Pinning in the S&P 500 futures

We show that Standard & Poor's (S&P) 500 futures are pulled toward the at-the-money strike price on days when serial options on the S&P 500 futures expire (pinning) and are pushed away from the cost-of-carry adjusted at-the-money strike price right before the expiration of options on the S&P 500 index (anti-cross-pinning). These effects are driven by the interplay of market makers' rebalancing of delta hedges due to the time decay of those hedges as well as in response to reselling (and early exercise) of in-the-money options by individual investors. The associated shift in notional futures value is at least $115 million per expiration day.     

S&P 500 Index Additions and Earnings Expectations

Stock price increases associated with addition to the S&P 500 Index have been interpreted as evidence that demand curves for stocks slope downward. A key premise underlying this interpretation is that Index inclusion provides no new information about companies' future prospects. We examine this premise by analyzing analysts' earnings per share (eps) forecasts around Index inclusion and by comparing postinclusion realized earnings to preinclusion forecasts. Relative to benchmark companies, companies newly added to the Index experience significant increases in eps forecasts and significant improvements in realized earnings. These results indicate that S&P Index inclusion is not an information‐free event.     

Changes in cash holdings around the S&P 500 additions

We analyze the changes in cash holding policies of S&P 500 firms from before to after their inclusion in the index. One year after inclusion, their mean industry-adjusted cash holdings decline by nearly 32% from the year before inclusion. Several factors explain this decline. The precautionary motive for cash subsides due to these firms becoming more visible, less uncertain, and less constrained to raise cheap external capital. Corporate governance deteriorates after inclusion due to increased managerial entrenchment, which leads to a reduction in cash as suggested by the free cash flow hypothesis. Most index firms face diminishing investment opportunities and decreasing capital expenditures, which implies a lesser need for cash holdings related to the transaction motive.     

Founding-Family Ownership and Firm Performance: Evidence from the S&P 500

We investigate the relation between founding‐family ownership and firm performance. We find that family ownership is both prevalent and substantial; families are present in one‐third of the S&P 500 and account for 18 percent of outstanding equity. Contrary to our conjecture, we find family firms perform better than nonfamily firms. Additional analysis reveals that the relation between family holdings and firm performance is nonlinear and that when family members serve as CEO, performance is better than with outside CEOs. Overall, our results are inconsistent with the hypothesis that minority shareholders are adversely affected by family ownership, suggesting that family ownership is an effective organizational structure.     

Analysis of DJIA,S&P 500, S&P 400, NASDAQ 100 and Russell 2000 ETFs and their influence on price discovery

This study examines the temporal behavior of price discovery in the spot, ETF and futures markets of the DJIA, S&P 500, S&P 400, NASDAQ 100 and Russell 2000. We document an increasing trend in the price discovery metrics of exchange traded funds for all indexes but the DJIA. Contrary to past studies, our findings show that the spot market rather than the futures market leads the price discovery. The arbitrage process that links exchange traded funds to spot prices, and not the futures prices might explain the results. This daily arbitrage that ensures exchange traded funds prices equal net asset values appear to promote spot market price discovery especially with the popularity of exchange traded funds in more recent years. We additionally document that the temporal behavior of the exchange traded funds price discovery metric affects differently price discovery in the spot and futures markets across indexes.     

Bounds for VIX futures given S&P 500 smiles

We derive sharp bounds for the prices of VIX futures using the full information of S&P 500 smiles. To that end, we formulate the model-free sub/superreplication of the VIX by trading in the S&P 500 and its vanilla options as well as the forward-starting log-contracts. A dual problem of minimizing/maximizing certain risk-neutral expectations is introduced and shown to yield the same value.The classical bounds for VIX futures given the smiles only use a calendar spread of log-contracts on the S&P 500. We analyze for which smiles the classical bounds are sharp and how they can be improved when they are not. In particular, we introduce a family of functionally generated portfolios which often improves the classical bounds while still being tractable; more precisely, they are determined by a single concave/convex function on the line. Numerical experiments on market data and SABR smiles show that the classical lower bound can be improved dramatically, whereas the upper bound is often close to optimal.     

Accounting for decarbonisation and reducing capital at risk in the S&P500

This article accounts for carbon emissions in the S&P 500 and explores the extent to which capital is at risk from decarbonising value chains. At a global level it is proving difficult to decouple carbon emissions from GDP growth. Top-down legal and regulatory arrangements envisaged by the Kyoto Protocol are practically redundant given inconsistent political commitment to mitigating global climate change and promoting sustainability. The United Nations Environment Programme (UNEP) and European Commission (EC) are promoting the role of financial markets and financial institutions as drivers of behavioural change mobilising capital allocations to decarbonise corporate activity.     

The jump component of S&P 500 volatility and the VIX index

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing of past volatility to generate forecasts. Implied volatility has the potential to reflect information that a model-based forecast could not. This paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information pertaining to future jump activity relative to model-based forecasts. It is found that the VIX index both subsumes information relating to past jump contributions to total volatility and reflects incremental information pertaining to future jump activity. This issue has not been examined previously and expands our understanding of how option markets form their volatility forecasts.     

An Examination of Option-Implied S&P 500 Futures Price Distributions

Expected S&P 500 futures price distributions are derived using no-arbitrage option pricing models. These distributions are parameterized both as the lognormal and as a less restrictive three-parameter Burr-XII distribution. The resulting option-based probability assessments display some evidence of miscalibration very near to expiration and far from expiration, but are accurate over intermediate time ranges. The means of the implied price distributions correspond closely to the contemporaneous futures prices for both distributions, although marginally better with the Burr-XII. The Burr-XII distribution also performs better than the lognormal based on calibration statistics, and hence, is used to recalibrate estimated distributions.     

Hedging the black swan: Conditional heteroskedasticity and tail dependence in S&P500 and VIX

The recent financial crisis has accentuated the fact that extreme outcomes have been overlooked and not dealt with adequately. While extreme value theories have existed for a long time, the multivariate variant is difficult to handle in the financial markets due to the prevalent heteroskedasticity embedded in most financial time series, and the complex extremal dependence that cannot be conveniently captured by a single structure. Moreover, most of the existing approaches are based on a limiting argument in which all variables become large at the same rate. In this paper, we show how the conditional approach of Heffernan and Tawn (2004) can be implemented to model extremal dependence between financial time series. We use a hedging example based on VIX futures to demonstrate the flexibility and superiority of the conditional approach against the conventional OLS regression approach.     

Volatility dynamics for the S&P 500: Further evidence from non-affine,multi-factor jump diffusions

We apply Markov chain Monte Carlo methods to time series data on S&P 500 index returns, and to its option prices via a term structure of VIX indices, to estimate 18 different affine and non-affine stochastic volatility models with one or two variance factors, and where jumps are allowed in both the price and the instantaneous volatility. The in-sample fit to the VIX term structure shows that the second (stochastic long-term volatility) factor is required to fit the VIX term structure. Out-of-sample tests on the fit to individual option prices, as well as in-sample tests, show that the inclusion of jumps is less important than allowing for non-affine dynamics. The estimation and testing periods together cover more than 21 years of daily data.     

Volatilities implied by price changes in the S&P 500 options and futures contracts

We develop a new volatility measure: the volatility implied by price changes in option contracts and their underlying. We refer to this as price-change implied volatility. We compare moneyness and maturity effects of price-change and implied volatilities, and their performance in delta hedging. We find that delta hedges based on a price-change implied volatility surface outperform hedges based on the traditional implied volatility surface when applied to S&P 500 future options.     

A comprehensive long-term analysis of S&P 500 index additions and deletions

We investigate the long-term effects of S&P 500 index additions and deletions on a sample of stocks from 1962 to 2003 and find a significant long-term price increase for both added and deleted stocks, with deleted stocks outperforming added stocks. The long-term price increase for added stocks can be attributed to increases in institutional ownership, liquidity, and analyst coverage, and a decrease in the shadow cost in the long-term. However, while deletion has no significant effect on analyst coverage and shadow cost, we find a rebound in the institutional ownership and liquidity of deleted stocks. The difference in the long-term price increase of added and deleted stocks can be explained by analyst coverage and operating performance.     

A Nonlinear Factor Analysis of S&P 500 Index Option Returns

Growing evidence suggests that extraordinary average returns may be obtained by trading equity index options, and that at least part of this abnormal performance is attributable to volatility and jump risk premia. This paper asks whether such priced risk factors are alone sufficient to explain these average returns. To provide an answer in as general as possible a setting, I estimate a flexible class of nonlinear models using all S&P 500 Index futures options traded between 1986 and 2000. The results show that priced factors contribute to these expected returns but are insufficient to explain their magnitudes, particularly for short‐term out‐of‐the‐money puts.     

SV mixture models with application to S&P 500 index returns

Understanding both the dynamics of volatility and the shape of the distribution of returns conditional on the volatility state is important for many financial applications. A simple single-factor stochastic volatility model appears to be sufficient to capture most of the dynamics. It is the shape of the conditional distribution that is the problem. This paper examines the idea of modeling this distribution as a discrete mixture of normals. The flexibility of this class of distributions provides a transparent look into the tails of the returns distribution. Model diagnostics suggest that the model, SV-mix, does a good job of capturing the salient features of the data. In a direct comparison against several affine-jump models, SV-mix is strongly preferred by Akaike and Schwarz information criteria.     

Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options

We build a new class of discrete-time models that are relatively easy to estimate using returns and/or options. The distribution of returns is driven by two factors: dynamic volatility and dynamic jump intensity. Each factor has its own risk premium. The models significantly outperform standard models without jumps when estimated on S&P500 returns. We find very strong support for time-varying jump intensities. Compared to the risk premium on dynamic volatility, the risk premium on the dynamic jump intensity has a much larger impact on option prices. We confirm these findings using joint estimation on returns and large option samples.     

CONDITIONAL HETEROSKEDASTICITY AND THE WEEKEND EFFECT IN S&P 500 INDEX FUTURES

Conflicting results have been reported regarding the existence of a weekend effect in S&P 500 index futures. Given the numerous evidence in recent research that asset returns are affected by conditional heteroskedasticity and have fat-tailed distributions, this paper re-examines the existence of a weekend effect in S&P 500 index futures by using a GARCH model. The results generated by the new methodology support the conclusion of Cornell (1985) that there is no weekend effect in S&P 500 index futures.     

Point and Figure charting: A computational methodology and trading rule performance in the S&P 500 futures market

This paper empirically contributes to the existing trading rule literature by providing a methodology for the calculation of Point and Figure charts using ultra-high-frequency data and tests trading rules using eight objective, pre-defined trading rules on S&P 500 futures contracts traded between 1990 and 1998. To assess the robustness of reported profits, a bootstrapping adjustment was conducted to determine the forecasting power of the PF trading rules. The results producing mixed statistical significance with some rules proving significant while many others were not.     

Strategic trading by index funds and liquidity provision around S&P 500 index additions

We examine the trades of index funds and other institutions around S&P 500 index additions. We find index funds begin rebalancing their portfolios with the announcement of composition changes and do not fully establish their positions until weeks after the effective date. Trading away from the effective date is more prevalent for stocks with lower levels of liquidity and among large index funds, which is consistent with index funds accepting higher tracking error in order to reduce the price impact of their trades. Small and mid-cap funds provide liquidity to index funds around additions, and added stocks with a greater proportion of these natural liquidity providers experience lower inclusion returns.