December 11 – June 14), the VIX/VXV ratio (in green) has been oscillating around 0.85 – 0.90 with a low of 0.71 (16-Mar-12) and a high of 1.0645 (02-Mar-14). The ratio has remained most of its time below 1.00, which is logical as the term structure should have an increasing concave shape (in theory). Basically, a ratio superior to one would mean that investors are more concerned about the near term fluctuations (usually a correction) of the S&P500 and often comes from an appreciation of the VIX due to market events such as FOMC meetings or companies’ earnings.


(Source: Bloomberg)

In the graph, we drew a white line which has (‘kinda’) acted as a support for the VIX-to-VXV ratio (around 0.82). However, when we look at the S&P500 chart (white/blue), we can see that most of the times that we hit this ‘imaginary’ resistance, the ratio rebounded and we either saw a stagnation or correction in the stock market.

For those who don’t agree with us concerning the application, they just to have to remember that the VXV provides a valuable tool for traders to identify the term structure of S&P 500 implied volatility and that a single value of the (SPX option) implied volatility is not enough.

Money Supply and Aggregates

Since GFC, central banks have injected trillions of dollars into the system in order to stimulate the economy and avoid a global liquidity crisis. We are now aware of how much important is the money supply for the financial markets as guru Bill ‘PIMCO’ Gross once wrote it in one of its Investment Outlook review: ‘There are bubbles everywhere…’ (Bond, equity or property).

However, even if we have a vague idea of the definition of ‘money supply’, which we would usually describe as the amount of money in the economy, let’s review the different ways to define ‘money’.

If we have a look at the ECB’s website for instance, we can find three different Euro area monetary aggregates:

  • M1: Narrow aggregate or ‘narrow money’ which takes into account the currency (notes and coins) in circulation in addition to overnight deposits (balances which can immediately be converted into currency or used for cash payments.
  • M2: Intermediate aggregate or ‘intermediate money’ comprises M1, deposits with an agreed maturity up to 2 years (non-transferable deposits which cannot be converted into currency before an agreed fixed term without penalty) and deposits redeemable at notice.
  • M3: Broad aggregate or ‘broad money’ comprises M1, M2, repurchase agreements, Money Market Fund (MMF) shares/units and debt securities up to 2 years.


 (Source: ECB website)

In the US, the Fed only publishes the M1 and M2 aggregates, as they announced they would cease publication of M3 in the spring of 2006:

  • M1: Total amount of cash/coins outside of the private banking system in addition to the amount of demand deposits, traveller’s checks and other checkable deposits.
  • M2 comprises M1 plus most saving accounts, money market accounts, retail MM mutual funds, and small denomination time deposits (CDs of under $100,000).

CBOE Skew vs. VIX

Today, we would like to speak about the convergence and divergence between the SKEW and the VIX. We guess that everybody is familiar with the VIX that reflects a market estimate of future volatility (introduced in 1993 by the Chicago Board Options Exchange – CBOE, measures the 30-day volatility implied by the ATM S&P500 option), however let me introduce you to the CBOE SKEW index.

Since the crash of October 1987 (Black Monday, DJ down 22.6%), investors have realized that S&P500 tail risk (returns that are under 2 or more standard deviations below the mean) is significantly greater than under a lognormal distribution. Therefore, the ‘skew’ measures the perceived tail risk of the market via the pricing of OTM options. A rise in skew indicates that ‘crash protection’ is in demand among institutional investors (also called the ‘big players’ in the SPX options market).
A ‘low VIX/high skew’ combination says that the market is complacent, however the ‘big players’ perceive far more tail risk than usually. Therefore, a surprise increase in realized volatility may not be too far away.

If we have a look at the chart below (which represents in fact the VIX and the SKEW), we can see that VIX (green) is sitting at very low levels at the moment (13.57%) and may need to release some ‘energy’. In blue, we have the Skew index which has been fluctuating within the 125 – 130 range for the past few weeks (now trading at 129.25). We recommend you closely watch your positions when the index is approaching the high of the ‘historical’ 100 – 150 range.

(Source: Bloomberg)

If you extend the historical chart since 1990, you can see that the perception of increased tail risk can be early (skew was above 130 level in 2005 already while the VIX was trading at 10.0 at that time), but it definitely remains one the ‘fear’ indicators watched by Wall Street players (with CSFB – Credit Suisse Fear Barometer – index).

Introducing the Butterfly

After our focus on Risk Reversal, let’s have a look at another important options strategy very well-known in the market, especially FX, the butterfly. The Risk Reversal lecture gave you an idea of the most important variable in the market, the implied volatility.

In the Black Scholes model, an option pricing (European options) model developed in the early 70s, we assume that the volatility σ of the underlying is constant. However, we saw that implied volatility varies over time (constantly in fact) among the different strike prices, and this discrepancy is know as the volatility skew (or smiles sometimes). In Options, futures and other financial derivatives (a must read for traders/sales/brokers), John Hull considers that currencies tend to provide the so–called volatility smile in general, which would mean that the price of a 25 OTM call (maturity 1 week for instance) would be equal to the price 25 OTM put. Which in fact is not true as we saw last time, because it would mean that the RR(25-Delta) would be equalled to zero in that case, therefore we also observe a skew in the FX market.

The Butterfly is a neutral option strategy that uses four call options contracts with the same expiration but three different strikes. It is a limited risk, non-directional options strategy that is designed to earn big (but limited) profits but with a low probability. As you can see it below, there are two types of strategies, the long and short Butterfly spread.

1. Long Butterfly Spread (Short two calls at middle strike, and long one call each at the lower and upper strike)


(Source: The Option Guide)

Trader is looking for underlying stock to achieve a specific price target at expiration of the options. In this case, he targets a stock at $40 per share at maturity (April this year), therefore the strategy used was:

–  Buy one April 30 Call
–  Buy one April 50 Call
–  Sell two April 40 Calls

At maturity, the trader will generate a profit if the underlying stock trades within the Downside and Upside Breakeven (BE), which is to say between 35 and 45. It means that you expected volatility to remain low in the coming weeks/months.

2. Short Butterfly Spread (Long two calls at middle strike, and Short one call each at the lower and upper strike)

(Source: The Option Guide)

Trader is looking for a volatility spike which would either increase or decrease the price of the stock sharply. Therefore, the strategy used was:

–  Short one April 30 Call
–  Short one April 50 Call
–  Buy two April 40 Calls

At maturity, the trader will generate a profit if the underlying stock trades outside the Downside and Upside Breakeven (BE) range, which is to say either below 35 or above 45.

The potential profit and loss are both very limited. In essence, a butterfly at expiration has a minimum value of zero and a maximum value equal to the distance between either wing and the body. Even if there is a limited risk exposure, both strategies usually offer small returns (compared to straddles for instance).

Bloomberg application: If you pick up one currency pair, let’s say EURUSD for instance. Then if you type OVDV (for option volatility surface), you get the page below which shows ATM implied volatilities (with all the major maturities), the 25 RR and the 25 BF (butterfly). According to the table below, the RR (25-Delta 1 Month) is trading at -0.985 (bid) with a BF (25-Delta 1 Month) at 0.125 (bid).


(Source: Bloomberg)

Butterfly is the difference between the average volatility of the call price and put price with the same moneyness level (25-Delta) and the ATM volatility level. For instance a BF 25 could be expressed by the following formula:

BF25 = (σ25C + σ25P) /2 – σATM

As Risk Reversal measure the slope (skewness), butterfly spreads measure the curvature (kurtosis). As a reminder, in statistics, the kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. Data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly and have heavy tails. Data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak. In short, the higher the Butterfly spreads, the more ‘peaked’ is your implied volatility curve.

Help to Buy Scheme and UK Housing Market

Help To Buy scheme
Help to Buy is a government scheme designed to help borrowers with deposits of just 5 per cent get on to the property ladder.
The first part of the scheme (Equity Loan) was launched in April last year and offers loans up to 20% of the cost of a new-build home (up to the value of £600K). The loan will be interest-free for the first five years, then in the 6th year you will be charged an annual 1.75% fee (of the loan), which will increase every year (using the retail price index plus 1%). The second part of the scheme, called the Mortgage Guarantee, applies to all property purchases (also with a house worth £600K maximum) with a minimum deposit of 5% from the buyers. For instance, as you can see it on the picture below, with a £10K deposit, you can buy a house in the UK worth £200K (it won’t be a house in London though).


(Source: Government website)

UK Housing Market
With an 8% increase in average in 2013 according to indexes such as Nationwide (8.4%) or Halifax (7.5%), the housing market has helped overall UK growth along at the fastest pace in three years. Britain’s Q3 GDP printed eventually at 0.8% QoQ (1.9% YoY); we heard lately the IMF raising its growth forecast from 1.9% to 2.4% for the year 2014.The average asking price in the UK reached new highs of £243,000 in January (Rightmove), with London at the first place (12% YoY increase) and its expensive areas (Average sale price in September 2013 was £4.4 million in Knightsbridge and Belgravia).
If you read analysts expectations for the year 2014, most of them expect an 8% increase also, with Greater London being the best opportunity. Thanks to the Help to Buy scheme, house prices in Greater London will tend to catch up with the prime central London ones. Moreover, as more lenders come on board (Latest: Barclays is offering a fee-free three-year fixed rate at 5.35% and a five year fix at 5.49% since yesterday, Tuesday 21 January), competition is driving rates down little by little and could attract potential buyers.

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