Why can volatility be so high?

Table of Content
0. Introduction
0.1. Background
0.2. Research questions
1. Extra liquidity injected by central banks around the world
2. The ‘Liquidity Trap’ and its implications
3. Algorithms, high-frequency trading, quants
4. Leveraging
5. Global distribution of information
6. Game Theory Conflict
6.1. Bounded rationality
6.2. Fear as opposed to greed
7. Concluding Observations
8. Bibliographies


Table 1: Dow Jones Industrial Average Largest Intraday Point Falls since 1975
Source: Thomson Reuters; Financial Times, 2018
Rank When Fall at low % at low Closing Level % at closing
1 February 5, 2018 -1597.08 -6.26% 24345.75 -4.60%
2 August 24, 2015 -1089.42 -6.62% 15871.35 -3.57%
3 May 6, 2010 -998.5 -9.19 10520,32 -3.20%
4 October 6, 2008 -800.06 -7.75 9955.5 -3.58%
5 October 15, 2008 -780.87 -8.39% 8577.91 -7.87%

0. Introduction

Monday, February 5, 2018, saw one of the largest one-day drops in financial history. Above (Table1) shows the Dow Jones’ top five largest one-day falls in the history, according to Financial Times (Wells, 2018a). That day, the DJIA plummeted 1,597.08 points, a record intraday drop, although, in terms of percentages, it was not as catastrophic as the 2008 financial crisis or the ‘flash crashes’ on May 6, 2010, and August 24, 2015. To put in perspective, the S&P 500 was down 4.5% on February 5, 2018, whereas, on Black Monday, October 19, 1987, the S&P 500 fell by 20.47% in a day, although the S&P 500 actually ended that year being up (Valetkevitch, 2013).

Below shows the CBOE Volatility Index (VIX)in the period of 1985-2012, which presents a distinctive spike in 1987 and another more recent one on October 24, 2008 when the VIX reached an intraday high of 89.53, which was much lower 37.32 on February 5, 2018, up 103.99% from the previous close (MarketWatch, 2018).


Graph1: The CBOE Volatility Index (VIX)in the period of 1985-2012

VIX copy

The graph below was compiled by J.P. Morgan Asset Management, showing the daily volatility fluctuations of the Dow Jones and VIX in the orange line, which indicates the above-mentioned spikes until 2014.

Graph 2: Daily Volatility Fluctuations of DJIA till 2014
Source: J.P. Morgan Asset Management

Daily Volatility of DJIA

Below table also shows the S&P 500 largest intraday falls since March 2009 (Wells, 2018a). (Table 2)

Table 2: S&P 500 Largest Intraday falls (%) since Financial Crisis March 2009 low
Source: ThomsonReuters; Financial Times, 2018
Rank When Fall at low Close (points) % at closing
1 May 6, 2010 -8.59% 1,128.15 -3.24%
2 August 8, 2011 -6.68% 1,119.46 -6.66%
3 August 24, 2015 -5.27% 1,893.21 -3.94%
4 August 18, 2011 -5.27 682.55 -4.46%
5 March 5, 2009 -4.90% 1,200.07 -4.25%
6 August 4, 2011 -4.82% 1,120.07 -4.78%
7 August 10, 2011 -4.65% 1,120.76 -4.42%
8 September 22, 2011 -4.50% 1,129.56 -3.19%
9 February 5, 2018 -4.49% 2,648.8 -4.10%
10 March 30, 2009 -4.43% 787.53 -3.48%

Looking at these data, an arising question might be; why can volatility be so high in today’s somewhat sophisticated world of investment? What are the causes peculiar to today’s global financial market? There are supposedly a number of underlying reasons for high levels of volatility that have been witnessed in recent years, to which the following will attend.

1. Extra liquidity injected by central banks around the world

The first obvious reason is that the equity markets around the world are valued extremely high and trading volumes are thus higher than in the past. In recent years, the records of all-time highs have been frequently broken (Smith, 2018; Wells, 2018b, 2018c, 2018c; 2018Yuk, 2017).

Since 2008, central banks in major developed countries have embarked on the quantitative easing (QE), a set of monetary easing policies to stimulate the flow of money, which often entail central banks buying up government bonds circulating in the markets and thereby injecting extra liquidity (Fox, 2016; Hayes, 2017). The revival of the Keynesian interventionism was called for because in 2008, what amplified was the crunching of credit as interbank lending was frozen amongst fearful banks, and consequently, the loss in confidence in the banking system and the fear of not being able to recoup what they lend to each other deterred the circulation of money and credit.

As in a body where the flow of blood enables the body to function, economies around the globe need the constant flow of money and credit, yet today, national economies are so inter-connected that unexpected consequences of “sensitive dependence on initial conditions,” more colloquially known as butterfly effects, were observed in seemingly random places across the world in the wake of Lehman’s spectacular collapse.

To restore confidence amongst financial institutions to lend, borrow and thereby encourage the flow of money to trigger multiplier effects, extra liquidity had to be pumped in. As if melted snow flows into random streams of rivers, ramped up cash, however, flew into the equity markets, instead of spreading evenly across the society, in the pursuit of capturing apparent investment opportunities, and indeed, some investors have multiplied their investment during this period.

2. The ‘Liquidity Trap’ and its implications

Another reason why world stock markets have attracted so much liquidity is perhaps due to near-zero interest rates in many advanced economies, commonly known as a liquidity trap in the Keynesian economics. Under such circumstances where the interest rate cannot be lowered any further, “the quantity of money becomes irrelevant because money and bonds are essentially perfect substitutes.” (Krugman, 1998, p.137) It means that the government bond becomes a non-instrumental form of investment, and thus, investors tend to move their capital away from the bond market to somewhere more profitable. That has also contributed to the skyrocketed stock markets globally in recent years.

Meanwhile, when the Fed decides to raise its interest rate in the face of inflation, bond yields will go up. As the US Treasury yields go up, it represents the US’s strong economy, more capital will flow into the equity market up to the 5% interest rate. Indeed, now the Fed is likely to raise the interest rate twice more this year, as it seems, albeit depending on the trade war situation, therefore more capital will flow into the equity market. On the other hands, over 5% of the interest rate, the US Treasury becomes a demonstrably higher-yielding form of investment, capital will then flow back into the bond market, as the bond market is essentially more secure than the equity market (Fleming and Wigglesworth, 2018). The historical correlations between interest rate movements and weekly stock returns between May 1963 and June 2014 have been computed by JP Morgan Asset Management (Kelly, 2014), which highlight that stock returns and interest rate movements above 5% are negatively correlated, whereas, below 5%, they are positively correlated.

Graph 3: Correlations between Interest Rates and Equities
Source: J.P. Morgan Asset Management

Correlations between Interest Rates and Equities


3. Algorithms, high-frequency trading, quants

The widespread adoption of algorithms and stop loss functions are probably other catalysts for the recent unpredictable volatility. Algorithms that are written into the automated systems used by many major investment institutions pick up on certain news and automatically start selling or buying to mitigate exposure to particular political and geopolitical events and eventualities. And yet, what ends up happening is that a large number of major institutions make the same moves simultaneously, and consequently, their collective actions make the market swing so wildly as it has. Mass movements in the markets due to the wide usage of algorithms and quants are thus recent phenomena that set off volatility to soar.

For many day traders and short-term investors, volatility represents opportunities. Like surfers, only big waves allow them to capture opportunities for high returns. It is because volatility symbolises mass-migration of people’s beliefs, and in such instances, the markets would likely become mispriced. It is a sign of the herd mentality that is highly contagious, in particular, when people are driven by emotional and instinctual urges that cloud their rationality (Kahneman and Tversky, 1979; 1992).

And the widespread usage of algorithms and the automation systems makes the market movements far more complex than it seems, because if algorithms pick up on a high volatility level and bet against the fearful mass, in order to capture an opportunity, there will be someone else that would try to bet against that decision, and this cat and mouse chain reactions may perpetuate.

Meanwhile, the computer-generated simulation of a simple double-rod pendulum, for example, which pertains to chaos theory, illustrates that when more than a few variables come into play, the possible outcomes and scenarios become almost utterly unpredictable (Boeing, 2016). This seems to explain the occurrences of “flash crash” that have been observed occasionally in recent years.

As investors search for individual opportunities over indexing strategies, in theory, correlations between stocks should remain low, which means that investors are making decisions based on independent thinking. Hence, past records show that during the times of crisis when the volatility level goes up, correlations amongst stocks had also increased, showing the emergence of the herd mentality amongst investors, according to the analysis of JP Morgan Asset Management (Kelly, 2014).

Graph 4: Equity Correlations and Volatility
Source: J.P. Morgan Asset Management

Equity Correlations and Volatility 


4. Leveraging

Leveraging is another aspect that makes the fluctuations of the market so wildly as it has been, as trading volumes are amplified by leveraged bets, thus flash crashes occur more frequently today than in the past.


5. Global distribution of information

Today, for better or worse, the interconnectedness of the global financial markets allows one to source information that used to be only accessible by a handful of investors. This transparency is a wonderful thing on one hand, but on the other, the major 24-hour news cycles may make people’s decision-making homogenised irrespective of their geographic locations. Sourcing information from the same or similar set of routes inevitably homogenises ideas derived from it, which makes it difficult to generate independent thinking.

6. Game Theory Conflict

As implied above, aspects of the game theory that have been built into the algorithms are also contributing to the sudden volatility surges. To predict the moves of others are always difficult as they also strive to do so, and yet, to be successful in the markets, one has to be a contrarian and an independent thinker. However, if a large number of investors try to become contrarians, their decisions may end up becoming rather homogenised. As major institutions often identify the same or similar key events as the points of reference to make major investment decisions, their supposedly independent decisions may inadvertently coincide with one another.

6.1. Bounded rationality

This can be attributed to the shortcomings of rational thinking caused by what Simon (1957) calls “bounded rationality.” Rationality has its boundary or limitations so much so that even a certain logic, which is seemingly impregnable, sometimes differs from the reality. The discovery of the quantum theory by Einstein, for example, highlighted the shortcomings of rationality in that it altered the general understanding of the universe, radically departing from what was previously understood, even though the previous understanding seemed also rationally sound.

Moreover, in the light of emotional exuberance and the herd mentality that occasionally emerge amongst the mass, people are not always the best decision makers (Thaler, 2015c; Schiller, 2000). In fact, people make terrible decisions at times. To illustrate a quintessential scenario of making the least optimal decisions, the prisoner’s dilemma game is often quoted, in which the choice of defection, as opposed to cooperation between the two prisoners, is always stable, even though such decisions lead to the least optimal outcome for both (Axelrod and Hamilton, 1981; Axelrod, 1984; Foster and Young, 1991).

6.2. Fear as opposed to greed

Also, today, many investors are not necessarily driven by greed, but in fact, the fear of losing their money, craving for a sense of security. Their highly risk-averse psychology makes them hypersensitive about current legislative, economic and political affairs and news, such as Trump’s incessant Tweets, trade wars, Brexit, and isolationist trends. The idea that the fear of losing is greater than greed has been proven by the Nobel laureate Kahneman and Tversky (1979; 1992), as the psychologists discovered that people are more sensitive to losses than gains.

Whichever the main driver of the market might be, when emotions drive the decision-making process, investors are driven by the forces unsuitable for making decisions to reach optimal outcomes, and the irony is that such fear-ridden decisions are sometimes a part of their risk management. Fear is an emotion that often makes our rational mind astray. Hence, despite all the technological advances and having recourse to algorithms, still many investors’ decisions at times coincide, even though they are supposedly making independent decisions.


7. Concluding Observation

There are various underlying causes as to the high volatility that has been observed in recent years. At the World Economic Forum in Davos, Switzerland this year, the representatives of central banks in the major economies discussed the possible end of the extensive monetary easing policies that have prevailed globally in recent years (Wolf, 2018). If such decisions are brought about, either by means of ending QE or interest rate hikes or the combination of both, it yet remains unpredictable as to whether volatility will flare up or central banks will manage to contain possible disruptions. As observed earlier, an interest rate of up to 5%, an interest rate rise is positively correlated with equity returns, suggesting that that is the extent to which central banks may be able to manoeuvre their policies. Meanwhile, politics will probably continue to have a strong influence on economies on both side of the Atlantic, as well as in Asia, therefore it is likely that a sudden surge in volatility driven by fear will be observed again, perhaps sooner than we hope.


8. Bibliographies

Axelrod, Robert., and Hamilton, D.William. (1981, March 27). The Evolution of Cooperation. Science, New Series, Vol. 211, No.4489. pp.1390-1396.
Axelrod, Robert. (1984). The Evolution of Cooperation. New York: Basic Books.
Boeing, Geoff. (2016). Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction. Systems. 4(4):37; doi:10.3390/systems4040037
Fleming, Sam., and Wigglesworth, Robin. (2018, June 13). Fed lifts rates and projects four rises for 2018. Financial Times. [Online] Available at: https://www.ft.com/content/19fe9bc8-6f1b-11e8-852d-d8b934ff5ffa [Accessed on June 14 2018]
Foster, Dean., and Young, H. Peyton. (1991). Cooperation in the Short and in the Long Run. Games and Economic Behaviour3, 145-156.
Fox, Jeff. (2016, February 12). $12.3 trillion of QE has added up to…this? CNBC. [Online] Available at: https://www.cnbc.com/2016/02/12/123-trillion-of-qe-has-added-up-tothis.html [Accessed on 16 July 2018]
Hayes, Adam. (2017, March 24). Why Didn’t Quantitative Easing Lead to Hyperinflation? Investopedia. [Online] Available at: https://www.investopedia.com/articles/investing/022615/why-didnt-quantitative-easing-lead-hyperinflation.asp [Accessed on 16 July 2018]
Kahneman, Daniel., and Tversky, Amos. (1979, March). Prospect Theory: An Analysis of Decision under Risk. Econometrica, Vol.47, No.2, pp.263-292.DOI: 10.2307/1914185
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Krugman, R, Paul. (1998). It’s Baaack: Japan’s Slump and the Return of the Liquidity Trap. In: Brookings Papers on Economic Activity, 2: 1998. pp.137-205.
Lorenz, Edward. (1972, December 29). Predictability: Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas? presented before the American Association for the Advancement of Science.
MarketWatch. (2018). CBOE Volatility Index. [Online] Available at: https://www.marketwatch.com/investing/index/vix
Schiller, J. Robert. (2000).Irrational Exuberance. Princeton, New Jersey: Princeton University Press.
Simon, Hubert. (1957). Models of Man. New York: Wiley.
Smith, Stephen. (2018, January 5). Global stocks celebrate 2018 with record highs. Financial Times. [Online] Available at: https://www.ft.com/content/58ad5092-f141-11e7-b220-857e26d1aca4 [Accessed on January 6 2018]
Thaler, Richard. H., Sunstein, C.R. (2008a). Nudge: improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Thaler, H. Richard. (2015b). Misbehaving. The making of behavioral economics. New York: W.W. Norton & Company.
Thaler, H.Richard. (2015c) Unless You Are Spock, Irrelevant Things Matter in Economic Behavior. The New York Times. [Online] Available at:www.nytimes.com/2015/05/10/upshot/unless-you-are-spock-irrelevant-things-matter-in-economic-behavior.html[Accessed on 16 July 2018]
Lecture by Thaler, R. H. (2016d) Richard Thaler on Behavioral Economics:Past, Present and Future, Dietrich College of Humanities and Social Sciences. Available at: http://www.youtube.com/watch?v=TJrpN5INvcs
The Royal Swedish Academy of Science. (2017, October 9). Richard H. Thaler: Integrating Economics with Psychology.
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Tversky, Amos., and Kahneman, Daniel. (1992). Advances in Prospect Theory Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, Vol.5, No.4, pp.297-323.
Valetkevitch, Caroline. (2013). Key dates and milestones in the S&P 500’s history. Thomson Reuters. [Online] Available at:https://www.reuters.com/article/us-usa-stocks-sp-timeline-idUSBRE9450WL20130506%5BAccessed on July 18 2018]
Wells, Peter. (2018a, February 5). Wall Street’s intraday drops among largest in years. Financial Times. [Online] Available at: https://www.ft.com/content/4f2196ce-0abd-11e8-8eb7-42f857ea9f09 [Accessed on February 06 2018]
Wells, Peter. (2018b, March 9). Recovery complete: Nasdaq hits record high. Financial Times. [Online] Available at: https://www.ft.com/content/532e502c-23b8-11e8-ae48-60d3531b7d11 [Accessed on March 10 2018]
Wells, Peter. (2018c, July 12). Tech stocks propel Nasdaq to record high. Financial Times. [Online] Available at: https://www.ft.com/content/eca48f26-860c-11e8-a29d-73e3d454535d [Accessed on July 16 2018]
Wells, Peter. (2018d, July 12). Nasdaq notches new record high despite ongoing trade jitters. Financial Times. [Online] Available at: https://www.ft.com/content/cf5e0776-85ee-11e8-a29d-73e3d454535d [Accessed on July 16 2018]
Wolf, Martin. (2018, January 21). Davos 2018: the monetary policy trick is managing a return to ‘normal.’ Financial Times. [Online] Available at: https://www.ft.com/content/0aa08fee-dc07-11e7-9504-59efdb70e12f [Accessed on 19 July 2018]
Yuk, Pan Kwan. (2017, November 29). S&P500, Dow open at new record highs. Financial Times. [Online] available at: https://www.ft.com/content/b3740477-43ed-3019-a4ed-4ed0b3bc4152 [Accessed on November 29 2017]

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