Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings*

In this paper we analyze whether presidential approval ratings can predict the S&P 500 returns over the monthly period of July 1941 to April 2018, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC‐MGARCH) model. Our results show that standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC‐MGARCH model, which is robust to such misspecifications, in 69% of the sample period, approval ratings in fact do strongly predict the S&P 500 stock return. Moreover, using the DCC‐MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P 500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time‐varying model.     

Volatility and jump risk in option returns

We examine the importance of volatility and jump risk in the time-series prediction of S&P 500 index option returns. The empirical analysis provides a different result between call and put option returns. Both volatility and jump risk are important predictors of put option returns. In contrast, only volatility risk is consistently significant in the prediction of call option returns over the sample period. The empirical results support the theory that there is option risk premium associated with volatility and jump risk, and reflect the asymmetry property of S&P 500 index distribution.     

A Markov regime switching approach for hedging stock indices

In this paper we describe a new approach for determining time‐varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the “state of the market,” one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out‐of‐sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649–674, 2004     

Forecasting equity returns: The role of commodity futures along the supply chain

This paper examines equity return predictability using the returns of commodity futures along the supply chain in China's financial market. We find that a considerable number of commodities exhibit significant in‐sample forecasting ability at the daily horizon, especially for supplier‐side equity returns. The macroeconomic risk premium effect, captured by the aggregate commodity prices, is an important source for this predictability. The out‐of‐sample results show that for most commodities, the predictability remains both statistically and economically significant, and the forecasting performance improves substantially during recessions or with economic constraints.     

The information content of the S&P 500 index and VIX options on the dynamics of the S&P 500 index

Given that both S&P 500 index and VIX options essentially contain information about the future dynamics of the S&P 500 index, in this study, we set out to empirically investigate the informational roles played by these two option markets with regard to the prediction of returns, volatility, and density in the S&P 500 index. Our results reveal that the information content implied from these two option markets is not identical. In addition to the information extracted from the S&P 500 index options, all of the predictions for the S&P 500 index are significantly improved by the information recovered from the VIX options. Our findings are robust to various measures of realized volatility and methods of density evaluation. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark     

VIX futures pricing with conditional skewness

We develop a closed‐form VIX futures valuation formula based on the inverse Gaussian GARCH process by Christoffersen et al. that combines conditional skewness, conditional heteroskedasticity, and a leverage effect. The new model outperforms the benchmark in fitting the S&P 500 returns and the VIX futures prices. The fat‐tailed innovation underlying the model substantially reduced pricing errors during the 2008 financial crisis. The in‐ and out‐of‐sample pricing performance indicates that the new model should be a default modeling choice for pricing the medium‐ and long‐term VIX futures.     

Market Excess Returns,Variance and the Third Cumulant

In this paper, we develop an equilibrium asset pricing model for market excess returns, variance and the third cumulant by using a jump‐diffusion process with stochastic variance and jump intensity in Cox et al. (1985) production economy. Empirical evidence with the S&P 500 index and options from January, 1996 to December, 2005 strongly supports our model prediction that the lower the third cumulant, the higher the market excess returns. Consistent with existing literature, the theoretical mean–variance relation is supported only by regressions on risk‐neutral variance. We further demonstrate empirically that the third cumulant explains significantly the variance risk premium.     

Volatility as an asset class: Holding VIX in a portfolio

Hedging market downturns without sacrificing upside has long been sought by investors. If VIX was directly investable, adding it as a hedge to the S&P 500 would result in significantly improved performance over the equity only portfolio. However, tradable VIX products do not provide the hedge or returns investors seek over long-term horizons. Alternatively, deconstructing VIX to find the key S&P 500 options which drive VIX movements leads to a synthetic VIX portfolio that provides a more effective hedge. Using these options captures correlations and returns similar to VIX, and combined with the S&P 500, outperforms the buy-and-hold index portfolio.     

Testing mean reversion in financial market volatility: Evidence from S&P 500 index futures

This article presents a comprehensive study of continuous time GARCH (generalized autoregressive conditional heteroskedastic) modeling with the thintailed normal and the fat‐tailed Student's‐t and generalized error distributions (GED). The study measures the degree of mean reversion in financial market volatility based on the relationship between discrete‐time GARCH and continuoustime diffusion models. The convergence results based on the aforementioned distribution functions are shown to have similar implications for testing mean reversion in stochastic volatility. Alternative models are compared in terms of their ability to capture mean‐reverting behavior of futures market volatility. The empirical evidence obtained from the S&P 500 index futures indicates that the conditional variance, log‐variance, and standard deviation of futures returns are pulled back to some long‐run average level over time. The study also compares the performance of alternative GARCH models with normal, Student's‐ t, and GED density in terms of their power to predict one‐day‐ahead realized volatility of index futures returns and provides some implications for pricing futures options. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1–33, 2008     

Central bank communications and equity ETFs

This article examines effects of monetary policy surprises on returns, volatilities, trading volumes, and bid–ask spread of two equity ETFs, the S&P 500 SPY fund and the S&P 400 MDY fund. The policy surprises are measured by both surprises in the federal funds rate target changes and surprises in the future direction of the Federal Reserve monetary policy. The results show that there is an overreaction of the SPY to the federal funds rate target surprise in the first 5 minutes' trading and that both the SPY and the MDY returns, volatilities, trading volumes, and bid–ask spread react more strongly to surprise cuts than to surprise increases in the federal funds rate target. Quantitatively, after 45 minutes, an unanticipated 25‐basis‐point cut in the federal funds rate target is associated with an increase of 1.2 and 1.6% in the SPY and the MDY, respectively, while an unanticipated 25‐basis‐point decline (or rise) in the four‐quarter‐ahead eurodollar futures rate is associated with an increase (or decrease) of 0.71 and 0.40% in the SPY and the MDY, respectively. Further evidence also suggests that the market reacts more strongly to surprises in the future direction of monetary policy during the monetary tightening period and that the impact of monetary policy surprises depends on their sizes. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:959–995, 2006     

The information content of implied volatility: Evidence from Australia

This study develops an implied volatility index for the Australian stock market, termed as the AVX, and assesses its information content. The AVX is constructed using S&P/ASX 200 index options with a constant time‐to‐maturity of three months. It is observed that the AVX has a significant negative and asymmetric relationship with S&P/ASX 200 returns. When evaluating the forecasting power of the AVX for future stock market volatility, it is found that the AVX contains important information both in‐sample and out‐of‐sample. In‐sample, the AVX significantly improves the fit of a GJR‐GARCH(1, 1) model. Out‐of‐sample, the AVX significantly outperforms the RiskMetrics approach and the GJR‐GARCH(1, 1) model, with its highest forecasting power at the one‐month forecasting horizon. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:134–155, 2010     

The Disappearing January/Turn of the Year Effect: Evidence From Stock Index Futures and Cash Markets

This study examines the returns, relative to the S&P 500, on cash indices and futures tracking smaller stocks around the turn of the year. While we control for volatility clustering, return autocorrelation in small stock indices, and other calendar effects, our main focus is the evolution of the turn of the year effect through time: in particular, whether the effect is smaller or takes place earlier subsequent to the introduction of the S&P Midcap and Russell 2000 futures in 1993. We find that evidence of a traditional turn of the year effect, in both cash and futures, is confined to the pre‐1993 period. Post‐1993, there are no abnormal returns during the turn of the year window as a whole. Interestingly, returns in this period remain high on the last trading day of December, but they are negative across the first five trading days of January. In addition, post‐1993, we often observe significant abnormal returns prior to the traditional turn of the year, i.e., in the pre‐Christmas and post‐Christmas windows. Taken together, our results suggest that market participants may be eliminating the turn of the year effect with the aid of two new futures contracts that are well suited to this purpose. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:755–784, 2004     

MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES

We examine the performances of several popular Lévy jump models and some of the most sophisticated affine jump‐diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Lévy jump models using stock and option prices. We show that models with infinite‐activity Lévy jumps in returns significantly outperform affine jump‐diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk‐neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.     

Aggregate Volatility and Market Jump Risk: An Option‐Based Explanation to Size and Value Premia

It is well documented that stock returns have different sensitivities to changes in aggregate volatility, however less is known about their sensitivity to market jump risk. By using S&P 500 crash‐neutral at‐the‐money straddle and out‐of‐money put returns as proxies for aggregate volatility and market jump risk, I document significant differences between volatility and jump loadings of value versus growth, and small versus big portfolios. In particular, small (big) and value (growth) portfolios exhibit negative (positive) and significant volatility and jump betas. I also provide further evidence that both volatility and jump risk factors are priced and negative. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:34–55, 2014     

Pricing efficiency of the S&P 500 index market: Evidence from the Standard & Poor's Depositary Receipts

Standard & Poor's Depositary Receipts (SPDRs) are exchange traded securities representing a portfolio of S&P 500 stocks. They allow investors to track the spot portfolio and better engage in index arbitrage. We tested the impact of the introduction of SPDRs on the efficiency of the S&P 500 index market. Ex‐post pricing efficiency and ex‐ante arbitrage profit between SPDRs and futures were also examined. We found an improved efficiency in the S&P 500 index market after the start of SPDRs trading. Specifically, the frequency and length of lower boundary violations have declined since SPDRs began trading. This result is consistent with the hypothesis that SPDRs facilitate short arbitrage by simplifying the process of shorting the cash index against futures. Tests of pricing efficiency comparing SPDRs and futures suggested that index arbitrage using SPDRs as a substitute for program trading in general results in losses. Although short arbitrages earn a small profit on average, gains are statistically insignificant. A trade‐by‐trade investigation showed that prices are instantaneously corrected after the presence of mispricing signals, introducing substantial risk in arbitraging. Evidence in general supported pricing efficiency between SPDRs and the S&P 500 index futures—both ex‐post and ex‐ante. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:877–900, 2002     

Exchange Traded Funds and Stock Market Volatility

This study investigates the relationship between the volatility of stock market indexes and the trading volumes of their exchange traded funds (ETFs). Using both ordinary least squares and generalized autoregressive conditional heteroskedasticity approaches, we demonstrate that the contemporaneous trading volume of S&P 500 ETFs is a key determinant of S&P 500 volatility at both monthly and daily frequencies. Vector autoregressive estimation on the other hand suggests a two‐way Granger causality between S&P 500 volatility and the trading of S&P 500 ETFs. A replication analysis of other market indexes and the corresponding ETFs tracking these indexes confirms that these findings are robust.     

The limits to stock index arbitrage: Examining S&P 500 futures and SPDRS

This study examines factors affecting stock index spot versus futures pricing and arbitrage opportunities by using the S&P 500 cash index and the S&P 500 Standard and Poor's Depository Receipt (SPDR) Exchange‐Traded Fund (ETF) as “underlying cash assets.” Potential limits to arbitrage when using the cash index are the staleness of the underlying cash index, trading costs, liquidity (volume) issues of the underlying assets, the existence of sufficient time to execute profitable arbitrage transactions, short sale restrictions, and the extent to which volatility affects mispricing. Alternatively, using the SPDR ETF as the underlying asset mitigates staleness and trading cost problems as well as the effects of volatility associated with the staleness of the cash index. Minute‐by‐minute prices are compared over different volatility levels to determine how these factors affect the limits of S&P 500 futures arbitrage. Employing the SPDR as the cash asset examines whether a liquid tradable single asset with low trading costs can be used for pricing and arbitrage purposes. The analysis examines how long mispricing lasts, the impact of volatility on mispricing, and whether sufficient volume exists to implement arbitrage. The minute‐by‐minute liquidity of the futures market is examined using a new transaction volume futures database. The results show that mispricings exist regardless of the choice of the underlying cash asset, with more negative mispricings for the SPDR relative to the S&P 500 cash index. Furthermore, mispricings are more frequent in high‐ and mid‐volatility months than in low‐volatility months and are associated with higher volume during high‐volatility months. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1182–1205, 2008     

Is volatility risk priced in the securities market? Evidence from S&P 500 index options

The authors examine whether volatility risk is a priced risk factor in securities returns. Zero‐beta at‐the‐money straddle returns of the S&P 500 index are used to measure volatility risk. It is demonstrated that volatility risk captures time variation in the stochastic discount factor. The results suggest that straddle returns are important conditioning variables in asset pricing, and investors use straddle returns when forming their expectations about securities returns. One interesting finding is that different classes of firms react differently to volatility risk. For example, small firms and value firms have negative and significant volatility coefficients, whereas big firms and growth firms have positive and significant volatility coefficients during high‐volatility periods, indicating that investors see these latter firms as hedges against volatile states of the economy. Overall, these findings have important implications for portfolio formation, risk management, and hedging strategies. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:617–642, 2007     

Is investor misreaction economically significant? Evidence from short‐ and long‐term S&P 500 index options

Several recent studies present evidence of investor misreaction in the options market. Although the interpretation of their results is still controversial, the important question of economic significance has not been fully addressed. Here this gap is addressed by formulating regression‐based tests to identify misreaction and its duration and constructing trading strategies to exploit the empirical patterns of misreaction. Regular S&P 500 index options and long‐dated S&P 500 LEAPS are used to find an underreaction that on average dissipates over the course of 3 trading days and an increasing misreaction that peaks after four consecutive daily variance shocks of the same sign. Option trading strategies based on these findings produce economically significant abnormal returns in the range of 1–3% per day. However, they are not profitable in the presence of transaction costs. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:717–752, 2005     

A note on closet-indexing

Actively managed equity mutual funds with returns that are highly correlated with popular stock market indices like the S&P 500 often are accused of “closet-indexing”. We ask whether a trading strategy that shorts these funds and buys “true” index funds makes money for investors. Using a sample of high-R² growth-and-income funds from the CRSP survivor bias free mutual fund file, we document that the Sharpe ratio of this trading strategy and the market Sharpe ratio are statistically indistinguishable in the 1991–2000 sample period. The result is consistent with the view that widespread closet-indexing does not exist in the mutual fund industry.