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?