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.     

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.     

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.     

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.     

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.     

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 volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday

We examine the volatility spillovers among various industries during the COVID-19 pandemic period. We measure volatility spillovers by defining the volatility of each sector in the S&P 500 index and implement a static and rolling-window analysis following the Diebold and Yilmaz (2012) approach. We find that the pandemic enhanced volatility spillovers, which reveals the financial contagion effects on the US stock market. Second, there were sudden, large changes in the dynamic volatility spillovers on Black Monday (March 9, 2020), much of it due to the energy sector shock. These findings have important implications for portfolio managers and policymakers.     

The impacts of index options on the underlying stocks: The case of the S&P 100

Existing theories predict lower trading volume, but ambiguous changes in price, bid–ask spread, and volatility for the underlying stocks following the advent of index derivatives. We further test these predictions around the introduction of the S&P 100 options in March 1983. Controlling for known factors respectively, we find that the listing of the S&P 100 options results in lower volume, spread, and volatility, but no price change for the underlying stocks, contrasting with the existing U.S. evidence and supporting the notion that the arrival of index derivatives induces informed and speculative portfolio traders to migrate from the underlying market to the derivatives market.     

Market effects of private equity placement: Evidence from Chinese equity and bond markets

Research has provided empirical evidence for the stock market reaction toward private placement; however, similar research has not been conducted in terms of the bond market. Using the event study method, we empirically examine the explanatory power of the signaling, free cash flow, and wealth transfer hypotheses based on the reaction of the stock market, bond market, and firm abnormal returns to the private placement announcement. The results show that the stock market has a negative reaction toward private placement, whereas the bond market has a positive reaction. The results also show that the scale of private placement is correlated with the severity of the market reaction. Abnormal returns indicate no significant change both before and after the private placement, and they are unaffected by the scale of private placement. These results are consistent with the wealth transfer hypothesis; however, the market reaction is not attributable to the signaling hypothesis and the free cash flow hypothesis. Extensive research shows that the abnormal returns of private placement change dramatically in non-state-owned enterprises and firms with low credit rating bonds, whereas the bond maturity has no significant impact on the abnormal returns—the wealth transfer effect of private placement is stronger in non-state-owned enterprises and firms with low credit rating bond.     

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.     

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.     

The outbreak of COVID-19 and stock market liquidity: Evidence from emerging and developed equity markets

The outbreak of the novel corona virus has heightened concerns surrounding the adverse financial effects of the outbreak on stock market liquidity and economic policies. This paper contributes to the emerging strand of studies examining the adverse effects of the virus on varied aspect of global markets. The paper examines the causality and co-movements between COVID-19 and the aggregate stock market liquidity of China, Australia and the G7 countries (Canada, France, Italy, Japan, Germany, the UK and the US), using daily three liquidity proxies (Amihud, Spread and Traded Value) over the period December 2019 to July 2020. Our empirical analysis encompasses wavelet coherence and phase-differences as well as a linear Granger causality test. Linear causality test results suggest that a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity. To quantitatively examine the degree of causality between COVID-19 outbreak and stock market liquidity, we employ the continuous wavelet coherence approach with results revealing the unprecedented impact of COVID-19 on stock market liquidity during the low frequency bands for countries that were hard hit with the COVID-19 outbreak, i.e., Italy, Germany, France, the UK and the US. Further, evidence shows that there is a heterogeneous lead-lag nexus across scales for the entire period of the study.     

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.     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

Time-varying price shock transmission and volatility spillover in foreign exchange,bond, equity,and commodity markets: Evidence from the United States

We study the cross-market financial shocks transmission mechanism on the foreign exchange, equity, bond, and commodity markets in the United States using a time-varying structural vector autoregression model with stochastic volatility (TV-SVAR-SV). The price shocks are absorbed immediately in two or three days, suggesting that all markets are quite efficient. A slight mean reversion and an overshooting behavior are observed. Considering the volatility spillover effect, we highlight two properties of volatility shocks. First, the effects of the volatility shocks are released gradually. Reaching peak volatility spillover levels would require five to ten days. Second, the dynamics of volatility spillovers vary tremendously over time. Different types of markets respond to certain, but not all, extreme events. Our findings suggest the need to conduct investor monitoring of current events instead of using technical analysis based on historical data. Investors should also diversify their portfolios using assets that can respond to different and extreme shocks.