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.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

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.     

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.     

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.     

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.     

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.     

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.     

Predicting volatility using the Markov-switching multifractal model: Evidence from S&P 100 index and equity options

In this paper, we evaluate the performance of the ability of Markov-switching multifractal (MSM), implied, GARCH, and historical volatilities to predict realized volatility for both the S&P 100 index and equity options. Some important findings are as follows. First, we find that the ability of MSM and GARCH volatilities to predict realized volatility is better than that of implied and historical volatilities for both the index and equity options. Second, equity option volatility is more difficult to be forecast than index option volatility. Third, both index and equity option volatilities can be better forecast during non-global financial crisis periods than during global financial crisis periods. Fourth, equity option volatility exhibits distinct patterns conditional on various equity and option characteristics and its predictability by MSM and implied volatilities depends on these characteristics. And finally, we find that MSM volatility outperforms implied volatility in predicting equity option volatility conditional on various equity and option characteristics.     

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.     

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.     

A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easy-to-implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and Lévy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.     

Stock prices and the location of trade: Evidence from China-backed ADRs

This study examines whether the trading location affects equity returns of China-backed American Depository Receipts (ADRs) traded in the US. If International Financial Markets are integrated, stock prices should be affected only by their fundamentals; otherwise, stock prices may also be affected by their trading locations/investor sentiment. We find that China ADRs’ returns are affected more by the US market fluctuations than by Chinese market returns. We interpret the results as suggesting that International Financial Markets are at least partially segmented and country-specific investor sentiment affects stock prices.     

Wavelet coherence analysis of returns,volatility and interdependence of the US and the EU money markets: Pre & post crisis

This research analyse the US and the EU money markets interdependence from 2004 to 2018. The study explains to what extent the volatility of the chosen money markets instruments in two regions is inter-correlated before, during and after the financial crisis of 2008. We apply the econometric analysis and estimate time-series models of class GARCH to study the historical dynamics of interbank rates and bond returns. The study demonstrates that correlation between returns of analogous money market instruments in the EU and US is not stable over time. We find that correlation rises in periods when countries are exposed to the same external shocks as global financial crisis. Wavelet coherence analysis suggests that investors do not get any advantages of portfolio diversification investing only in US treasuries with different maturities for more than 256 days and do not get any advantages at all investing only in European bonds.     

Misvaluation comovement,market efficiency and the cross-section of stock returns: Evidence from China

In this study, we examine the relation between stock misvaluation and expected returns in China's A-share market. We measure individual stocks’ misvaluation based on their pricing deviation from fundamental values, following Rhodes-Kropf et al. (2005. J. Finan. Econ. 77 (3), 561) and Chang et al. (2013. J. Bank. Finance, forthcoming), and find that the measure has strong and robust return predictive power in the Chinese market. We further form a misvaluation factor and find that misvaluation comovement and systematic misvaluation exist in the Chinese market. A comparison of our results with those of Chang et al. (2013. J. Bank. Finance, forthcoming) reveals that the misvaluation effect is much stronger in the Chinese market than in the U.S market. This evidence is consistent with the notion that the Chinese market is much less efficient than the U.S. market. Finally, we show that the return predictive power of misvaluation has weakened since China launched its split-share structure reform in 2005, which could result from the fact that the reform helps to promote market efficiency.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Role of credit and monetary policy in determining asset prices: Evidence from emerging market economies

We examine the relative dominance of credit and monetary policy shocks in influencing asset prices in emerging markets. Estimates from panel VAR models for 22 EMEs provide evidence of a significant impact of bank credit on house prices in contrast to trivial impact on stock prices, possibly due to prudential regulations on banks’ exposure to stock markets. Contractionary monetary policy triggers sizeable and persistent decline in stock than housing prices as higher interest rates may render the funding of leverage costlier. Global shocks play an important role in explaining fluctuations in domestic stock prices rather than house prices since the latter class of asset is largely non-tradable across countries.     

Heterogeneity dependence between oil prices and exchange rate: Evidence from a parametric test of Granger causality in quantiles

This paper re-examines the nexus between crude oil price and exchange rate by investigating their heterogeneity dependence structure within the framework of Granger causality in quantiles for a sample of developed and emerging economies (namely UK, Canada, Brazil, Russia, Mexico, Norway, India, Japan, South Africa, South Korea and European Union (EU)). The results indicate no distinct causality between the crude oil price changes and the real exchange rate returns for all countries besides Russia at the median of the conditional distribution. Besides, the crude oil price changes influence the exchange rate returns in all countries, except Norway and EU, particularly around the tails of the conditional distributions of exchange rate returns. This suggests that the oil price changes influence the real exchange rate returns when the real exchange rate returns are either in extreme appreciation or depreciation. Moreover, the crude oil price movement can be explained by the exchange rate returns for most oil importers only when the crude oil market is bearish or bullish. By contrast, the real exchange rate can permanently affect the crude oil price for most oil-importing countries irrespective of the crude oil market's state. Finally, our findings provide an essential reference for managing the extreme risk dependence between the exchange rate market and the crude oil market.