VIX Archives - QUANTITATIVE RESEARCH AND TRADING

July 14, 2020

Riders on the Storm

The Worst Volatility Scare for Years

February 2018 was an insane month for stocks, wrote CNN:

A profound inflation scare. Not one but two 1,000-point plunges for the Dow. And a powerful comeback that almost went straight back up.

The CNN story-line continues:

The Dow plummeted more than 3,200 points, or 12%, in just two weeks. Then stocks raced back to life, at one point recovering about three-quarters of those losses.

Fittingly, February ended with more drama. The Dow tumbled 680 points during the month’s final two days, leaving it down about 1,600 points from the record high in late January.

The headline in the Financial Times was a little more nuanced, focusing on the impact of the market turmoil on quant hedge funds:

Quant Funds Get Trashed

The FT reported:

Computer-driven, trend-following hedge funds are heading for their worst month in nearly 17 years after getting whipsawed when the stock market’s steady soar abruptly reversed into one of the quickest corrections in history earlier in February.

The carnage amongst hedge funds was widespread, according to the article:

Société Générale’s CTA index is down 5.55 per cent this month, even after the recent market rebound, making it the worst period for these systematic hedge funds since November 2001.
Man AHL’s $1.1bn Diversified fund lost almost 10 per cent in the month to February 16, while the London investment firm’s AHL Evolution and Alpha funds were down about 4-5 per cent over the same period. The flagship funds of GAM’s Cantab Capital, Systematica and Winton lost 9.5 per cent, 7.2 per cent and 4.6 per cent* respectively between the start of the month and February 16. Aspect Capital’s Diversified Fund dropped 9.5 per cent in the month to February 20, while a trend-following fund run by Lynx Asset Management slumped 12.7 per cent. A leveraged version of the same fund tumbled 18.8 per cent. One of the other big victims is Roy Niederhoffer, whose fund lost 21.1 per cent in the month to February 20.

Painful reading, indeed.

Traders conditioned to a state of somnambulance were shocked by the ferocity of the volatility spike, as the CBOE VIX index soared by over 200% in a single day, reaching a high of over 38 on Feb 5th:

Indeed, this turned out to be the largest ever two-day increase in the history of the index:

This Quant Strategy Made 27% In February Alone

So, for a quant-driven options strategy that is typically a premium seller, February must surely have been a disaster, if not a total wipe-out. Not quite. On the contrary, our Option Trader strategy made a massive gain of 27% for the month. As a result strategy performance is now running at over 55% for 2018 YTD, while maintaining a Sharpe Ratio of 2.23.

You can tell that the strategy has a tendency to collect option premiums, not only because the strategy description says as much, but also from the observation that over 90% of strategy trades have been profitable – one of the defining characteristics of volatility strategies that are short-Vega, long-Theta. The theory is that such strategies make money most of the time, but then give it all back (and more) when volatility inevitably spikes. While that is generally true, in my experience, that clearly didn’t occur here. So what’s the story?

One of the advantages of our Algo Trading Platform is that it not only reports in detail the live performance of our strategies, but it also reveals the actual trades on the site (typically delayed by 24-72 hours). A review of the trades made by the Option Trader strategy from the end of January though early February indicates a strongly bullish bias, with short put trades in stocks such as Netflix, Inc. (NFLX), Shopify Inc. (SHOP), The Goldman Sachs Group, Inc. (GS) and Facebook, Inc. (FB), coupled with short call trades in VIX ETF products such as ProShares Ultra VIX Short-Term Futures (UVXY) and iPath S&P 500 VIX ST Futures ETN (VXX). As volatility began to spike on 2/5, more calls were sold at increasingly fat premiums in several of the VIX Index ETFs. These short volatility positions were later hedged with long trades in the underlying ETFs and, over time, both the hedges and the original option sales proved highly profitable. In other words, the extremely high levels of volatility enabled the strategy to profit on both legs of the trade, a highly unusual occurrence. Meanwhile, while it was hedging its bets in the VIX ETF option trades, the strategy was becoming increasingly aggressive in the single stocks sector, taking outright long positions in Baidu, Inc. (BIDU), Align Technology, Inc. (ALGN), Netflix, Inc. (NFLX) and others, just as they became trading off their lows in the second week of the month. By around Feb 12th the strategy recognized that the volatility shock had begun to subside and took advantage of the inflated option premia, selling puts across the board, in particular in the technology (Tesla, Inc. (TSLA), NVIDIA Corporation (NVDA)) and retail sectors (GrubHub Inc. (GRUB), Alibaba Group Holding Limited (BABA)) that had suffered especially heavy declines. Many of these trades were closed at a substantial profit within a span of just a few days as the market stabilized and volatility subsided. The strategy broadened the scope of its option selling as the month progressed, initially recovering the entirety of the drawdown it had initially suffered, before going on to register substantial profits on almost every trade.

To summarize:

Like many other market players, the Volatility Trader strategy was initially caught on the wrong side of the volatility spike and suffered a significant drawdown.
Instead of liquidating positions, the strategy began hedging aggressively in sectors holding the greatest danger – VIX ETFs, in particular. These trades ultimately proved profitable on both option and hedge legs as the market turned around and volatility collapsed.
As soon as volatility showed signed of easing, the strategy began making aggressive bets on market stabilization and recovery, taking long positions in some of the most beaten-down stocks and selling puts across the board to capture inflated option premia.

Lesson Learned: Aggressive Defense is the best Options Strategy in a Volatile Market

If there is one lesson above all others to be learned from this case study it is this: that a period of market turmoil is a time of opportunity for option traders, but only if they play aggressively, both in defense and offense. Many traders run scared at times like this and liquidate positions, taking heavy losses in the process that can prove impossible to recover from if, as here, the drawdown is severe. This study shows that by holding one’s nerve and hedging rather than liquidating loss-making positions and then moving aggressively to capitalize on inflated option prices a trader can not only weather the storm but, as in this case, produce exceptional returns.

The key take-away is this: in order to play aggressively you have to have sufficient reserves in the tank to enable you to hold positions rather than liquidate them and, later on, to transition to selling expensive option premiums. The mistake many option traders make is to trade too close to the line in term of margin limits, resulting in a forced liquidation of positions that would otherwise have been profitable.

You can trade the Option Trader strategy live in your own brokerage account – go here for details.

March 31, 2020

Correlation Cointegration

In a previous post I looked at ways of modeling the relationship between the CBOE VIX Index and the Year 1 and Year 2 CBOE Correlation Indices:

http://jonathankinlay.com/2017/08/modeling-volatility-correlation/

The question was put to me whether the VIX and correlation indices might be cointegrated.

Let’s begin by looking at the pattern of correlation between the three indices:

If you recall from my previous post, we were able to fit a linear regression model with the Year 1 and Year 2 Correlation Indices that accounts for around 50% in the variation in the VIX index. While the model certainly has its shortcomings, as explained in the post, it will serve the purpose of demonstrating that the three series are cointegrated. The standard Dickey-Fuller test rejects the null hypothesis of a unit root in the residuals of the linear model, confirming that the three series are cointegrated, order 1.

Vector Autoregression

We can attempt to take the modeling a little further by fitting a VAR model. We begin by splitting the data into an in-sample period from Jan 2007 to Dec 2015 and an out-of-sample test period from Jan 2016 to Aug 2017. We then fit a vector autoregression model to the in-sample data:

When we examine how the model performs on the out-of-sample data, we find that it fails to pick up on much of the variation in the series – the forecasts are fairly flat and provide quite poor predictions of the trends in the three series over the period from 2016-2017:

Conclusion

The VIX and Correlation Indices are not only highly correlated, but also cointegrated, in the sense that a linear combination of the series is stationary.

One can fit a weakly stationary VAR process model to the three series, but the fit is quite poor and forecasts from the model don’t appear to add much value. It is conceivable that a more comprehensive model involving longer lags would improve forecasting performance.

January 1, 2019

My Big Fat Greek Vacation

LEARNING TO TRUST A TRADING SYSTEM

One of the most difficult decisions to make when running a systematic trading program is knowing when to override the system. During the early 2000’s when I was running the Caissa Capital fund, the models would regularly make predictions on volatility that I and our head Trader, Ron Henley, a former option trader from the AMEX, disagreed with. Most times, the system proved to have made the correct decision. My take-away from that experience was that, as human beings, even as traders, we are not very good at pricing risk.

My second take-away was that, by and large, you are better off trusting the system, rather than second-guessing its every decision. Of course, markets can change and systems break down; but the right approach to assessing this possibility is to use statistical control procedures to determine formally whether or not the system has broken down, rather than going through a routine period of under-performance (see: is your strategy still working?)

GREEK LESSONS

So when the Greek crisis blew up in June my first instinct was not to start looking immediately for the escape hatch. However, as time wore on I became increasingly concerned that the risk of a Grexit or default had not abated. Moreover, I realized that there was really nothing comparable in the data used in the development of the trading models that was in any way comparable to the scenario facing Greece, the EU and, by a process of contagion, US markets. Very reluctantly, therefore, I came to the decision that the smart way to play the crises was from the sidelines. So we made the decisions to go 100% to cash and waited for the crisis to subside.

A week went by. Then another. Of course, I had written to our investors explaining what we intended to do, and why, so there were no surprises. Nonetheless, I felt uncomfortable not making money for them. I did my best to console myself with the principal rule of money management: first, do not lose money. Of course we didn’t – but neither did we make much money, and ended June more or less flat.

COMEBACK

After the worst of the crisis was behind us, I was relieved to see that the models appeared almost as anxious as I was to make up for lost time. One of the features of the system is

that it makes aggressive use of leverage. Rather like an expert poker player, when it judges the odds to be in its favor, the system will increase its bet size considerably; at other times it will hunker down, play conservatively, or even exit altogether. Consequently, the turnover in the portfolio can be large at times. The cost of trading high volume can substantial, especially in some of the less liquid ETF products, where the bid/ask spread can amount to several cents. So we typically aim to execute passively, looking to buy on the bid and sell on the offer, using execution algos to split our orders up and randomize them. That also makes it tougher for HFT algos to pick us off as we move into and out of our positions.

So, in July, our Greek “vacation” at an end, the system came roaring back, all guns blazing. It quickly moved into some aggressive short volatility positions to take advantage of the elevated levels in the VIX, before reversing and gong long as the index collapsed to the bottom of the monthly range.

A DOUBLE-DIGIT MONTHLY RETURN: +21.28%

The results were rather spectacular: a return of +21.28% for the month, bringing the total return to 38.25% for 2015 YTD.

In the current low rate environment, this rate of return is extraordinary, but not entirely unprecedented: the strategy has produced double-digit monthly returns several times in the past, most recently in August last year, which saw a return of +14.1%. Prior, to that, the record had been +8.90% in April 2013.

Such outsized returns come at a price – they have the effect of increasing strategy volatility and hence reducing the Sharpe Ratio. Of course, investors worry far less about upside volatility than downside volatility (or simi-variance), which is why the Sortino Ratio is in some ways a more appropriate measure of risk-adjusted performance, especially for strategies like ours which has very large kurtosis.

Since inception the compound annual growth rate (CAGR) of the strategy has been 45.60%, while the Sharpe Ratio has maintained a level of around 3 since that time.

Most of the drawdowns we have seen in the strategy have been in single digits, both in back-test and in live trading. The only exception was in 2013, where we experienced a very short term decline of -13.40%, from which the strategy recovered with a couple of days.

In the great majority of cases, drawdowns in VIX-related strategies result from bad end-of-day “marks” in the VIX index. These can arise for legitimate reasons, but are often

caused by traders manipulating the index, especially around option expiration. Because of the methodology used to compute the VIX, it is very easy to move the index by 5bp to 10bp, or more, by quoting prices for deep OTM put options as expiration nears. This can be critically important to holders of large VIX option positions and hence the temptation to engage in such manipulation may be irresistible.

For us, such market machinations are simply an annoyance, a cost of doing business in the VIX. Sure, they inflate drawdowns and strategy volatility, but there is not much we can do about them, other wait patiently for bad “marks” to be corrected the following day, which they almost always are.

Looking ahead over the remainder of the year, we are optimistic about the strategy’s opportunities to make money in August, but, like many traders, we are apprehensive about the consequences if the Fed should decide to take action to raise rates in September. We are likely to want to take in smaller size through the ensuing volatility, since either a long- or short-vol positions carries considerable risk in such a situation. As and when a rate rise does occur, we anticipate a market correction of perhaps 20% or more, accompanied by surge in market volatility. We are likely to see the VIX index reach the 20’s or 30’s, before it subsides. However, under this scenario, opportunities to make money on the short side will likely prove highly attractive going into the final quarter of the year. We remain hopeful of achieving a total return in the region of 40% to 50%, or more in 2015.

STRATEGY PERFORMANCE REPORT Jan 2012 – Jul 2015

December 11, 2018

The Case for Volatility as an Asset Class

Volatility as an asset class has grown up over the fifteen years since I started my first volatility arbitrage fund in 2000. Caissa Capital grew to about $400m in assets before I moved on, while several of its rivals have gone on to manage assets in the multiple billions of dollars. Back then volatility was seen as a niche, esoteric asset class and quite rightly so. Nonetheless, investors who braved the unknown and stayed the course have been well rewarded: in recent years volatility strategies as an asset class have handily outperformed the indices for global macro, equity market neutral and diversified funds of funds, for example.

The Fundamentals of Volatility

It’s worth rehearsing a few of the fundamental features of volatility for those unfamiliar with the territory.

Volatility is Unobservable

Volatility is the ultimate derivative, one whose fair price can never be known, even after the event, since it is intrinsically unobservable. You can estimate what the volatility of an asset has been over some historical period using, for example, the standard deviation of returns. But this is only an estimate, one of several possibilities, all of which have shortcomings. We now know that volatility can be measured with almost arbitrary precision using an integrated volatility estimator (essentially a metric based on high frequency data), but that does not change the essential fact: our knowledge of volatility is always subject to uncertainty, unlike a stock price, for example.

Volatility Trends

Huge effort is expended in identifying trends in commodity markets and many billions of dollars are invested in trend following CTA strategies (and, equivalently, momentum strategies in equities). Trend following undoubtedly works, according to academic research, but is also subject to prolonged drawdowns during periods when a trend moderates or reverses. By contrast, volatility always trends. You can see this from the charts below, which express the relationship between volatility in the S&P 500 index in consecutive months. The r-square of the regression relationship is one of the largest to be found in economics. And this is a feature of volatility not just in one asset class, such as equities, nor even for all classes of financial assets, but in every time series process for which data exists, including weather and other natural phenomena. So an investment strategy than seeks to exploit volatility trends is relying upon one of the most consistent features of any asset process we know of (more on this topic in Long Memory and Regime Shifts in Asset Volatility).

Volatility Mean-Reversion and Correlation

One of the central assumptions behind the ever-popular stat-arb strategies is that the basis between two or more correlated processes is stationary. Consequently, any departure from the long term relationship between such assets will eventually revert to the mean. Mean reversion is also an observed phenomenon in volatility processes. In fact, the speed of mean reversion (as estimated in, say, an Ornstein-Ulenbeck framework) is typically an order of magnitude larger than for a typical stock-pairs process. Furthermore, the correlation between one volatility process and another volatility process, or indeed between a volatility process and an asset returns process, tends to rise when markets are stressed (i.e. when volatility increases).

Another interesting feature of volatility correlations is that they are often lower than for the corresponding asset returns processes. One can therefore build a diversified volatility portfolio with far fewer assets that are required for, say, a basket of equities (see Modeling Asset Volatility for more on this topic).

Finally, more sophisticated stat-arb strategies tend to rely on cointegration rather than correlation, because cointegrated series are often driven by some common fundamental factors, rather than purely statistical ones, which may prove temporary (see Developing Statistical Arbitrage Strategies Using Cointegration for more details). Again, cointegrated relationships tend to be commonplace in the universe of volatility processes and are typically more reliable over the long term than those found in asset return processes.

Volatility Term Structure

One of the most marked characteristics of the typical asset volatility process its upward sloping term structure. An example of the typical term structure for futures on the VIX S&P 500 Index volatility index (as at the end of May, 2015), is shown in the chart below. A steeply upward-sloping curve characterizes the term structure of equity volatility around 75% of the time.

Fixed income investors can only dream of such yield in the current ZIRP environment, while f/x traders would have to plunge into the riskiest of currencies to achieve anything comparable in terms of yield differential and hope to be able to mitigate some of the devaluation risk by diversification.

The Volatility of Volatility

One feature of volatility processes that has been somewhat overlooked is the consistency of the volatility of volatility. Only on one occasion since 2007 has the VVIX index, which measures the annual volatility of the VIX index, ever fallen below 60.

What this means is that, in trading volatility, you are trading an asset whose annual volatility has hardly ever fallen below 60% and which has often exceeded 100% per year. Trading opportunities tend to abound when volatility is consistently elevated, as here (and, conversely, the performance of many hedge fund strategies tends to suffer during periods of sustained, low volatility)

Anything You Can Do, I Can Do better

The take-away from all this should be fairly obvious: almost any strategy you care to name has an equivalent in the volatility space, whether it be volatility long/short, relative value, stat-arb, trend following or carry trading. What is more, because of the inherent characteristics of volatility, all these strategies tend to produce higher levels of performance than their more traditional counterparts. Take as an example our own Volatility ETF strategy, which has produced consistent annual returns of between 30% and 40%, with a Sharpe ratio in excess of 3, since 2012.

(click to enlarge)

Where does the Alpha Come From?

It is traditional at this stage for managers to point the finger at hedgers as the source of abnormal returns and indeed I will do the same now. Equity portfolio managers are hardly ignorant of the cost of using options and volatility derivatives to hedge their portfolios; but neither are they likely to be leading experts in the pricing of such derivatives. And, after all, in a year in which they might be showing a 20% to 30% return, saving a few basis points on the hedge is neither here nor there, compared to the benefits of locking in the performance gains (and fees!). The same applies even when the purpose of using such derivatives is primarily to produce trading returns. Maple Leaf’s George Castrounis puts it this way:

Significant supply/demand imbalances continuously appear in derivative markets. The principal users of options (i.e. pension funds, corporates, mutual funds, insurance companies, retail and hedge funds) trade these instruments to express a view on the direction of the underlying asset rather than to express a view on the volatility of that asset, thus making non-economic volatility decisions. Their decision process may be driven by factors that have nothing to do with volatility levels, such as tax treatment, lockup, voting rights, or cross ownership. This creates opportunities for strategies that trade volatility.

We might also point to another source of potential alpha: the uncertainty as to what the current level of volatility is, and how it should be priced. As I have already pointed out, volatility is intrinsically uncertain, being unobservable. This allows for a disparity of views about its true level, both currently and in future. Secondly, there is no universal agreement on how volatility should be priced. This permits at times a wide divergence of views on fair value (to give you some idea of the complexities involved, I would refer you to, for example, Range based EGARCH Option pricing Models). What this means, of course, is that there is a basis for a genuine source of competitive advantage, such as the Caissa Capital fund enjoyed in the early 2000s with its advanced option pricing models. The plethora of volatility products that have emerged over the last decade has only added to the opportunity set.

Why Hasn’t It Been Done Before?

This was an entirely legitimate question back in the early days of volatility arbitrage. The cost of trading an option book, to say nothing of the complexities of managing the associated risks, were significant disincentives for both managers and investors. Bid/ask spreads were wide enough to cause significant heads winds for strategies that required aggressive price-taking. Mangers often had to juggle two sets of risks books, one reflecting the market’s view of the portfolio Greeks, the other the model view. The task of explaining all this to investors, many of whom had never evaluated volatility strategies previously, was a daunting one. And then there were the capacity issues: back in the early 2000s a $400m long/short option portfolio would typically have to run to several hundred names in order to meet liquidity and market impact risk tolerances. Much has changed over the last fifteen years, especially with the advent of the highly popular VIX futures contract and the newer ETF products such as VXX and XIV, whose trading volumes and AUM are growing rapidly. These developments have exerted strong downward pressure on trading costs, while providing sufficient capacity for at least a dozen volatility funds managing over $1Bn in assets.

Why Hasn’t It Been Done Right Yet?

Again, this question is less apposite than it was ten years ago and since that time there have been a number of success stories in the volatility space. One of the learning points occurred in 2004-2007, when volatility hit the lows for a 20 month period, causing performance to crater in long volatility funds, as well as funds with a volatility neutral mandate. I recall meeting with Nassim Taleb to discuss his Empirica volatility fund prior to that period, at the start of the 2000s. My advice to him was that, while he had some great ideas, they were better suited to an insurance product rather than a hedge fund. A long volatility fund might lose money month after month for an entire year, and with it investors and AUM, before seeing the kind of payoff that made such investment torture worthwhile. And so it proved.

Conversely, stories about managers of short volatility funds showing superb performance, only to blow up spectacularly when volatility eventually explodes, are legion in this field. One example comes to mind of a fund in Long Beach, CA, whose prime broker I visited with sometime in 2002. He told me the fund had been producing a rock-steady 30% annual return for several years, and the enthusiasm from investors was off the charts – the fund was managing north of $1Bn by then. Somewhat crestfallen I asked him how they were producing such spectacular returns. “They just sell puts in the S&P, 100 points out of the money”, he told me. I waited, expecting him to continue with details of how the fund managers handled the enormous tail risk. I waited in vain. They were selling naked put options. I can only imagine how those guys did when the VIX blew up in 2003 and, if they made it through that, what on earth happened to them in 2008!

Conclusion

The moral is simple: one cannot afford to be either all-long, or all-short volatility. The fund must run a long/short book, buying cheap Gamma and selling expensive Theta wherever possible, and changing the net volatility exposure of the portfolio dynamically, to suit current market conditions. It can certainly be done; and with the new volatility products that have emerged in recent years, the opportunities in the volatility space have never looked more promising.

September 20, 2018September 20, 2018

Day Trading System in VIX Futures – JonathanKinlay.com

This is a follow up to my earlier post on a Calendar Spread Strategy in VIX Futures (more information on calendar spreads ).

The strategy trades the front two months in the CFE VIX futures contract, generating an annual profit of around $25,000 per spread.

DAY TRADING SYSTEM
I built an equivalent day trading system in VIX futures in Trading Technologies visual ADL language, using 1-min bar data for 2010, and tested the system out-of-sample in 2011-2014. (for more information on X-Trader/ ADL go here).

The annual net PL is around $20,000 per spread, with a win rate of 67%. On the downside, the profit factor is rather low and the average trade is barely 1/10 of a tick). Note that this is net of Bid-Ask spread of 0.05 ($50) and commission/transaction costs of $20 per round turn. These cost assumptions are reasonable for online trading at many brokerage firms.

However, the strategy requires you to work the spread to enter passively (thereby reducing the cost of entry). This is usually only feasible on a platform suitable for a high frequency trading, where you can assume that your orders have acceptable priority in the limit order queue. This will result in a reasonable proportion of your passive bids and offers will be executed. Typically the spread trade is held throughout the session, exiting on close (since this is a day trading system).

Overall, while the trading system characteristics are reasonable, the spread strategy is better suited to longer (i.e. overnight) holding periods, since the VIX futures market is not the most liquid and the tick value is large. We’ll take a look at other day trading strategies in more liquid products, like the S&P 500 e-mini futures, for example, in another post.

(click to enlarge)