Twitter’s daily happiness sentiment,economic policy uncertainty,and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.     

S&P 500 futures market and the normal backwardation hypothesis

This paper examines the behavior of near term S&P 500 index futures contract prices in the context of the theory of normal backwardation. Daily S&P 500 futures prices for 41 contracts over the 1982–1992 period are examined. There is no evidence that S&P 500 futures prices are biased estimates of the expected future spot price on expiration. Daily futures prices usually lie below the expected future spot price on expiration and usually rise over the contract period, but these price movements are not statistically significant. The surprising result of this study is the number of observations where backwardation appears not to hold. Furthermore, changes in the U.S. dollar exchange rates, the Tax Reform Act of 1986 and the switching of S&P 500 contracts quarterly expiration day had no significant effect on the behavior of S&P 500 futures prices.     

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.     

An investigation of the weekend effect during different market orientations

Studies of stock returns over short horizons indicated irregularities in returns, the weekend effect, and consequently the notion of market efficiency has been questioned. Despite extensive research on the weekend effect, little research has been conducted to define the prominence of the seasonal anomaly in Bear markets versus non-Bear markets. In the paper the weekend effect is investigated for daily returns in the Dow Jones Industrial Average (DJIA), the S&P 500, and the NASDAQ for Bear and non-Bear markets. Results support a weekend effect but only during non-Bear market orientations and a possible day-of-the-week effect during Bear and non-Bear markets.     

Information effects of announced stock index additions: evidence from S&P 400

The study examines the information content of press announcements of S&P 400 additions between 2002 and 2007. Prior research into stock index additions has explained the positive valuation effects of additions to S&P indices mostly in terms of the price pressure hypothesis and downward sloping curve hypothesis. The two hypotheses attribute the positive market reaction purely to index-fund buying rather than information effects of announcements. My empirical investigation further reinforces the credibility of the information hypothesis by showing that the market varies its response to added firms depending on the information released about them at the announcement. The analysis demonstrates that the mode of addition, exchange listing, reason for index change, and firm size can modulate valuation effects of stock index additions. The paper also strengthens the argument that announcements of additions to an S&P index contain new signals about the industries represented by the added firms. Positive and significant wealth effects are exclusively attributable to ??non-member?? rival firms. Overall, the results imply that the market discerns and rewards firms that come from outside the S&P universe (pure additions) and rival firms that are not part of a target index.     

Is there one safe-haven for various turbulences? The evidence from gold,Bitcoin and Ether

This paper aims to investigate the safe-haven properties of gold and two cryptocurrencies, Bitcoin and Ether. Safe havens are the financial assets that allow investors to protect their portfolios within the market turmoil. The research sample covers five years and includes several downturns on the financial markets, starting from the Chinese stock market turbulences in 2015/2016 and ending up with the recent pandemic outbreak in 2020. We find that only gold used to be a strong safe-haven against the stock market indices. Yet, this property evaporated during the crisis caused by the COVID pandemic. Occasionally, cryptocurrencies could have been considered weak safe-havens against the examined instruments. Ether acted more often as a weak safe-haven against DAX or S&P500, while Bitcoin played this role against FTSE250, STOXX600 and S&P500.     

Day-of-the-week effect on the VIX. A parsimonious representation

The study of significant deterministic seasonal patterns in financial asset returns is of high importance to academia and investors. This paper analyzes the presence of seasonal daily patterns in the VIX and S&P 500 returns series using a trigonometric specification. First, we show that, given the isomorphism between the trigonometrical and alternative seasonality representations (i.e., daily dummies), it is possible to test daily seasonal patterns by employing a trigonometrical representation based on a finite sum of weighted sines and cosines. We find a potential evolutive seasonal pattern in the daily VIX that is not in the daily S&P 500 log-returns series. In particular, we find an inverted Monday effect in the VIX level and changes in the VIX, and a U-shaped seasonal pattern in the changes in the VIX when we control for outliers. The trigonometrical representation is more robust to outliers than the one commonly used by literature, but it is not immune to them. Finally, we do not find a day-of-the-week effect in S&P 500 returns series, which suggests the presence of a deterministic seasonal pattern in the relation between VIX and S&P 500 returns.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.     

The week-of-the-month effect in stock returns: The evidence from the S&P Composite Index

Theweekend effect and theyearend effect are some of the seasonal anomalies in financial markets that have been widely discussed in the finance literature. In this paper, using weekly observations of the S&P Composite Index over the period from July 1962 through June 1990, plus several subperiods, the authors identify the presence of a thus far unknown seasonal anomaly in the form of aweek-of-the-month return pattern in the stock market. The results suggest the existence of a statistically different weekly return pattern for different weeks of a month. Specifically, the returns during the first week of a month tend to be significantly positive while the returns during the other weeks of a month are statistically indistinguishable from zero.     

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.     

Investigating the relationship between volatilities of cryptocurrencies and other financial assets

Decisions in Economics and Finance - This paper analyzes the relationships between volatilities of five cryptocurrencies, American indices (S&P500, Nasdaq, and VIX), oil, and gold. The...     

The day of the week effect on stock market volatility

This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings shown that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are observed on Friday and Wednesday, respectively. Further investigation of sub-periods reinforces our findings that the volatility pattern across the days of the week is statistically different.(JEL G10, G12, C22)     

The rise of passive investing and index-linked comovement

I introduce a general equilibrium model with active investors and indexers. Indexing causes market segmentation, and the degree of segmentation is a function of the relative wealth of indexers in the economy. Shocks to this relative wealth induce correlated shocks to discount rates of index stocks. The wealthier indexers are, the greater the resulting comovement is. I confirm empirically that S&P 500 stocks comove more with other index stocks and less with non-index stocks, and that changes in passive holdings of S&P 500 stocks predict changes in comovement of index stocks.     

Variance dynamics: Joint evidence from options and high-frequency returns

This paper analyzes the S&P 500 index return variance dynamics and the variance risk premium by combining information in variance swap rates constructed from options and quadratic variation estimators constructed from tick data on S&P 500 index futures. Estimation shows that the index return variance jumps. The jump arrival rate is not constant over time, but is proportional to the variance rate level. The variance jumps are not rare events but arrive frequently. Estimation also identifies a strongly negative variance risk premium, the absolute magnitude of which is proportional to the variance rate level.     

Hedging and pricing early-exercise options with complex fourier series expansion

We introduce a new numerical method called the complex Fourier series (CFS) method proposed by Chan (2017) to price options with an early-exercise feature—American, Bermudan and discretely monitored barrier options—under exponential Lévy asset dynamics. This new method allows us to quickly and accurately compute the values of early-exercise options and their Greeks. We also provide an error analysis to demonstrate that, in many cases, we can achieve an exponential convergence rate in the pricing method as long as we choose the correct truncated computational interval. Our numerical analysis indicates that the CFS method is computationally more comparable or favourable than the methods currently available. Finally, the superiority of the CFS method is illustrated with real financial data by considering Standard & Poor’s depositary receipts (SPDR) exchange-traded fund (ETF) on the S&P 500® index options, which are American options traded from November 2017 to February 2018 and from 30 January 2019 to 21 June 2019.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

The potential effect of US baby-boom retirees on stock returns

Empirical studies demonstrated that US baby boomers consumption and savings patterns have affected economic aggregates over the past decades, among them equity returns. Boomers’ retirement is expected to mitigate the demand for equities until 2050, but its impact varies with the specific population age structure along decades. This paper employs a dynamic asset pricing model with optimum consumption and portfolio rules to estimate aging effects on S&P500 returns between 1950 and 2050. Calibration for demographic and economic data between 1950 and 2005 yields model estimates that significantly explain the moving average of S&P500 returns. Further, taking into account the present value of expected demographic effects until 2050 suggests that the S&P500 was fairly priced at the heart of the financial crisis, on April 2009, but overpriced thereafter.     

Tax-free trading on calendar stock and bond market patterns

This study investigates the feasibility of using individual retirement accounts to exploit well-known calendar anomalies in the financial markets. We find no evidence of either a January effect or a weekend effect which may imply that investors have traded them out of existence. However, we find a significant turn-of-the-month effect in both stocks and bonds and show that investors may be able to enhance the performance of their retirement portfolios. We demonstrate that investors using a turn-of-the-month switching strategy would have outperformed a buy-and-hold strategy in stocks or bonds. Finally, our results have policy implications for investment companies.