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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.

Suppressing Industry Momentum Strategy Crashes

Does adjusting leverage based on lagged strategy volatility protect an industry momentum strategy from crashes? In their September 2016 paper entitled “Risk-Managed Industry Momentum and Momentum Crashes”, Klaus Grobys, Joni Ruotsalainen and Janne Aijo investigate the profitability of risk-managed industry momentum strategies. Their asset universe consists of the 49 Fama-French value-weighted industry portfolios. They focus on a conventional momentum strategy that each month takes equally weighted long positions in past winners (top eight industries) and short positions in past losers (bottom eight industries) based on cumulative returns from 12 months ago to one month ago (12-2). They also analyze 6-2 and 12-7 variations to determine whether more recent or older past returns drive results. For risk management, they forecast next-month momentum strategy volatility based on past strategy volatility calculated based on daily returns over the past one, three or six months. They apply the volatility forecasts to determine the portfolio leverage required to target constant 12% annualized volatility. Using monthly and daily returns for the 49 industries during July 1926 through September 2014, they find that: Keep Reading

Exploitable VIX Trends?

Does identification of trends in the CBOE Volatility Index (VIX) via simple moving averages (SMA) support effective timing of the U.S. stock market or VIX futures exchange-traded notes (ETN)? to investigate we consider timing four asset pairs:

  1. SPDR S&P 500 (SPY)ProShares Short S&P500 (SH) since SH inception on 6/21/06.
  2. SPY – iShares 1-3 Year Treasury Bond (SHY) since 6/21/06.
  3. VelocityShares Daily Inverse VIX ST ETN (XIV)iPath S&P 500 VIX ST Futures ETN (VXX) since XIV inception on 11/30/10.
  4. XIV – SHY since 11/30/10.

SPY and XIV are offensive assets, and SHY and VXX are defensive assets. We consider five individual SMAs to determine VIX trend: 200-day (SMA200); 100-day (SMA100); 50-day (SMA50); 20-day (SMA20); and, 10-day (SMA10). We also consider one “majority rules” combination wherein at least three of the five individual SMAs agree (SMA-Multi). When daily VIX is above (below) its SMA, expected stock market volatility is trending up (down), and we hold the defensive (offensive) asset of the above pairs. We assume a baseline 0.1% for asset switching frictions. Using daily values of the above assets as specified through most of September 2016 (10.3 years for SPY pairs and 5.8 years for XIV pairs), we find that: Keep Reading

Change in VIX Futures Term Structure as Stock Market Return Predictor

Is the term structure of CBOE Volatility Index (VIX) futures useful for timing the underlying stock index? In the February 2012 version of his paper entitled “The Relationship between VIX Futures Term Structure and S&P500 Returns”, Athanasios Fassas relates the VIX futures term structure to both contemporaneous and future S&P500 Index returns. He measures the VIX futures term structure as the slope of a best-fit line for VIX (spot value) and closing prices for available VIX futures as a function of time to maturity. He rolls futures such that no contract in the calculation is within two weeks of maturity. He tests relationships between change in VIX futures term structure and S&P 500 Index return via regressions run at frequencies of one day, one week, two weeks, one month and two months, with the sample winnowed in each case so that measurements do not overlap. Using daily closing prices of spot VIX and the six nearest VIX futures with at least two weeks to maturity during late March 2004 through July 2010, he finds that: Keep Reading

Tail Risk as Stock Return Anomaly Driver

Do investors exploiting common stock return anomalies risk extraordinarily large drawdowns during market crashes? In their May 2016 paper entitled “Can Exposure to Aggregate Tail Risk Explain Size, Book-to-Market, and Idiosyncratic Volatility Anomalies?”, Sofiane Aboura and Eser Arisoy investigate whether portfolios based on the size, book-to-market ratio and idiosyncratic volatility effects bear elevated stock market tail risk. They measure market tail risk as change in VIX Tail Hedge Index (VXTH), which hedges extreme drops in the S&P 500 Index by holding the index and one-month far out-of-the-money (30-delta) call options on the CBOE Volatility Index (VIX). They test sensitivity of size and book-to-market factors to overall risk and tail risk by adding change in VIX (market volatility risk factor) and change in VXTH (market tail risk factor) to the Fama-French three-factor (market, size, book-to-market) model of stock returns. They consider two equal subperiods, one containing the 2008 financial crisis, to check robustness of findings. Using monthly values of VIX and VXTH, factor model returns and U.S. Treasury bill yields during January 2007 through February 2016 (110 months), they find that: Keep Reading

Stock Returns After Idiosyncratic Volatility Spikes

Should investors buy or sell stocks experiencing unique (idiosyncratic) volatility spikes? In their August 2016 paper entitled “Unusual News Flow and the Cross-Section of Stock Returns”, Turan Bali, Andriy Bodnaruk, Anna Scherbina and Yi Tang investigate relationships among sudden increases in stock idiosyncratic volatility, unusual firm news, changes in analyst earnings forecast dispersion, short selling and future returns. They identify idiosyncratic volatility shocks as large deviations from the volatility predicted out-of-sample by a regression model that accounts for market, size and book-to-market effects. They identify unusual news flow using Thomson-Reuters News Analytics data (covering 41 media) by comparing the number of stories about a firm in the current month to the average monthly coverage the prior four months, measured overall and separately for positive, negative and neutral stories. They measure changes in analyst earnings forecast dispersion (standard deviation divided by mean) based on data from I/B/E/S as the difference between current dispersion and dispersion two months ago. They measure data on shorting demand and utilization (shares borrowed divided by shares available for lending) using data from Markit. Using monthly values of the specified data from various inceptions through December 2012, they find that: Keep Reading

Low-volatility Effect Unexplained?

Does the Fama-French five-factor model of stock returns (employing market, size, book-to-market, investment and profitability factors) explain the outperformance of low-volatility stocks. In their July 2016 paper entitled “The Profitability of Low Volatility”, David Blitz and Milan Vidojevic examine whether: (1) any of several models expose a conventional return-for-risk market beta effect for stocks; and, (2) the low-volatility effect is distinct from a low-beta effect. They calculate volatilities for stocks and the market using daily or monthly returns over the past year. They calculate stock betas using these volatilities and daily or monthly stock-versus-market return correlations over the past five years, with shrinkage by 1/3 toward a value of one. They include momentum (return from 12 months ago to one month ago) as an explanatory factor, even though the five-factor model does not. Using data for a broad sample of U.S. common stocks and model factors (excluding extreme outliers) during July 1963 through December 2015, they find that: Keep Reading

Understanding Volatility Trading Strategies

What are the principal strategies for exploiting the volatility and volatility skew risk premiums? In his May 2016 workshop presentation package entitled “Volatility Modelling and Trading”, Artur Sepp provides an overview of systematic volatility risk premium capture strategies. He focuses on simple rule-based strategies with monthly reformation suitable for an investable index or a proprietary strategy. He covers delta-hedged strategies for capturing the volatility/volatility skew risk premiums (straddles/strangles) and buy-write and put-write options strategies as applied to major stock indexes and liquid exchange-traded funds (ETF). He covers the following strategy elements:

  1. Measuring realized volatility.
  2. Forecasting expected volatility.
  3. Measuring and forecasting implied and realized volatility skew.
  4. Computing option delta.
  5. Trading off transaction costs versus delta risk.
  6. Managing tail risk.

Using relevant data for target assets during January 2005 through January 2016, he finds that: Keep Reading

Correlation and Volatility Effects on Stock Pairs Trading

How does stock pairs trading performance interact with lagged pair correlation and volatility? In her May 2016 paper entitled “Demystifying Pairs Trading: The Role of Volatility and Correlation”, Stephanie Riedinger investigates how stock pair correlation and summed volatilities influence pair selection, pair return and portfolio return. Her baseline is a conventional pairs trading method that each month: (1) computes sums of daily squared normalized price differences (SSD) for all possible stock pairs over the last 12 months and selects the 20 pairs with the smallest SSDs; (2) over the next six months, buys (sells) the undervalued (overvalued) member of each of these pairs whenever renormalized prices diverge by more than two selection phase standard deviations; and, (3) closes positions when prices completely converge, prices diverge beyond four standard deviations, the trading phase ends or a traded stock is delisted. A pair may open and close several times during the trading period. At any time, six pairs portfolios trade simultaneously. She modifies this strategy to investigate correlation and volatility effects by: (1) measuring also during the selection phase return correlations and sum of volatilities based on daily closing prices for each possible stock pair; (2) allocating each pair to a correlation quintile (ranked fifth) and to a summed volatility quintile; and, (3) randomly selecting 20 twenty pairs out of each of the 25 intersections of correlation and summed volatility quintiles. She accounts for bid-ask frictions by executing all buys (sells) at the ask (bid) and by calculating daily returns at the bid. Using daily bid, ask and closing prices for all stocks included in the S&P 1500 during January 1990 (supporting initial pair trades in January 1991) through December 2014, she finds that: Keep Reading

Volatility Risk Premium an Exploitable Stock Market Predictor?

Does the U.S. stock market volatility risk premium (VRP), measured as the difference between the volatility implied by stock index option prices recent actual index volatility, usefully predict stock market returns? To investigate, we consider a simple VRP specification: S&P 500 Implied Volatility Index (VIX) minus standard deviation of daily S&P 500 Index returns over the past 21 trading days. Since VIX is an annualized percentage, we annualize actual daily volatility by multiplying by the square root of 252. We then relate this simple VRP to future S&P 500 Index returns and apply a VRP-related signal to time SPDR S&P 500 (SPY). Using daily data for the S&P 500 Index since December 1989, VIX since January 1990, and SPY and 13-week U.S. Treasury bills (T-bill) since the end of January 1993, all through April 2016, we find that: Keep Reading

Asset Class Momentum Interaction with Market Volatility

Subscribers have proposed that asset class momentum effects should accelerate (shorter optimal ranking interval) when markets are in turmoil (bear market/high volatility). “Asset Class Momentum Faster During Bear Markets?” addresses this hypothesis in a multi-class, relative momentum environment. Another approach is to evaluate the relationship between time series (intrinsic or absolute) momentum and volatility. Applied to the S&P 500 Index and the S&P 500 Implied Volatility Index (VIX), this alternative offers a longer sample period less dominated by the 2008-2009 equity market crash. Specifically, we examine monthly correlations between S&P 500 Index return over the past 1 to 12 months with next-month return to measure strength of time series momentum (positive correlations) or reversal (negative correlations). We compare correlations by ranked fifth (quintile) of VIX at the end of the past return measurement interval to determine (in-sample) optimal time series momentum measurement intervals for different ranges of VIX. We also test whether: (1) monthly change in VIX affects time series momentum for the S&P 500 Index; and, (2) VIX level affects time series momentum for another asset class (spot gold). Using monthly S&P 500 Index levels and spot gold prices since January 1989 and monthly VIX levels since inception in January 1990, all through April 2016, we find that: Keep Reading

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