# Volatility Effects

Reward goes with risk, and volatility represents risk. Therefore, volatility means reward; investors/traders get paid for riding roller coasters. Right? These blog entries relate to volatility effects.

**October 7, 2014** - Equity Options, Volatility Effects

S&P 500 Index options data imply expected S&P 500 Index volatility (VIX) over the next month. In turn, VIX futures options data imply expected volatility of VIX (VVIX) over the next month. Does VVIX predict stock index option and VIX option returns? In their September 2014 paper entitled “Volatility-of-Volatility Risk”, Darien Huang and Ivan Shaliastovich investigate whether VVIX represents a time-varying risk affecting: (1) S&P 500 Index option returns above and beyond the risk represented by VIX; and (2) VIX futures option returns. They measure risk effects via returns on S&P 500 Index options hedged daily by shorting the S&P 500 Index and VIX futures options hedged daily by shorting VIX futures. Using monthly S&P 500 Index returns, VIX futures returns, VIX, VVIX, S&P 500 Index option prices and VIX option prices during February 2006 through June 2013, *they find that:* Keep Reading

**October 3, 2014** - Strategic Allocation, Technical Trading, Volatility Effects

A subscriber requested comparison of four variations of an “Ivy 5″ asset class allocation strategy, as follows:

- Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
- Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
- Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
- Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

The subscriber proposed the following five asset class proxies for testing:

iShares 7-10 Year Treasury Bond (IEF)

SPDR S&P 500 (SPY)

SPDR Dow Jones REIT (RWR)

iShares MSCI EAFE Index (EFA)

PowerShares DB Commodity Index Tracking (DBC)

The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and the yield on 13-week U.S. Treasury bills (T-bills) as a proxy for return on cash during February 2006 through August 2014 (103 months), *we find that:* Keep Reading

**September 30, 2014** - Volatility Effects

Is the return on CBOE S&P 500 Volatility Index (VIX) futures predictable? In his preliminary paper entitled “The Expected Return of Fear”, Ing-Haw Cheng investigates whether the relationship between VIX futures prices and VIX level predicts the return on VIX futures. He focuses on monthly returns to a continuously-invested position in the nearest available VIX futures contract. He considers several different explanations for the behavior of VIX futures prices. Using VIX futures daily settlement prices during March 2004 through July 2014 (125 months), *he finds that:* Keep Reading

**September 24, 2014** - Volatility Effects

Are there times when investors should avoid low-volatility stocks? In their August 2014 paper entitled “Tactical Timing of Low Volatility Equity Strategies”, Sanne De Boer and James Norman investigate which factors predict the performance of low-volatility stocks relative to a capitalization-weighted index globally since 1980. They focus on two concerns: (1) will low-volatility stocks perform poorly when they are relatively expensive compared to the rest of the market; and, (2) will low-volatility stocks, which tend to pay high dividends, underperform when interest rates rise. Their low-volatility portfolio is a capitalization-weighted collection of country sectors processed quarterly in three steps designed to achieve a balance of low risk and sufficient diversification. They do not account for quarterly portfolio reformation frictions in return calculations. Using weekly data for all country sectors included in the MSCI Developed Markets Index during January 1975 through March 2014, *they find that:* Keep Reading

**August 22, 2014** - Animal Spirits, Volatility Effects

What is the best way to harvest asset mispricings derived from aggregate overreaction/underreaction by naive investors? In his July 2014 presentation package entitled “Betting On ‘Dumb Volatility’ with ‘Smart Beta'”, Claude Erb examines strategies for exploiting the “dumb volatility” arguably generated by naive investors who buy high and sell low, temporarily driving prices materially above and below fair values. These strategies generally involve periodically rebalancing portfolios to equal weights or some version of fair value weights (smart beta). Using monthly returns for a variety of indexes and funds during December 2004 through June 2014 (since the advent of smart beta research), *he finds that:* Keep Reading

**August 14, 2014** - Sentiment Indicators, Volatility Effects

Experts and pundits sometimes cite a high Chicago Board Options Exchange (CBOE) Volatility Index (VIX), the options-implied volatility of the S&P 500 Index, as contrarian indication of investor panic and therefore of pending U.S. stock market strength. Conversely, they cite a low VIX as indication of complacency and pending market weakness. However, a more nuanced conventional wisdom considers both very high VIX and very low VIX as favorable for future stock market returns. Does evidence support belief in either version of conventional wisdom? To check, we relate the level of VIX to S&P 500 Index returns over the next 5, 10, 21, 63 and 126 trading days. Using daily and monthly closes for VIX and for the S&P 500 Index over the period January 1990 through July 2014 (296 months), *we find that: Keep Reading *

**August 13, 2014** - Big Ideas, Mutual/Hedge Funds, Volatility Effects

Are widely used volatility-adjusted investment performance metrics, such as Sharpe ratio, robust to different measurement intervals? In the July 2014 version of their paper entitled “The Divergence of High- and Low-Frequency Estimation: Implications for Performance Measurement”, William Kinlaw, Mark Kritzman and David Turkington examine the sensitivity of such metrics to the length of the return interval used to measure it. They consider hedge fund performance, conventionally estimated as Sharpe ratio calculated from monthly returns and annualized by multiplying by the square root of 12. They also consider mutual fund performance, usually evaluated as excess return divided by excess volatility relative to an appropriate benchmark (information ratio). Finally, they consider Sharpe ratios of risk parity strategies, which periodically rebalance portfolio asset weights according to the inverse of their return standard deviations. Using monthly and longer-interval return data over available sample periods for each case, *they find that:* Keep Reading

**July 24, 2014** - Mutual/Hedge Funds, Volatility Effects

Are the sources of active mutual fund risk mostly common (systematic) or unique (idiosyncratic)? In his July 2014 paper entitled “Components of Portfolio Variance: R2, SelectionShare and TimingShare”, Anders Ekholm decomposes mutual fund return variance (risk) into three sources: (1) passive systematic factor exposure (R-squared); (2) active security selection or stock picking (SelectionShare); and, (3) active systematic factor timing (TimingShare). He demonstrates estimation of these three components based on mutual fund returns (reflecting daily manager actions) rather than holdings (known only via quarterly snapshots). He employs the widely used four-factor (market, size, book-to-market, momentum) model of stock returns to define systematic risk. Using daily returns for a broad sample of actively managed U.S. equity mutual funds and for the four factors during 2000 through 2013, *he finds that:* Keep Reading

**May 21, 2014** - Volatility Effects

Is implied volatility of implied volatility, interpretable as a measure of changes in investor fear level, a useful indicator of future stock market returns or VIX futures returns? To investigate, we examine relationships between the CBOE VVIX Index, a measure of the expected volatility of the 30-day forward level of the S&P 500 Implied Volatility Index (VIX) derived from prices of VIX options, and future returns for SPDR S&P 500 (SPY), iPath S&P 500 VIX Short-Term Futures (VXX) and VelocityShares Daily Inverse VIX Short-Term ETN (XIV). Using daily levels of VVIX and daily adjusted closes for SPY, VXX and XIV as available during January 2007 (VVIX inception) through April 2014, *we find that:* Keep Reading

**April 30, 2014** - Technical Trading, Volatility Effects

Findings in “Add Stop-gain to Asset Class Momentum Strategy?” suggest that systematic capture of upside volatility may enhance the strategy. Does this conclusion hold for a simpler application to a single liquid asset over a longer sample period? To investigate, we apply a stop-gain rule to SPDR S&P 500 (SPY) that: (1) exits SPY if its intra-month return exceeds a specified threshold; and, (2) re-enters SPY at the end of the month. Using monthly unadjusted monthly highs and closes (in stop-gain calculations) and dividend-adjusted closes (in return calculations) for SPY during February 1993 through March 2014 (254 months), *we find that:* Keep Reading