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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.

Using SVXY to Capture the Volatility Risk Premium

In response to “Shorting VXX with Crash Protection”, which investigates shorting iPath S&P 500 VIX Short-Term Futures (VXX) to capture the equity volatility risk premium, a subscriber asked about instead using a long position in ProShares Short VIX Short-Term Futures (SVXY). To investigate, we consider two scenarios based on monthly measurements:

  1. Buy and Hold – buying an initial amount of SVXY and letting this position ride indefinitely.
  2. Monthly Skim – buying the same initial amount of SVXY and transferring to cash any month-end gains exceeding the initial investment (the beginning-of-month SVXY position may become smaller, but not larger, than the initial investment).

The offeror changed the SVXY investment objective at the end of February 2018 (when short VIX strategies crashed), targeting henceforth -0.5 times the daily performance of the S&P 500 VIX Short-Term Futures Index rather than -1.0 times as before. We therefore examine SVXY performance separately before and after that change. We assume switching frictions of 0.25% for movements of funds from SVXY to cash in scenario 2. We assume return on cash is the 3-month U.S. Treasury bill (T-bill) yield. Using monthly split-adjusted closing prices for SVXY and contemporaneous T-bill yield during October 2011 through June 2019, we find that: Keep Reading

Factor Premium Reliability and Timing

How reliable and variable are the most widely accepted long-short factor premiums across asset classes? Can investors time factor premium? In their June 2019 paper entitled “Factor Premia and Factor Timing: A Century of Evidence”, Antti Ilmanen, Ronen Israel, Tobias Moskowitz, Ashwin Thapar and Franklin Wang examine multi-class robustness of and variation in four prominent factor premiums:

  1. Value – book-to-market ratio for individual stocks; value-weighted aggregate cyclically-adjusted price-to-earnings ratio (P/E10) for stock indexes; 10-year real yield for bonds; deviation from purchasing power parity for currencies; and, negative 5-year change in spot price for commodities.
  2. Momentum – past excess (relative to cash) return from 13 months ago to one month ago.
  3. Carry – front-month futures-to-spot ratio for equity indexes since 1990 and excess dividend yield before 1990; difference in short-term interest rates for currencies; 10-year minus 3-month yields for bonds; and, percentage difference in prices between the nearest and next-nearest contracts for commodities.
  4. Defensive – for equity indexes and bonds, betas from 36-month rolling regressions of asset returns versus equal-weighted returns of all countries; and, no defensive strategies for currencies and commodities because market returns are difficult to define.

They each month rank each asset (with a 1-month lag for conservative execution) on each factor and form a portfolio that is long (short) assets with the highest (lowest) expected returns, weighted according to zero-sum rank. When combining factor portfolios across factors or asset classes, they weight them by inverse portfolio standard deviation of returns over the past 36 months. To assess both overfitting and market adaptation, they split each factor sample into pre-discovery subperiod, original discovery subperiod and post-publication subperiod. They consider factor premium interactions with economic variables (business cycles, growth and interest rates), political risk, volatility, downside risk, tail risk, crashes, market liquidity and investment sentiment. Finally, they test factor timing strategies based on 12 timing signals based on 19 methodologies across six asset classes and four factors. Using data as available from as far back as February 1877 for 43 country equity indexes, 26 government bonds, 44 exchange rates and 40 commodities, all through 2017, they find that: Keep Reading

Are Low Volatility Stock ETFs Working?

Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through June 2018, we find that: Keep Reading

Test of Seasonal Risk Adjustment Strategy

A subscriber requested review of a strategy that seeks to exploit “Sell in May” by switching between risk-on assets during November-April and risk-off assets during May-October, with assets specified as follows:

On each portfolio switch date, assets receive equal weight with 0.25% overall penalty for trading frictions. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) measured at 6-month intervals and Sharpe ratio measured at 6-month intervals as key performance statistics. As benchmarks, we consider buying and holding SPY, IWM or TLT and a 60%-40% SPY-TLT portfolio rebalanced frictionlessly at the ends of April and October (60-40). Using April and October dividend-adjusted closes of SPY, IWM, PDP, TLT and SPLV as available during October 2002 (first interval with at least one risk-on and one risk-off asset) through April 2019, and contemporaneous 6-month U.S. Treasury bill (T-bill) yield as the risk-free rate, we find that: Keep Reading

Does Volatility Management Work for Equity Factor Portfolios?

Do equity strategy portfolios characterized by aggressive (conservative) scaling when portfolio volatility is recently low (high) reliably beat unmanaged performance? In their March 2019 paper entitled “On the Performance of Volatility-Managed Portfolios”, Scott Cederburg, Michael O’Doherty, Feifei Wang and Xuemin Yan assess whether practical volatility management is systematically attractive. For each of 103 anomalies (nine widely used factors and 94 other published anomalies), they construct a hedge portfolio that is each month long (short) the value-weighted tenth of stocks with the highest (lowest) expected returns. They then construct volatility-managed versions of these portfolios based on inverse variance of daily portfolio returns the prior month. Focusing on gross Sharpe ratio, they compare head-to-head performances of volatility-managed portfolios and unmanaged counterparts. Focusing on gross Sharpe ratio and certainty equivalent return (CER), they also employ an historical training subsample to estimate mean-variance optimal allocations for: (1) a strategy that chooses among a given volatility-managed portfolio, its unmanaged counterpart and a risk-free asset; and, (2) a strategy chooses between only the unmanaged counterpart and the risk-free asset. Using daily returns for the 103 equity hedge portfolios, they 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.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond (IEF)
SPDR S&P 500 (SPY)
Vanguard REIT ETF (VNQ)
iShares MSCI EAFE Index (EFA)
PowerShares DB Commodity Index Tracking (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). 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 U.S. Treasury bills (T-bills) as the return on cash during February 2006 through March 2019, we find that: Keep Reading

Inflated Expectations of Factor Investing

How should investors feel about factor/multi-factor investing? In their February 2019 paper entitled “Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing”, Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa explore three critical failures of U.S. equity factor investing:

  1. Returns are far short of expectations due to overfitting and/or trade crowding.
  2. Drawdowns far exceed expectations.
  3. Diversification of factors occasionally disappears when correlations soar.

They focus on 15 factors most closely followed by investors: the market factor; a set of six factors from widely used academic multi-factor models (size, value, operating profitability, investment, momentum and low beta); and, a set of eight other popular factors (idiosyncratic volatility, short-term reversal, illiquidity, accruals, cash flow-to-price, earnings-to-price, long-term reversal and net share issuance). For some analyses they employ a broader set of 46 factors. They consider both long-term (July 1963-June 2018) and short-term (July 2003-June 2018) factor performances. Using returns for the specified factors during July 1963 through June 2018, they conclude that:

Keep Reading

Country Stock Market Anomaly Momentum

Do country stock market anomalies have trends? In his March 2018 paper entitled “The Momentum Effect in Country-Level Stock Market Anomalies”, Adam Zaremba investigates whether country-level stock market return anomalies exhibit trends (momentum) based on their past returns. Specifically, he:

  • Screens potential anomalies via monthly reformed hedge portfolios that long (short) the equal-weighted or capitalization-weighted fifth of country stock market indexes with the highest (lowest) expected gross returns based on one of 40 market-level characteristics/combinations of characteristics. Characteristics span aggregate market value, momentum, reversal, skewness, quality, volatility, liquidity, net stock issuance and seasonality metrics.
  • Tests whether the most reliable anomalies exhibit trends (momentum) based on their respective returns over the past 3, 6, 9 or 12 months.
  • Compares performance of a portfolio that is long the third of reliable anomalies with the highest past returns to that of a portfolio that is long the equal-weighted combination of all reliable anomalies.

He performs all calculations twice, accounting in a second iteration for effects of taxes on dividends across countries. Using returns for capitalization-weighted country stock market indexes and data required for the 40 anomaly hedge portfolios as available across 78 country markets during January 1995 through May 2015, he finds that: Keep Reading

Global Factor Premiums Over the Very Long Run

Do very old data confirm reliability of widely accepted asset return factor premiums? In their January 2019 paper entitled “Global Factor Premiums”, Guido Baltussen, Laurens Swinkels and Pim van Vliet present replication (1981-2011) and out-of-sample (1800-1908 and 2012-2016) tests of six global factor premiums across four asset classes. The asset classes are equity indexes, government bonds, commodities and currencies. The factors are: time series (intrinsic or absolute) momentum, designated as trend; cross-sectional (relative) momentum, designated as momentum; value; carry (long high yields and short low yields); seasonality (rolling “hot” months); and, betting against beta (BAB). They explicitly account for p-hacking (data snooping bias) and further explore economic explanations of global factor premiums. Using monthly global data as available during 1800 through 2016 to construct the six factors and four asset class return series, they find that:

Keep Reading

Rebalance Timing Noise

Does choice of multi-asset portfolio rebalance date(s) materially affect performance? In their October 2018 paper entitled “Rebalance Timing Luck: The Difference Between Hired and Fired”, Corey Hoffstein, Justin Sibears and Nathan Faber investigate effects of varying portfolio rebalance date on performance. Specifically, they quantify noise (luck) from varying annual rebalance date for a 60% S&P 500 Index-40% 5-year constant maturity U.S. Treasury note (60-40) U.S. market portfolio. Using monthly total returns for these two assets during January 1922 through June 2018, they find that: Keep Reading

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