Volatility Effects

How have different asset classes recently interacted with the CBOE Volatility Index (VIX)? To investigate, we look at lead-lag relationships between VIX and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

We look also at average next-month performances of these ETFs across ranges of of a VIX 3-month simple moving average (SMA3). Using end-of-month levels of VIX since January 1990 and dividend-adjusted monthly closing prices for the asset class proxies as available since July 2002, all through October 2024, we find that: Keep Reading

How can the low-volatility effect, whereby stocks with low past volatility tend to outperform the market on a risk-adjusted basis (but lag during long bull markets), help achieve common investment goals? In their October 2024 paper entitled “Leveraging the Low-Volatility Effect”, Lodewijk van der Linden, Amar Soebhag and Pim van Vliet test ways to use the low-volatility effect to support five distinct investment goals. Their low-volatility benchmark strategy each month holds the 100 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities. They consider ways to exploit the effect in five ways:

1. To safely boost return, they integrate value (net payout yield) and momentum (return from 12 months ago to one month ago) with low-volatility by each month: (1) selecting the 500 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities; and, (2) picking the 100 of these stocks with the highest combined net payout yield and momentum. 
2. To beat a conventional 60-40 stocks-bonds portfolio, they consider: (1) replacing 10% of stocks and 5% of bonds with a 15% allocation to Strategy 1; (2) assigning equal weights to stocks, bonds and Strategy 1; or, (3) allocating 70% to Strategy 1 and 30% to bonds.
3. To beat the stock market, they target a market beta of 1.00 via a 140% long position in Strategy 1, financed either by: (1) borrowing 40%, with credit spread plus the T-bill rate as the borrowing cost; or, (2) using equity market index futures, with annual return slippage and implicit costs 0.2%.
4. For absolute returns, they consider a 100% position in Strategy 1, offset by: (1) 48% short positions in speculative stocks (high volatility, low net payout yield and low momentum), assuming 2% annual shorting costs; or, (2) a 72% position in short equity market index futures, with 0.2% annual costs.
5. For crash protection compared to 5% out-of-the-money 1-month put options, they target a market beta of -0.50 by combining: (1) a 30% long position in the low-volatility benchmark with a 50% short position in speculative stocks, with credit spread over the T-bill rate as the borrowing cost; or, (2) a 70% long position in the low-volatility benchmark with a 100% short position in equity market index futures, with 0.2% annual costs.

In general, portfolio rebalancing is monthly. Using monthly data for the largest 1,000 U.S. stocks and for the other asset types specified above during 1990 through 2023, they find that: Keep Reading

Can investors reliably exploit the somewhat opaquely presented strategy summarized in “Using Wilder Volatility Stops to Time the U.S. Stock Market”, which employs Welles Wilder’s Average True Range (ATR) volatility metric to generate buy and sell signals for broad U.S. stock market indexes? To investigate, we each trading day for the SPDR S&P 500 ETF Trust (SPY):

1. Compute true range as the greatest of: (a) daily high minus low; (b) absolute value of daily high minus previous close; and, (c) absolute value of daily low minus previous close.
2. Calculate ATR as the simple average of the last five true ranges (including the current one).
3. Generate a Wilder Volatility Stop (WVS) by multiplying ATR by a risk factor of 2.5.
4. When out of SPY, buy when it closes above a dynamic trendline defined by a trend minimum plus current WVS (breakout). When in SPY, sell when it closes below a dynamic trendline defined by a trend maximum minus current WVS (breakdown).

We perform the above calculations using raw (not adjusted for dividends) daily SPY prices, but use dividend-adjusted prices to calculate returns. We assume any breakout/breakdown signal and associated SPY-cash switch occurs at the same close. We initially ignore SPY-cash switching frictions, but then test outcome sensitivity to different levels of frictions. We ignore return on cash due to frequency of switching. We further test outcome sensitivity to parameter choices and to an alternative definition of ATR. We use buy-and-hold SPY as a benchmark. Using daily raw and dividend-adjusted prices for SPY during January 1993 (inception) through most of October 2024, we find that: Keep Reading

Some experts interpret stock market return volatility as an indicator of investor sentiment, with high (low) volatility indicating ascendancy of fear (greed). Volatility of volatility (VoV) would thus indicate uncertainty in investor sentiment. Does the risk associated with this uncertainty depress stock prices and thereby predict strong stock market returns? To investigate, we consider two measures of U.S. stock market volatility: (1) realized volatility, calculated as standard deviation of daily S&P 500 Index returns over the last 21 trading days (annualized); and, (2) implied volatility as measured by the Chicago Board Options Exchange Market Volatility Index (VIX). For both, we calculate VoV as the standard deviation of volatility over the past 21 trading days and test the ability of VoV to predict SPDR S&P 500 (SPY) returns. To avoid overlap in volatility and VoV calculations, we focus on monthly return intervals. Using daily values of the S&P 500 Index since December 1989 and VIX since inception in January 1990, and monthly dividend-adjusted SPY closes since inception in January 1993, all through August 2024, we find that: Keep Reading

How do crypto-asset prices interact with conventional market risks, monetary policy and crypto-specific factors? In their July 2024 paper entitled “What Drives Crypto Asset Prices?”, Austin Adams, Markus Ibert and Gordon Liao investigate factors influencing crypto-asset returns using a sign-restricted, structural vector auto-regressive model. Specifically, they decompose daily Bitcoin returns into components reflecting:

• Monetary policy – estimated from effects of changes in the short-term risk-free rate on crypto-asset prices.
• Conventional risk premiums – estimated from daily interactions of 2-year zero coupon U.S. Treasury notes (T-notes) and the S&P 500 Index to account for changes in risk compensation required for holding traditional financial assets.
• Crypto risk premium – estimated from variations in the risk compensation demanded
  by investors for holding crypto assets as indicated by crypto-asset market liquidity and volatility.
• Level of crypto adoption – estimated from co-movements of Bitcoin and stablecoin market capitalizations to reflect crypto-asset innovation, regulatory changes and sentiment shifts.

Using daily data for the risk-free rate, S&P 500 Index, T-notes, Bitcoin and two stablecoins (USDT and USDC), during January 2019 through February 2024, they find that: Keep Reading

Does the CBOE Volatility Index (VIX) exhibit exploitable seasonality? To investigate, we calculate by calendar month and compare average monthly:

• Change in VIX.
• Return for ProShares VIX Short-Term Futures ETF (VIXY).
• Return for ProShares Short VIX Short-Term Futures ETF (SVXY).

Using monthly closes of VIX since January 1990, monthly split-adjusted closes for  for VIXY since inception in January 2011 and monthly split-adjusted closes for SVXY since inception in October 2011, all through June 2024, we find that: Keep Reading

“Use Short-term S&P 500 Index Indicators to Predict VIX Futures?” describes research finding a potentially exploitable relationship between S&P 500 Index short-term overbought/oversold conditions and short-term VIX futures gross returns. Do findings transfer to short-term VIX futures exchange-traded funds (ETF)? To investigate, we look at predictive relationships between daily SPDR S&P 500 ETF Trust (SPY) returns and daily returns for:

• ProShares VIX Short-Term Futures ETF (VIXY), which seeks daily investment results, before fees and expenses tracking the performance of the S&P 500 VIX Short-Term Futures Index.
• ProShares Short VIX Short-Term Futures ETF (SVXY), which seeks daily investment results, before fees and expenses tracking one-half the inverse (-0.5x) of the performance of the S&P 500 VIX Short-Term Futures Index.

Using daily dividend-adjusted values of SPY since January 2011, and daily split-adjusted values of VIXY since January 2011 and SVXY since October 2011, all through most of June 2024, we find that: Keep Reading

Does the S&P 500 Index (SPX) or the CBOE Volatility Index (VIX) yield better short-term trading signals for stocks and VIX futures? In the May 2024 revision of his paper entitled “Chicken and Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX?”, Robert Hanna explores mutual predictive relationships between SPX and VIX, with an eye toward exploitation via market timing strategies. He considers several long-term trend indicators to investigate whether SPX or VIX data offers better SPX return predictions. He considers two types of short-term overbought/oversold predictive rules: (1) short-term relative strength index (RSI) readings of 2, 3 and 4 days; and, (2) short-term high and low readings of 5 to 25 days in length. He applies both sets of short-term rules separately to SPX and VIX to predict movements of SPX and VIX futures. Using daily SPX and VIX levels since 1990 and short-term VIX futures prices since 2007, all through 2023, he finds that: Keep Reading

Does Federal Reserve (Fed) policy strongly and differently affect individual stock? In his April 2024 paper entitled “Navigating Federal Reserve Policy with IFED”, Rufus Rankin analyzes performance of the Invest With the Fed (IFED) stock selection strategy, which selects portfolios positioned to prosper across environments signaled by Fed actions. Specifically, the strategy selects individual equities based on 12 factors, adjusting weights of these factors based on Fed policy signals. The strategy rebalances with Fed policy changes or in June when there is no policy change for a year. He looks at two indexes representing different versions of the strategy:

1. IFED US-Large Cap Index (IFED-L), with the S&P 500 Index (S&P 500) as a benchmark.
2. IFED US Large-Cap Low Volatility Index (IFED-LV), with the S&P 500 Low Volatility Index (S&P 500 LV) as a benchmark.

Using monthly returns during April 2002 through September 2023, he finds that: Keep Reading

Can long volatility investors improve performance of their portfolios by scaling positions inversely to some measure of volatility? In his March 2024 paper entitled “Volatility-Managed Volatility Trading”, Aoxiang Yang tests volatility risk premium (VRP) timing strategies that hold a volatility asset and a risk-free asset, with the weight of the former inverse to some measure of volatility. He considers three volatility assets that are each month:

1. Long a 1-month variance swap contract, held to maturity (with prices sometimes approximated using VIX-squared).
2. Long a 1-month constant-maturity VIX futures portfolio (ignoring both a margin requirement and frictions required to maintain constant maturity).
3. Short a 1-month constant-maturity S&P 500 Index at-the-money (ATM) straddle (including a margin requirement of 100% of selling proceeds plus 20% of current S&P 500 Index level, but ignoring frictions required to maintain constant maturity).

Each month, he weights each asset by one of four measures related to stock market volatility:

1. Inverse of realized volatility.
2. Inverse of implied volatility (VIX).
3. Inverse of an autoregression forecast of next-month volatility.
4. Forecast of next-month VRP (which has an inverse VIX term) from a vector autoregression of realized volatility and VIX.

For each measure of volatility, he multiplies by a scaling constant that makes the respective long volatility portfolio have the same standard deviation of monthly returns as the S&P 500 Index. His benchmark portfolios hold the same assets with constant weights. He further analyzes performance of volatility portfolios during times of high volatility (highest 20%) and low volatility (lowest 80%). Using estimates for actual monthly prices for variance swaps during 1990-2023 (and actual prices for recent subperiods), for a constant-maturity VIX futures portfolio during 2004-2023 and for a constant-maturity S&P 500 Index ATM straddles portfolio during 1996-2022, he finds that: Keep Reading