Volatility Effects

What is the best way to measure and interpret market reaction to new information? In their October 2010 paper entitled “Measuring Flow Toxicity in a High Frequency World”, David Easley, Marcos López de Prado and Maureen O’Hara introduce a new method to estimate the degree to which trading in financial markets is informed. They name this metric Volume-Synchronized Probability of Informed Trading (VPIN), approximated by the fraction of trading volume that is imbalanced (absolute difference between seller-initiated and buyer-initiated volumes, divided by total volume).  Their approach builds on three beliefs: (1) new orders indicate arrival of new information potentially predictive of subsequent price moves; (2) a specific volume of trades therefore represents a more consistent metric for information arrival than an interval of time; and, (3) a trade imbalance is the hallmark of arrival of important information. In a related November 2010 paper entitled “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading”, these same authors focus this method on the May 6, 2010 market crash. Using high-frequency (one-minute intervals) price and volume data for a variety of futures contracts during January 2008 through August 2010 to construct rolling sets of equal-volume increments, they find that: Keep Reading

Does the relatively illiquid, opaque, retail environment of over-the-counter (OTC) stocks make them behave differently from comparable listed stocks? In their November 2010 paper entitled “The Cross Section of Over-the-Counter Equities”, Andrew Ang, Assaf Shtauber and Paul Tetlock test the abilities of market capitalization, book-to-market ratio, liquidity, return momentum and idiosyncratic volatility to predict OTC stock returns and compare results to those for listed stocks with comparable market capitalizations. As a part of the study, they examine hedge portfolios that are long/short extreme fifths of OTC stocks ranked by these characteristics to estimate of the magnitudes of the respective  premiums. Using trading volumes, market capitalizations, book-to-market ratios (as available) and closing, bid and ask prices for a large sample of OTC-only firms with at least one Financial Industry Regulatory Authority market maker, and for comparable listed firms, during 1975 through 2008, they find that: Keep Reading

Author Jeff Augen describes his 2010 book Trading Realities: The Truth, the Lies, and the Hype In-Between as “designed to help investors understand the economic and political forces that drive financial markets and to invest alongside those forces instead of against them. It also provides a blunt assessment of the limitations that most private investors face. Understanding these limitations and being able to manage risk are as important as choosing the right investments.” Some notable points from the book are: Keep Reading

Is aggregate equity investor fear multifaceted? In the August 2010 version of his paper entitled “The Equity Fear Premium and Daily Comovements of the S&P 500 E/P ratio and Treasury Yields before and during the 2007 Financial Crisis”, Christophe Faugère introduces a Total Fear Premium derived from independent flight-to-safety and flight-to-liquidity impulses. The flight-to-safety component assumes investors fleeing stocks buy long-term Treasuries, driving the real, after-tax yield on Treasury bonds below long-term trend (growth rate of real Gross Domestic Product per capita). The flight-to-liquidity component assumes investors fleeing stocks buy short-term instruments (such as Treasury bills), driving their yields below the federal funds target rate. Using daily levels of the S&P 500 forward earnings yield, Treasury yields and S&P 500 Index implied volatility (VIX) over the period July 2004 through February 2010, he finds that: Keep Reading

Is beta an effective tool for selecting exchange-traded funds (ETF)? In their October 2010 paper entitled “Black Swans, Beta, Risk, and Return”, Javier Estrada and Mari­a Vargas investigate the usefulness of beta as a metric for constructing portfolios of country and industry ETFs. They use the MSCI world market index (consisting of developed markets only before 1988 and both developed and emerging markets thereafter) as the beta reference. They calculate beta based on a rolling historical window of the most recent 36-60 months (depending on data availability). They arbitrarily define black swan months as those with a world market index return of at least 5% down or up. Using monthly total returns of MSCI indexes for 47 countries (23 developed and 24 emerging) and 57 industries from the earliest month available for each (1970 for the oldest) through December 2009, they find that: Keep Reading

Does high-frequency trading amplify noise and thereby reduce the signal-to-noise ratio in stock returns? In his August 2010 paper entitled “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Frank Zhang examines the effect of high-frequency trading on stock price volatility and on incorporation of fundamental news into price. He defines high-frequency trading as that driven by fully automated trading strategies with very high trading volume and extremely short holding periods ranging from milliseconds to minutes and possibly hours (typically not overnight). He estimates the volume of high-frequency trading as the residual after accounting for institutional and individual investor activities. Using price, trading and institutional holdings data for a broad sample of U.S. stocks from the first quarter of 1985 through the second quarter of 2009, he finds that: Keep Reading

Should long-term investors view their retirement portfolios more like houses than savings plans? In other words, should they start out with considerable leverage and draw the leverage down gradually over time? In their October 2010 paper entitled “Diversification Across Time”, Ian Ayres and Barry Nalebuff investigate the effects of initially implementing and then gradually phasing out leverage for long-term (retirement) equity investment. This strategy exploits the large present value of investments made early in life, while protecting accumulated wealth from equity market volatility late in life. Tests limit initial leverage to 200%. Using return data for U.S. stocks and margin interest rate estimates over the period 1871 through 2009, they conclude that: Keep Reading

How do stock index volatility and variance futures contracts compare with stock index put options as hedges against market crashes? In their August 2010 paper entitled “Using Volatility Instruments as Extreme Downside Hedges”, Bernard Lee and Yueh-Neng Lin investigate the effectiveness of stock index volatility and variance futures contracts as extreme downside hedges and compare this effectiveness to that of out-of-the-money index put options. Specifically, they compare the outcomes of hedging a long Standard & Poor’s Depository Receipts (SPY) position via 1-month and 3-month rolling positions in S&P 500 Volatility Index (VIX) futures contracts, S&P 500 3-month Variance Futures (VT) contracts and 10% out-of-the-money (OTM) S&P 500 Index put options with reasonable hedge trading frictions. Using price data for SPY, VIX and VT futures contracts and index put options spanning 6/10/04-10/14/09 for 1-month rolling hedges and 7/19/04-9/9/09 for 3-month rolling hedges, they find that: Keep Reading

Does average volatility of individual stock prices, as a measure of market risk, usefully predict stock market behavior? In the June 2010 draft of their paper entitled “Average Stock Variance and Market Returns: Evidence of Time-Varying Predictability at the Daily Frequency”, Jason Chen, Hernán Ortiz-Molina and Stacy Zhang investigate the ability of a daily measure of average stock variance to predict next-day market returns. Using daily market returns, daily returns and market capitalizations for a broad sample of individual stocks and daily yields for Treasury bills spanning 1926-2008, they find that: Keep Reading

Conventional theory holds that financial markets reward risk (volatility) with return. Do stocks with relatively high volatilities in fact generate relatively high returns? In his April 2010 paper entitled “Low Risk and High Returns: Evidence from the German Stock Market”, Stefan Koch examines the relationship between past idiosyncratic volatility and future returns for individual German stocks. Using daily stock return and firm characteristics data for a broad sample of German firms spanning 1974-2006, he concludes that: Keep Reading