Mispricing and trader positions in the S&P 500 index futures market

This study examines the effect of traders’ net positions on mispricing in the S&P 500 index futures market. We find that while positive mispricing is associated with hedgers’ net short and speculators’ net long positions, negative mispricing is related to hedgers’ net long and speculators’ net short positions. This relationship is stable for speculators across the pre- and post-2004 periods; however, it is dominant for hedgers particularly during the pre-2004 period. Contrary to the popular belief, our analysis finds no evidence that speculators are responsible for irrational movements in futures prices by enlarging the size of mispricing. Furthermore, a high magnitude of hedgers’ net positions signals the convergence of mispricing. We also found that according to a recent new disaggregation for trader positions, asset managers tend to delay the convergence of mispricing and hedge funds help shrink the size of mispricing. However, these relationships are not stronger than those implies by the hedger/speculator classification. These findings support the view that speculators’ positions are informative about the direction of index futures mispricing, while hedgers’ positions determine the convergence of mispricing.     

Contagion testing in frontier markets under alternative stressful S&P 500 market scenarios

We use alternative approaches to identify stable and stressful scenarios in the S&P 500 market, to offer a new perspective for constructing contagion tests in recipient frontier markets vulnerable to disturbances from this source market. The S&P 500 market is decomposed into discrete conditions of: (1) tranquil versus turbulent volatility; (2) bull versus bear market phases; (3) normal periods versus asset bubbles and crashes. Based on these identified scenarios, we use various co-moment contagion tests to analyse the changing relationship between the S&P 500 market and major frontier markets in the Caribbean region that have prominent trade related exposure to the US. Our findings show that, outside of the events of the Great Recession, the Caribbean stock exchanges are largely independent of the S&P 500 market.     

Sense,nonsense and the S&P500

The theory of financial markets is well developed, but before any of it can be applied there are statistical questions to be answered: Are the hypotheses of proposed models reasonably consistent with what data show? If so, how should we infer parameter values from data? How do we quantify the error in our conclusions? This paper examines these questions in the context of the two main areas of quantitative finance, portfolio selection and derivative pricing. By looking at these two contexts, we get a very clear understanding of the viability of the two main statistical paradigms, classical (frequentist) statistics and Bayesian statistics.     

Phase transition in the S&P stock market

We analyze the returns of stocks contained in the Standard & Poor’s 500 index from 1987 until 2011. We use covariance matrices of the firms’ returns determined in a time windows of several years. We find that the eigenvector belonging to the leading eigenvalue (the market) exhibits a phase transition. The market is in an ordered state from 1995 to 2005 and in a disordered state after 2005. We can relate this transition to an order parameter derived from the stocks’ beta and the trading volume. This order parameter can also be interpreted within an agent-based model.     

On the informational efficiency of S&P500 implied volatility

Implied volatility is often considered to represent a market's prediction of future volatility. If such a market was to generate efficient volatility forecasts, implied volatility should reflect all relevant conditioning information. The purpose of this paper is to determine whether a publicly available and commonly used implied volatility index, the VIX index (as published by the Chicago Board of Options Exchange) is in fact efficient with respect to a wide set of conditioning information. Results indicate that the VIX index is not efficient with respect to all elements in the information set that may be used to form volatility forecasts.     

Higher moment exchange rate exposure of S&P500 firms

We examine the impact of higher order moments of changes in the exchange rate on stock returns of U.S. large-cap companies in the S&P500. We find a robust negative effect of exchange rate volatility on S&P500 company returns. The consumer discretionary and the consumer staples sectors have significant negative exposure to exchange rate volatility suggesting that exchange rate volatility affects stock returns through the channel of international operations. In terms of industries, the household products and personal products industries have significant negative exposure as well. The impact in the financial sector suggests that derivatives and hedging activity can mitigate exposure to exchange rate volatility. We find weak evidence that exchange rate skewness has an effect on S&P500 stock returns, but, find evidence that exchange rate kurtosis affects returns of companies that are more exposed to exchange rate volatility.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

A strategy for trading the S&P 500 futures market

A system for trading the S&P 500 futures market is proposed. The system is applied to S&P 500 futures data during the period from September 14, 1987, to September 27, 1999. The system uses a momentum oscillator for generating entry or exit prices. In addition, the system uses another indicator for predicting the direction of the trend. When only the oscillator is used for selecting trades, the system is not, in general, as good as buy-and-hold. However, when the trend indicator is used as a filter, the trading system is, at least, as good as buy-and-hold.     

Production functions and productivity of family firms: Evidence from the S&P 500

This paper examines whether the higher profitability and valuation of family firms is related to differences in production technologies and production efficiency. Using data on S&P 500 manufacturing firms, we find that family firms are more productive than comparable non-family firms. Furthermore, our results show that the production technologies of family and non-family firms do not differ per se, thereby suggesting that the differences in the level of production output are caused by higher production efficiency of family firms. These findings indicate that the superior performance of family firms is related to their more efficient use of labor and capital resources.     

The empirical linkages among market returns,return volatility,and trading volume: Evidence from the S&P 500 VIX Futures

The purpose of this study is to examine the relationships between return and trading volume as well as between return volatility and trading volume by analyzing the asymmetric relationships of contemporaneity and lead-lags between these factors for the S&P 500 VIX Futures Index. We apply the threshold model with the GJR-GARCH framework for empirical analysis herein. The main findings demonstrate that the threshold effects exist in both the contemporaneous and lead-lag relationships between return-volume and volatility-volume. Moreover, the delayed effects of a one-trading-day lag through to three-trading-day lags exist from trading volume to returns and return volatility. Larger trading volume is beneficial for investors to gain returns, but it also leads to higher volatility. The implication of our findings offers a suggestion as to the opportune timing for investors to buy S&P 500 VIX Futures.     

Trading cost expectations: Evidence from S&P 500 index replacement stock announcements

This paper tests the effect on stock value of an expected change in future trading costs. The capitalized value of a reduction in trading costs is hypothesized to increase the stock value, a trading cost effect. Improved liquidity reduces trading costs. Inclusion as an S&P 500 Index replacement stock is an event hypothesized to increase liquidity. We use 114 observations between January 1, 1983 and October 12, 1989 of stocks added to the Index as replacements for stocks removed. The abnormal return of each stock is regressed against the ratio of the bidask spread to the price of the stock, the change in trading volume of the stock, and the open interest in the Index futures contracts at the close of the month prior to the replacement announcement. We find that the positive abnormal returns for replacement stocks are related to increased daily trading volume after inclusion in the Index. Further, the trading cost effect is proportional to percentage bid-ask spreads prior to inclusion. The trading cost effect increases as trading in derivatives of the Index increases. The volume and stock price changes after replacement are not transitory, indicating an improvement in liquidity. Three alternate hypotheses suggested in prior research to explain the abnormal returns for replacement stocks are tested. Testing each of the three models previously considered: price pressure, inelastic demand curves, and information, we find that none can be accepted with statistical confidence. The abnormal returns of Index replacement stocks are consistent with rational pricing of an anticipated reduction in future transaction costs. This anticipated reduction is capitalized in the value of the stock at the time of the replacement announcement. These results are consistent with a trading cost effect.     

Asymmetry in the jump-size distribution of the S&P 500: Evidence from equity and option markets

This paper studies alternative distributions for the size of price jumps in the S&P 500 index. We introduce a range of new jump-diffusion models and extend popular double-jump specifications that have become ubiquitous in the finance literature. The dynamic properties of these models are tested on both a long time series of S&P 500 returns and a large sample of European vanilla option prices. We discuss the in- and out-of-sample option pricing performance and provide detailed evidence of jump risk premia. Models with double-gamma jump size distributions are found to outperform benchmark models with normally distributed jump sizes.     

Comparative empirical study of binomial call-option pricing methods using S&P 500 index data

In this paper, several binomial models are tested empirically on S&P500 Index on the levels of tradability, proximity to market (RMS) prices and profitability, especially close to expiration day. These comparisons will be carried out for many different business environments, including different market trends and moneyness levels traded. Among the models under analysis we assess the quality of the SH model, developed by the authors in previous work, in relation to other models. The option price in the SH model is affected by the players’ assessments about the behavior of the prices of the underlying asset up to the expiration day and by their “eagerness” levels (i.e., players’ readiness to respond to a given bid proposed by their opponent). We found that for all models, the higher the moneyness, the greater the proximity of models prices to actual market prices and that, eagerness parameters have a decisive effect on tradability. We also found that there was no correlation between the degree of proximity of modeled prices to actual prices and the expected profit gained by players that act according to a given model and that the SH model traded relatively small number of options. The expected profit is highest for the SH model in the ITM and ATM for days that are far from the expiration day.     

Model specification of conditional jump intensity: Evidence from S&P 500 returns and option prices

This study investigates the model specification of the conditional jump intensity under option pricing models having a generalized autoregressive conditional heteroskedastic with jumps (GARCH-jump). We compare three GARCH-jump models of Chang, Chang, Cheng, Peng, and Tseng (2018) to examine whether specifying asymmetric jumps in conditional jump intensity can improve the empirical performance. The empirical results from S&P 500 returns and options show that specifying the asymmetric jumps into the conditional jump intensity does improve the in-sample pricing errors and implied volatility errors. However, the out-of-sample results depend on the error measurement.     

Analysis of the probability of deletion of S&P 500 companies: Survival analysis and neural networks approach

We examine the probability of deletion of a firm from the S&P 500 index due to a decision of the index committee because the firm did not satisfy the index committee criteria. We study the probability of deletion with survival analysis and neural networks methods. We document that deletion might be predictable, which is contrary to the findings of most studies that the market cannot predict the timing of a company deletion from the S&P 500 index. It might also be beneficial to know ahead of time which company might be deleted from an index, to supplement the arbitrage opportunities that exist already in the announcement-effective date event window.     

Time series modeling of histogram-valued data: The daily histogram time series of S&P500 intradaily returns

Histogram time series (HTS) and interval time series (ITS) are examples of symbolic data sets. Though there have been methodological developments in a cross-sectional environment, they have been scarce in a time series setting. Arroyo, González-Rivera, and Maté (2011) analyze various forecasting methods for HTS and ITS, adapting smoothing filters and nonparametric algorithms such as the k-NN. Though these methods are very flexible, they may not be the true underlying data generating process (DGP). We present the first step in the search for a DGP by focusing on the autocorrelation functions (ACFs) of HTS and ITS. We analyze the ACF of the daily histogram of 5-minute intradaily returns to the S&P500 index in 2007 and 2008. There are clusters of high/low activity that generate a strong, positive, and persistent autocorrelation, pointing towards some autoregressive process for HTS. Though smoothing and k-NN may not be the true DGPs, we find that they are very good approximations because they are able to capture almost all of the original autocorrelation. However, there seems to be some structure left in the data that will require new modelling techniques. As a byproduct, we also analyze the [90,100%] quantile interval. By using all of the information contained in the histogram, we find that there are advantages in the estimation and prediction of a specific interval.     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

Forecasting returns in the VIX futures market

This paper introduces a new forecasting model for VIX futures returns. The model is structural in nature and parsimonious, and contains parameters that are relatively easy to estimate. The forecasts of next day VIX futures returns based on this model are superior to those produced by a linear forecasting model that uses the same set of predictors. Moreover, the profits to a market-timing model based on the proposed forecasts are statistically and economically significant, and are robust to both the method used for adjusting for risk and transaction costs (up to around 15 basis points). In contrast, the forecasts generated by the linear forecasting model are not.     

Price discovery in the Indian gold futures market

This paper examines the price discovery process of the nascent gold futures contracts in the Multi Commodity Exchange of India (MCX) over the period 2003 to 2007. The study employs vector error correction models (VECMs) to show that futures prices of both standard and mini contracts lead spot price. We find that mini contracts contribute to over 30% of price discovery in gold futures trade even though they account for only 2% of trading value on the MCX. Our finding reveals that trades initiated in mini contracts are much more informative than what the size of their market share of volume suggests.