Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method

This article uses the investor sentiment index to investigate the Granger causality between investor sentiment and stock returns for the US economy using a multi-scale method. To focus on the local analysis of different investor horizons, bivariate empirical mode decomposition is used to decompose time series of investor sentiment and stock returns at different timescales. We employ the linear and nonlinear integrated Granger causality method to examine the causal relationship of decomposed series on similar timescales. The results indicate both strong bilateral linear and nonlinear causality between longer-term investor sentiment and stock returns. However, there is no strong evidence for correlation of stock returns and investor sentiment on shorter timescales.     

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.     

Economic policy uncertainty,financial markets and probability of US recessions

We use probit recession forecasting models to assess the ability of economic policy uncertainty indexes developed by Baker et al. (2013) to predict future US recessions. The model specifications include policy indexes on their own, and in combination with financial variables, such as interest rate spreads, stock returns and stock market volatility. Both in-sample and out-of-sample analysis suggests that the policy uncertainty indexes are statistically and economically significant in forecasting recessions at the horizons beyond five quarters. The index based on newspaper reports emerges as the best predictor, outperforming the term spread at the longer forecast horizons.     

Spillover effects of the 2008 financial crisis on NIE stock markets

This article estimates dynamic conditional correlations of stock returns across countries by using DCC–GARCH model and analyse spillover effects of the 2008 financial crisis on the NIE’s stock markets. The results show that there is no regime shift in mean equation of the correlation coefficient during the financial crisis. It may imply there are no mean spillover effects of the US financial crisis on the NIE’s stock markets. However, there are volatility spillover effects of the financial crisis sparked in 2008 from the US to the NIE’s markets.     

Strength of co-movement between sector CDS indexes and relationship with major economic and financial variables over time and during investment horizons

The main purpose of this article is to analyse the co-movement in both time and frequency between financial sector CDS indexes and between these indexes and their main economic and financial control variables for the period 2004–2014. Empirically, we implement the wavelet-squared coherence methodology to analyse the co-movement through time, frequency and power. Our results unveil that the co-movement between the three financial sectors’ CDSs changes through time and investment horizons, stressing the importance of hedging portfolios in real time. Also, we uncover that the changes in co-movement to relatively higher frequencies coincide with the inception of the recent global financial crisis. This result is collaborated with the co-movement between each CDS index and other global risk factors, including crude oil prices, interest rates and equity market volatility. Finally, we compare the wavelet coherence results with those of the DCC-FIAPARCH model and find that the two different approaches provide quite similar conditional correlations over time. Our results are important for investors, debtors, creditors and other decision-makers which are interested in CDS spread co-movements at different frequencies or investment horizons. It would be useful for all market participants to resort to an appropriate frequency domain to have better understanding of the sector CDS interrelationship behaviour in this domain.     

Do different time horizons in the volatility of the US stock market significantly affect the China ETF market?

This article investigates the linear, nonlinear and time-varying Granger causality between different time horizons in the volatility of US stock market and the China Exchange-Traded Fund (ETF) market. We find evidence of linear causality from the US stock market to the China ETF market, with a bilateral nonlinear causal relationship in the longer term. Bootstrap rolling causality analysis indicates high rejection rates of a noncausal relationship running from the US stock market to the China EFT market. The causality linkage from the China ETF market to the US stock market was determined to be time-horizon-dependent, and the null hypothesis rejection rate of non-Granger causality increased in the longer term.     

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

Bull or bear markets: A wavelet dynamic correlation perspective

In this paper, we contribute to the literature on the international stock market co-movements and contagion, especially during the recent subprime crisis, by researching the interconnections between international stock markets in time-frequency domain.Our innovative approach consists on carrying out a wavelet decomposition of return time series before investigating the correlation dynamics across stock markets during the recent financial crisis. It thus enables us to show how the contagion dynamics between international stock market returns are changing across time scales corresponding to investors with heterogeneous time horizons. Moreover, our results reveal that the contagion dynamics depends on the bull or bear periods of stock markets, on stock markets maturity, and on regional aspects. Therefore, all these finding should be considered from an international portfolio diversification perspective.     

R&D, patents and stock return volatility

Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.     

Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression

The literature studying stock index options confirms severe biases and inefficiencies in using implied volatility as a forecast of future volatility. In this paper, we revisit the implied–realized volatility relationship with wavelet band least squares (WBLS) exploring the long memory of volatility, a possible cause of the bias. Using the S&P 500 and DAX monthly and bi-weekly option prices covering the recent financial crisis, we conclude that the implied–realized volatility relation is driven solely by the lower frequencies of the spectra representing long investment horizons. The findings enable improvement of future volatility forecasts as they support unbiasedness of implied volatility as a good proxy for future volatility in the long run.     

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.     

An empirical analysis of the US stock market and output growth volatility spillover effects on three Anglo-Saxon countries

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1–2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.     

Modelling the relationship between future energy intraday volatility and trading volume with wavelet

Although the energy and stock markets are both characterized by volatility and liquidity, and there has been substantial research to explore the relationship between volatility and trading volume (TV) in stock markets, few researchers have investigated this relationship in energy markets. Moreover, studies that have explored this association within energy markets did not describe its nature or impetus. To redress this oversight, we investigate this relationship using intraday data from the oil and gas markets – the most liquid energy markets in the world. In this way, the current article extends the previous studies through the use of a frequency approach to propose an original analysis of the relationship between volume and volatility. More specifically, we employ a continuous wavelet transform to identify the lead–lag phase between volatility and volume. This framework supplants usual time series modelling, as it uses a measure of coherence for different frequencies and time-scales to capture further changes and time variation in the volume–volatility relationship. Our results provide supportive evidence for the well-known positive relationship between realized volatility and TV, thereby supporting the mixture distribution hypothesis. In particular, our results show that volume causes volatility only during ‘turbulent times’, while volatility causes volume during ‘good times’. Furthermore, there is no relationship between volume and volatility in the long term, due to the absence of noise traders and liquidity traders in the long run. These findings are helpful for investors and policymakers as they contribute to better forecast the TV and price volatility during turbulent and calm periods and over several investment horizons.     

Volatility and dynamic conditional correlations of worldwide emerging and frontier markets

This study examines the relationship between time-varying correlations and conditional volatility among 32 worldwide emerging and frontier stock markets and the MSCI World stock market index from January 2000 to December 2012. Correlations are estimated in the standard and asymmetric dynamic conditional correlation model frameworks. The results can be summarized by three main findings: (1) asymmetry in volatility is not a common phenomenon in emerging and frontier markets; (2) asymmetry in correlations is found only with respect to the Hungarian stock market; and (3) the relationship between volatility and correlations is positive and significant in most countries. Thus, diversification benefits decrease during periods of higher volatility.     

Does Correlation Between Stock Returns Really Increase During Turbulent Periods?

Correlations betwen international equity markets are often claimed to increase during periods of high volatility. Therefore the benefits of international diversification are reduced when they are most needed, i.e. during turbulent periods. This paper investigates the relationship between international correlation and stock-market turbulence. We estimate a multivariate Markov-switching model, in which the correlation matrix varies across regimes. Subsequently, we test the null hypothesis that correlations are regime-independent. Using weekly stock returns for the S&P, the DAX and the FTSE over the period 1988–99, we find that international correlations significantly increased during turbulent periods.
(J.E.L.: C53, G15).     

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.     

A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach

In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.     

Predicting long-run risk factors of stock returns: Evidence from Australia

This study utilises the stock market data provided by the Australian Equity Database to analyse the long-run relationship between Australian stock returns and key macroeconomic variables over the period 1926–2017. To measure the diverse risk factors in the stock market, we examine the possible determinants in four main categories: real, financial, domestic and international. Our results reveal that historical stock returns are strongly connected to financial and international factors as compared to real and domestic factors. Both the 1973–1974 OPEC Oil Price Crisis and 2007–2008 Global Financial Crisis had dampening effects on stock returns. There is a positive association between the US and Australian stock markets in the long-run. These findings on stock market dynamics and their linkages with domestic and international macroeconomic policy changes in the long-run have important implications for traders and practitioners.     

Are exchange rates interdependent? Evidence using wavelet analysis

We examine the co-movement in daily returns of USD–INR, EUR–INR, GBP–INR, and JPY–INR currency pair futures contracts traded on the National Stock Exchange of India (NSE) using the wavelet cohesion approach. This study contributes to the literature by examining the scantly studied area of co-movement in exchange rates and using the wavelet approach, which allows us to analyse time–frequency-wise co-movement of the time series. The empirical results indicate that the currency futures markets are nearly perfectly integrated in the long run (monthly, quarterly and biannual scales) offering little potential gains from international portfolio diversification. The discrepancies between currency futures markets are small and almost fade away within 3–6 months. Moreover, international currency diversification might offer relatively higher potential gains at intraweek, weekly, and fortnightly time horizons owing to lower correlations among the currencies under consideration. Finally, our multiple-wavelet correlation and cross-correlation analysis shows that GBP acts as a potential leader/follower across scales. The results of our analysis indicate the dynamic pattern of co-movement among the major currency futures contracts, which provides several implications for portfolio managers and international investors participating in the Indian market.     

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.