Dynamic hedge ratio for stock index futures: application of threshold VECM

This study represents one of the first papers in stock-index-futures arbitrage literature to investigate the effects of arbitrage threshold on stock index futures hedging effectiveness by using threshold vector error correction model (hereafter threshold VECM). Moreover, in contrast to prior studies focusing on examining case studies involving mature stock markets, this study not only adopts US S&P 500 stock market as the sample but also adds an analysis of one emerging stock market, Hungarian BSI and examines the differences between them. Finally, this investigation employs a rolling estimation process to examine the impact of arbitrage threshold behaviours on the setting of futures hedging ratio. The empirical findings of this study are consistent with the following notions. First, arbitrage behaviour reduces co-movement between futures and spot markets and increases the volatility of both futures and spot markets. Second, this article denotes the outer regime of futures-spot market for the case of Hungarian BSI (US S&P 500) as a crisis (an unusual) condition. Moreover, arbitrage threshold behaviours make remarkable (unremarkable) shift on optimal hedge ratio between two different market regimes for the case of Hungarian BSI (US S&P 500). Finally, the framework involving regime-varying hedge ratio designed in this study provides a more efficient futures hedge ratio design for Hungarian BSI stock market, but not for US S&P 500 stock market.     

On the Internal Consistency of Stock Market Forecasts

Using the Livingston survey data, we test internal consistency restrictions on short-term, medium-term, and long-term stock market forecasts of the S&P 500®. We find that neither short-term forecasts are consistent with medium-term forecasts nor that medium-term forecasts are consistent with long-term forecasts. Using a forecast formation process featuring a distributed lag structure, however, we find some weak evidence of internal inconsistency of medium-term forecasts with long-term forecasts.     

Information and adjustment in a local housing market

Existing studies on bubbles have been mainly concerned with investigating the stationarity properties of stock prices and market fundamentals. We develop a new method of testing for bubbles that relates the bubble component of stock prices to the probability of bursting in the context of the Weibull distribution. There were several eruptions and subsequent collapses of seeming bubbles over the past three decades: 1987 (Black Monday), 2000 (information technology (IT) boom) and 2007 (housing market boom). Using US monthly data for the S&P 500 and NASDAQ series, we have found that the S&P 500 series contained an explosive bubble only during the boom of the housing market that occurred before the 2007 global economic crisis, and the NASDAQ market contained an explosive bubble during the surge of stock prices peaking in 1987 and 2007, although our stationarity tests fail to detect the bubbles. No bubble was found in both the S&P and NASDAQ series during the 2000 IT boom. Our evidence corroborates the criticism that the traditional unit root and cointegration tests may not be able to detect some important class of bubbles.     

Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective

We propose to use the wavelet concept of the phase angle to determine the lead–lag relationship between investor sentiment and excess returns that are related to the bubble component of stock prices. The wavelet phase angle allows for decoupling short- and long-run relations and is additionally capable of identifying time-varying comovement patterns. Based on the monthly S&P500 index and two alternative monthly US sentiment indicators, we find that in the short run (until 3 months), sentiment is leading returns whereas for periods above 3 months the opposite can be observed. Moreover, the initially strong positive relationship becomes less pronounced with increasing time horizon, thereby indicating that the over- or undervaluation in the short run is gradually corrected in the long run.     

Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails

This article extends the quasi-autoregressive (QAR) plus Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) dynamic conditional score (DCS) model. For the new DCS model, the degrees of freedom parameter is time varying and tail thickness of the error term is updated by the conditional score. We compare the performance of QAR plus Beta-t-EGARCH with constant degrees of freedom (benchmark model) and QAR plus Beta-t-EGARCH with time-varying degrees of freedom (extended model). We use data from the Standard and Poor’s 500 (S&P 500) index, and a random sample of its 150 components that are from different industries of the United States (US) economy. For the S&P 500, all likelihood-based model selection criteria support the extended model, which identifies extreme events with significant impact on the US stock market. We find that for 59% of the 150 firms, the extended model has a superior statistical performance. The results suggest that the extended model is superior for those industries, which produce products that people usually are unwilling to cut out of their budgets, regardless of their financial situation. We perform an application to compare the density forecast performance of both DCS models. We perform an application to Monte Carlo value-at-risk for both DCS models.     

A Study of Social Network Effects on the Stock Market

Consumer confidence is an economic indicator that measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation. The authors measure consumer sentiment via analysis of social networks and show that such sentiment affects stock prices; specifically, the S&P 500 and the Dow Jones Industrial Average. Shiller, Fischer and Freidman [1984], Fisher and Statman [2003], and Bremmer [2008] also examine the influence of consumer sentiment, measured from Conference Board data, on the stock market. The authors add to this literature by creating a measure of consumer confidence by utilizing Twitter data and by examining the relationship between our measure of consumer sentiment and the S&P 500 and the Dow. They implemented lexicographic analysis of Twitter data over a three-month period and found that talk intensity of economic issues not only causes shifts in the daily stock market prices, but also has a significant negative effect.     

Determinants of stock market comovements among US and emerging economies during the US financial crisis

By analyzing the dynamic conditional correlations (DCC) of the daily stock returns of 10 emerging economies in comparison with those of the US for the period of 2006–2010, we find different patterns of crisis spillover among 10 emerging economies. While a group of countries has three distinctive phases of crisis spillover (contagion, herding, and post-crisis adjustment), other groups show different phases of crisis spillover. It is also shown that increases in CDS spread and TED spread decrease conditional correlations while increases in foreign institutional investment, exchange market volatility, and the VIX index of the S&P 500 increase conditional correlations.     

The effect of quantitative easing on stock prices: a structural time series approach

A structural time series model is estimated and tested to examine the effect of quantitative easing (QE) on US stock prices. The model is estimated by maximum likelihood in a Time-varying parametric (TVP) framework, using the S&P 500 index as the dependent variable and the Fed’s balance as an explanatory variable in addition to the unobserved components accounting for the behaviour of variables that do not appear explicitly in the equation. The results show that QE had a sizeable, but not exclusive, effect on stock prices and that stock prices were also affected by other missing variables and cyclical movements. Several explanations are presented for the rise of the US stock market in the post-QE period, particularly since the election of Donald Trump.     

Which types of commodity price information are more useful for predicting US stock market volatility?

This study aims to investigate which types of commodity price information are more useful for predicting US stock market realized volatility (RV) in a data-rich word. The standard predictive regression framework and monthly RV data are used to explore the RV predictability of commodity futures for the next-month RV on S&P 500 spot index. We utilize principal component analysis (PCA) and factor analysis (FA) to extract the common factors for each type and all types of commodity futures. Our results indicate that the futures volatility information of grains and softs has a significant predictive ability in forecasting the RV of the S&P 500. In addition, the FA method can yield better forecasts than the PCA and average methods in most cases. Further analysis shows that the volatility information of grains and softs exhibits higher informativeness during recessions and pre-crises. Finally, the forecasts of the five combination methods and different out-of-sample periods confirm our results are robust.     

Market overreaction and investment strategies

We investigated the overreaction of the Korean market in response to shocks in the US stock market, and analysed the dynamic relationship between these two markets since 1996. We found that the KOSPI 200 index futures overreacted to the S&P 500 index returns during the period from 2000 to 2009 when the Korean market was in its growth stage. As the Korean market matured and the KOSPI 200 overnight futures were introduced in 2009, the overreaction disappeared. When investors employed the Kelly model or Value-at-Risk to exploit the overreaction, their trading strategies produced significant profits during the growth stage even after considering transaction costs and risk, but the profits attenuated once the overnight futures market was launched in 2009.     

THE LEAD–LAG RELATION BETWEEN THE S&P500 SPOT AND FUTURES MARKETS: AN INTRADAY-DATA ANALYSIS USING A THRESHOLD REGRESSION MODEL

In this paper we examine the lead–lag interaction between the futures and spot markets of the S&P500 using the threshold regression model on intraday data. The use of threshold variables to model the changes in the regression structure with respect to different market conditions enables us to investigate the lead–lag interaction in a data-based approach and avoid stratifying the data arbitrarily. Using the basis as the threshold variable, we find that the short-selling restrictions in the spot market reduce the effect of the spot index as the leading variable. To study the effect of market-wide information on the interaction between the spot and futures markets, we use the coefficient of determination in the regression of the S&P500 on the Morgan–Stanley Composite Index-US and the Major Market Index as the threshold variable. We find that the lead effect of the futures market over the spot market is stronger when there is more market-wide information. On the other hand, the lead effect of the cash market over the futures market is weaker when there is more market-wide information. In addition, we also use the lagged 45-min return of the spot market as the threshold variable. We find that the lead effect of the spot market is stronger in periods of directionless trading than in periods of good or bad markets.     

Calendar anomalies in cash and stock index futures: International evidence

This paper examines calendar anomalies (day-of-the-week and monthly seasonal effects) in cash and stock index futures returns. We consider daily data from FTSE100 (UK), FTSE/ASE-20 (Greece), S&P500 (US) and Nasdaq100 (US) spot and future indexes over the period 2004–2011. We employ a Regime-Switching specification which allows us to distinguish between different regimes corresponding to high and low volatile periods. The results show differences in the seasonal patterns in cash and futures indexes due to the existence of basis risk. Calendar effects are also conditioned to the market situation. During a low volatile situation these calendar effects tend to be positive, but these effects turn negative if the market is under a high volatile period. These findings are recommended to financial risk managers dealing with futures markets.     

Oil and US stock market shocks: Implications for Canadian equities

Oil and US stock market shocks are relevant to Canadian equities because Canada is an oil exporter exposed to market developments in the wider continent. We evaluate how the relationship between Canadian stock market indices and such external shocks change under extraordinary events. To do this, we subject statistically identified oil and S&P 500 market shocks to a surprise filter, which detects shocks with the greatest magnitude occurring over a given lookback period, and an outlier filter, which detects extrema shocks that exceed a normal range. Then, we examine how the dependence structure between shocks and Canadian equities change under the extreme surprise and outlier episodes through various co-moment spillover tests. Our results show co-moments beyond correlation are important in reflecting the changes occurring in the relationships between external shocks and Canadian equities in extreme events. Additionally, the differences in findings under extreme positive and negative shocks provide evidence for asymmetric spillover effects from the oil and US stock markets to Canadian equities. Moreover, the observed heterogeneity in the relationships between disaggregated Canadian equities and shocks in the crude oil and S&P 500 markets are useful to policy-makers for revealing sector-specific vulnerabilities and provide portfolio diversification opportunities for investors to exploit.     

Correlations and volatility spillovers across commodity and stock markets: Linking energies,food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets.     

Stock market development and real economic activity in Peru

We explore the causal effect of stock market development on real economic activity in Peru by setting up a simple growth model that underpins long-run identifying restrictions for vector autoregressive models. This allows us to identify stock market shocks and to uncover the dynamic response of real output per capita. Using annual time series data for the period 1965–2013, we find that stock market shocks have had a short-run causal effect on real GDP per capita only after 1991, a result that is consistent with standard Granger causality tests; however, the contribution of stock market shocks to output growth dynamics has been small. Thus, policy actions aimed at further developing the Peruvian stock market may have a positive impact on the dynamics of economic growth.     

An information theoretic analysis of stock returns,volatility and trading volumes

Information theory is used to examine the dynamic relationships between stock returns, volatility and trading volumes for S&P500 stocks. This provides an alternative approach to traditional Granger causality tests when dealing with nonlinear relationships. The article highlights the dominant role played by trading volumes in all of these relationships – even in the return–volatility relation – and finds evidence of a market level feedback effect from index returns to the return–volatility relation at the stock level. The article also produces a number of stylized facts from an information theoretic perspective.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Common trends and common cycles in stock markets

In this paper we examine the role of permanent and transitory shocks in explaining variations in the S&P 500, Dow Jones and the NASDAQ. Our modeling technique involves imposing both common trend and common cycle restrictions in extracting the variance decomposition of shocks. We find that: (1) the three stock price indices are characterized by a common trend and common cycle relationship; and (2) permanent shocks explain the bulk of the variations in stock prices over short horizons.     

QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500

Statistical performance and out-of-sample forecast precision of ARMA-GARCH and QARMA-Beta-t-EGARCH are compared. We study daily returns on the Standard and Poor’s 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 for period May 2006 to July 2010. Competing models are estimated for periods before and during the US financial crisis of 2008. Out-of-sample point and density forecasts are performed for periods during and after the US financial crisis. The results provide evidence of the superior in-sample statistical and out-of-sample predictive performance of QARMA-Beta-t-EGARCH.     

S&P 500 Affiliation and Stock Price Informativeness

Abstract

When firms are added to a stock index, more information should be discovered, traded on, and incorporated into their stock prices, making them more informative. We test this hypothesis using a large sample of additions to the S&P 500 index. Using two alternative statistical tests, we find that the stocks added experience more random, less predictable return and, thus, appear to be priced more efficiently information-wise. We further find concurrent increases in institutional ownership and investor awareness, which tend to contribute to the higher pricing efficiency, adding to the literature. These findings should be of interest to academics and practitioners.