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.

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Magnitude and Evolution of ETF Trading Frictions

Are exchange-traded funds (ETF) efficient from a trading frictions perspective? In the October 2015 version of their paper entitled “ETF Liquidity”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti examine magnitude of trading frictions, best liquidity metric, time-variation in liquidity time and liquidity-return relationship across a large number of ETFs. Their universe consists of 870 ETFs: 571 equity (411 U.S. and 160 international),  83 bond, 17 commodity, 19 currency, 25 real estate, and 155 “other” that involve leveraged or short exposures to any asset classes. They employ the Dow Jones Industrial Average ETF (DIA) to relate ETF liquidity to underlying asset liquidity. They consider three trading friction metrics: (1) effective spread (twice the absolute difference between natural logarithms of price and bid-ask midpoint); (3) quoted spread (ask minus bid divided by midpoint); and, (3) price impact (five-minute changes in midpoint). Using tick and daily data for the selected ETFs and DIA components during 1996 through 2014, they find that: Keep Reading

Shorting VXX with Crash Protection

Does shorting the iPath S&P 500 VIX Short-Term Futures ETN (VXX) with crash protection (to capture the equity volatility risk premium safely) work? To investigate, we apply crash protection rules to three VXX shorting scenarios: (1) shorting an initial amount of VXX and letting this position ride indefinitely (Let It Ride); (2) shorting a fixed amount of VXX and continually resetting this fixed position (Fixed Reset); and, (3) shorting an initial amount of VXX and adjusting the size of the short position according to periodic gains/losses (Gain/Loss Adjusted). We consider a simple monthly crash protection rule and two alternatives proposed by subscribers as follows:

Prior-Month Positive Rule – short VXX (go to cash) when the prior-month return on a VXX short position is positive (negative).

20-50 SMA Rule – short VXX when its 20-day simple moving average (SMA) falls below its 50-day SMA and go to cash when VXX falls below both SMAs.

Prior-Week Positive Rule – short VXX (go to cash) when the prior week return on a VXX short position is positive (negative).

For tractability, we ignore trading frictions, costs of shorting and return on cash proceeds from shorting gains. Using monthly closes for the S&P 500 Volatility Index (VIX) and monthly, weekly and daily reverse split-adjusted closing prices for VXX from January 2009 through November 2015 (83 months), we find that: Keep Reading

Simple Volatility Harvesting?

Findings in “Add Stop-gain to Asset Class Momentum Strategy?” suggest that systematic capture of upside volatility may enhance the base 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. We also look at a corresponding stop-loss rule. Using monthly unadjusted highs, lows and closes (for stop-gain and stop-loss calculations) and dividend-adjusted closes (for return calculations) for SPY during February 1993 through November 2015 (275 months), we find that:

Keep Reading

Comparing Ivy 5 Allocation Strategy Variations

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

  1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
  2. 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).
  3. 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.
  4. 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)
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 October 2015 (117 months), we find that: Keep Reading

Combining Trend Following and Risk Parity across Asset Classes

Are trend following (intrinsic or time series momentum) and risk parity complementary multi-class portfolio construction approaches? In his October 2015 paper entitled “Trend-Following, Risk-Parity and the Influence of Correlations”, Nick Baltas compares performances of inverse volatility weighting and risk parity weighting as adapted to a long-short trend following strategy. Unlike volatility weighting, risk parity weighting incorporates asset return correlations, assigning higher (lower) weights to assets with lower (higher) average pairwise correlations with other assets. For both weighting schemes, portfolios are each month long (short) assets with positive (negative) past 12-month returns. Monthly inverse volatility weights derive from actual daily asset return volatilities over the past 90 trading days. Monthly risk parity weights derive from actual daily asset return volatilities and correlations over the past 90 trading days. Both weighting schemes target 10% portfolio volatility by each month applying overall leverage based on actual annualized volatility of an unleveraged trend following portfolio over the past 60 trading days divided by 10%. Using daily closing prices for the most liquid contract for each of 35 (6 energy, 10 commodity, 6 government bond, 6 currency exchange rate and 7 equity index) futures contract series as available during January 1987 through December 2013, he finds that: Keep Reading

When Low-beta Minus High-beta Works

Is there a way to predict when beta anomaly arbitrage (long low-beta stocks and short high-beta stocks will work? In the August 2015 version of their paper entitled “The Booms and Busts of Beta Arbitrage”, Shiyang Huang, Dong Lou and Christopher Polk investigate the power of a metric that measures beta arbitrage activity to predict associated returns. They measure beta via regressions of daily stock returns over the past 12 months versus daily market returns (contemporaneous and five lags to smooth unrelated effects). Each month, they rank stocks by beta and reform a hedge portfolio that is long (short) the value-weighted tenth, or decile, of stocks in the lowest (highest) betas. They measure beta arbitrage activity as the average pairwise correlation of weekly three-factor alphas (adjusting for market, size and book-to-market factors) of stocks in the lowest beta decile over the past 52 weeks. They then measure average hedge portfolio alphas for different ranges (ranked fifths, or quintiles) of beta arbitrage activity by month over long holding intervals. Using daily returns and other data for a broad sample of U.S. common stocks during 1970 through 2010, they find that: Keep Reading

Adaptive Higher Even Moment Currency Trading Strategy

Are higher even moments of asset return distributions useful predictors of future returns? In the September 2015 version of her paper entitled “A Low-Risk Strategy based on Higher Moments in Currency Markets”, Claudia Zunft explores an adaptive currency trading strategy that exploits the predictive power of higher even moments of forward currency exchange rate returns. The strategy is each month long (short) the equally weighted fifth, or quintile, of currencies with the lowest (highest) higher even return moments relative to recent past levels. For each currency, she first computes 13 even daily return moments over the last month (versus the U.S. dollar) ranging from 4 to 100 and then subtracts from these moments their respective average monthly values over lookback intervals of 12, 24, 36, 48 and 60 months and inception-to-date. From the resulting 78 combinations of moments and lookback intervals, she each month selects the combination with the highest average excess portfolio return over the last three months. For comparison, she also tests long-short quintile carry trade (high interest rate currencies minus low interest rate currencies) and momentum (high prior-month return currencies minus low prior month currencies) portfolios. Using bid, ask and mid-quote spot and forward contract (maturities up to a year) exchange rates versus the U.S. dollar for 20 of the most liquid developed and emerging market currencies as reliably available during December 1989 through October 2014, she finds that: Keep Reading

Secular Headwind for Risk Parity?

Is there a “trick” to good results for risk parity backtests? In their April 2014 brief research paper entitled “The Risks of Risk Parity”, the Brandes Institute examines the sustainability of a critical performance driver for the risk parity asset allocation approach. This approach weights asset classes such that their expected contributions to overall portfolio risk (volatility) are equal, generally by shifting conventional portfolio weights substantially from equities to bonds. Using hypothetical portfolio performance during 1994 through 2013 and bond yield data during 1871 through 2013, they find that: Keep Reading

Best Beta Anomaly Stock Strategy?

How can investors best exploit research showing that low-beta (high-beta) stocks tend to outperform (underperform)? In their August 2015 paper entitled “Low-Beta Investment Strategies”, Olaf Kornz and Laura-Chloe Kuntz test 32 “zero-cost” (long-short) strategies designed to exploit the beta anomaly as applied to S&P 500 stocks. The strategies derive from three choices: (1) length of the rolling window used to calculate stock and market index betas (one, three, six or 12 months of daily returns); (2) portfolio holding period (12 months or three months); and, (3) portfolio tilt method (four alternatives). The four portfolio tilt alternatives are:

  • Long Low-Beta/Short Index – long the equally weighted 50 stocks with the lowest betas, offset by a short position in the S&P 500 Index. 
  • Long-Short Low-High Beta Equal Weight – equal dollar values for a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, plus a small position in the market to offset any long-short beta mismatch.
  • Long-Short Low-High Beta Matched – a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, in different dollar amounts determined to produce a portfolio beta of one.
  • Short High-Beta/Long Index – short the equally weighted 50 stocks with the highest betas, offset by a long position in the S&P 500 Index.

Portfolio tilt alternatives also include positions in 1-month U.S. Treasury bill futures to satisfy an exact zero-cost assumption. The authors form portfolios monthly, such that there are 12 (four) overlapping portfolios with 12-month (3-month) holding periods. They also test the ability of the betas of aggregate high-beta stocks and aggregate low-beta stocks to predict market returns. Using daily returns for the S&P 500 index and its component stocks during September 1988 through October 2014, they find that: Keep Reading

Best Stock Momentum Strategy Crash Indicator?

What indicator works best to mitigate stock momentum strategy crashes? In his March 2015 paper entitled “Momentum Crash Management”, Mahdi Heidari compares performances of seven indicators for avoiding conventional stock momentum strategy crashes: (1) prior-month market return; (2) change in prior-month market return: (3) market volatility (standard deviation of 52 weekly returns); (4) dispersion (variance) of daily returns across all stocks; (5) market illiquidity (aggregate impact of trading on price); (6) momentum volatility (standard deviation of momentum strategy returns the past six months); and, (7) change in momentum volatility. The conventional strategy is each month long (short) the value-weighted tenth of stocks with the highest (lowest) returns from 12 months ago to one month ago. For each of the competing indicators, he invests in the conventional momentum strategy (cash) when the indicator is below (within) the top 10% of its values over the past five years. He uses portfolio turnover to compare implementation costs. Using data for a broad sample of relatively liquid U.S. stocks during January 1926 through December 2013, he finds that: Keep Reading

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