Recent Volatility in U.S. Equity Markets and Some
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Transcript Recent Volatility in U.S. Equity Markets and Some
By Toby White, CFA, FSA
Drake University
Finance / Actuarial Science
Iowa Actuarial Education Day
March 27, 2012
Outline
Introduction to Volatility: Motivations and Definitions
Volatility v. Return Relationships
Extreme Volatility in U.S. Equity Markets
Why has Volatility Increased in Recent Years?
Factors Affecting Volatility Levels
Using Derivatives to Manage Volatility Risk
Conclusion: Predicting Future Volatility
Intro: Why Care about Volatility?
Shifts in Volatility may make a diversified portfolio
‘less diversified.’
Arbitrageurs may get it wrong when volatility becomes
too high.
Abnormal event-related returns are strongly impacted
by volatility.
Both stock and option prices are associated with
changes in volatility.
Definitions of Volatility
Historical Volatility – based on the s.d. of continuously
compounded stock returns.
Idiosyncratic Volatility – based on the s.d. of residuals
from a factor model for returns.
Implied Volatility – the volatility level that would
produce an observed option price.
VIX (“fear index”) – measures the market’s volatility
expectation over the next 30 days.
Volatility v. Return Relationship
Stocks with large sensitivities to market volatility have
lower average returns.
Periods of high volatility tend to occur in bear markets,
and periods of low volatility occur in bull markets.
Return dispersion is countercyclical, but is related
positively to subsequent market volatility, and tends to
lead unemployment.
Explanation for Relationship
It is no surprise that high-risk stocks do relatively well
in ‘up’ markets, but relatively poorly in ‘down’ markets.
However, the negative effects from ‘down markets’
often dominate the positive effects from ‘up markets.’
This might indicate an inverse relationship between
risk (historical volatility) + return.
Can CAPM be salvaged?
CAPM states that there is direct relationship between
risk (beta) + return.
However, when investor sentiment (and volatility
levels) are high, speculative, high-risk stocks do worse
than bond-like stocks.
Empirical data supports a quadratic CAPM rather than
a linear model, where returns do rise with risk up to
some point, but then fall when volatility is excessive.
Extreme Volatility Events
Volatility Spikes tend to occur during times of low
or insufficient liquidity:
October 19, 1987 (portfolio insurance)
August (2nd half), 1998 (Russian financial crisis)
September 11, 2001 (WTC / markets closed for 4 days)
May 6, 2010 (Flash Crash)
Extreme Volatility Episodes
The Great Depression
The Internet Bubble
The Recent Financial Crisis
In 2008: the daily DJIA changes were at least 1% on
134/253 (53%) of all trading days
This compares to a 15.6% avg. (2004-2007)
European Debt Crisis / U.S. Treasury Downgrade (3rd
Quarter 2011)
Fatter Tails than Expected
Risk Modelers were unprepared for 2008, since
volatility had not been this high (and for so long) since
the mid-to-late 1930s.
Tail events can be caused by a currency crisis,
sovereign bond defaults, large-scale disasters, or other
hard-to-predict events.
Tails are fatter now than they were 15-20 years ago due
to increased systemic risk.
Discrete Jumps in Stock Prices
Discrete jumps often occur when reported earnings are
different than expectations.
Institutional investors now react quite swiftly to such
news, and in similar fashion.
Thus, stock price change distributions have higher
kurtosis/fatter tails (v. normal), especially among
lightly traded stocks.
Recently, the magnitude of price changes has exceeded
what fundamentals dictate.
Why has Volatility Increased?
Firm-Specific Factors:
Newly listed firms are younger, riskier, and need a less
proven track record (to be listed)
The number of stocks on U.S. exchanges has doubled
since 1980, but the average size of the newly listed firms
is smaller
Increased Volatility of Firm Fundamentals like EPS and
ROE (levels declining, variability up)
More Financial Leverage and Innovation
Why has Volatility Increased?
Macro-level Factors:
Increased Equity Weights among institutional investors,
who invest in block trades, and get information + form
opinions in similar circles
Increasing Prominence of NASDAQ market
Trend of Breaking up Conglomerates
Product Markets getting more competitive
More Incentives for Executives to assume risk and to
pursue higher growth rates
Other Factors Affecting Volatility
Volatility tends to be higher for small firms.
The variability of interest rates, bond yields and the
amplitude of the business cycle can affect stock and
option volatilities.
Behavioral effects (e.g. – ‘follow the herd’ mentality)
can impact volatility, as investors tend to overreact to
the arrival of new information.
Firms with high market-to-book ratios and firms with
high growth strategies tend to have higher firmspecific volatility levels.
Long v. Short Volatility Views
Long positions are like buying insurance (i.e. – buying
calls or puts) – they mostly lose money but can provide
huge payoffs.
Short positions are like selling insurance (i.e. – selling
calls or puts) – they mostly gain money but have
potentially high loss.
Between 2004-2007, a strong preference existed for
‘short volatility positions’, which contributed to pain in
the financial crisis.
Collared Stock
This position is created when a long stock holding is
supplemented with a long put and a short call with a
higher strike price.
Premium = S + P1 – C2 (where K2 > K1)
This manages volatility risk by locking in a certain
volatility level (i.e. – the maximum profit and
maximum loss is limited).
Straddle (Purchased / Written)
A purchased straddle consists of buying a call and
buying a put with the same strike.
Premium = C2 + P2
If one has a ‘long volatility’ view, buying a straddle can
exploit this – the more the stock moves in either
direction, the better.
If one has a ‘short volatility’ view, writing a straddle
can create premium revenues – the less the stock
moves, the better.
Strangle
Similar to a Straddle, except now, both the call and put
are out-of-the-money options, so as to reduce initial
premium outlay.
Premium = C3 + P1 (K1 < K2 < K3)
Compared to a straddle, profits will be lower (when
the stock price moves a lot), but the maximum loss
will also be lower (of stock prices do not move at all).
Butterfly Spread
This position is created when a written straddle is
supplemented with a purchased strangle, thus
reducing downside risk.
Premium = (– C2 – P2) + (C3 + P1)
This creates a situation where losses are small (but
limited) whenever stock prices move a lot, but gains
can still occur if stock prices remain close to K2.
Conclusion: Predicting Volatility
It is easier to predict future volatility (given past
volatility) than it is to predict future returns (given
past returns).
This is because there is considerable serial correlation
in volatility measures.
However, volatility levels tend to occur in episodes, so
that periods of high volatility are often followed by
periods of low volatility, and vice-versa.
In 2012, only 5/58 (8.6%) of all trading days so far have
seen the DJIA move by at least 1%, and 4 of the 5 days
were ‘up’ days. The Dow is now near its 50-mo. high.
Thank you
Iowa Actuaries Club
PricewaterhouseCoopers
Kelley Insurance Center
Drake University
Tom Root
Lingxiao Li
QUESTIONS?