Sentiment-prone investors and volatility dynamics between ... · Sentiment-prone investors and volatility dynamics between spot and futures markets 1. -Introduction The introduction

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Sentiment-prone investors and volatility dynamics between spot and

futures markets

Pilar Corredor, Elena Ferrer and Rafael Santamaria

Public University of Navarre

October, 2012

Abstract

This paper analyses the role of investor sentiment in the contemporaneous dynamics of

spot and futures markets and in volatility spillovers between them. They are a potential

effect of high investor sentiment leading to an increase in noisy trading and a drop in

arbitrage activity due to institutional investors’ attempts to limit their risk exposure.

This reduces correlation between the spot and futures markets. Consistent with the

impact of overconfidence and self-attribution bias, both of which are stronger in noise

traders, prices take longer to adjust news. In fact, shocks on volatility have less impact

during periods of high sentiment.

Keywords: Investor Sentiment, noise traders, spot-futures correlation, volatility

spillovers

JEL: G10, G13, G14

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Sentiment-prone investors and volatility dynamics between spot and futures

markets

1. -Introduction

The introduction of futures markets brought about a significant improvement in

the news transmission mechanism by allowing a more rapid adjustment of prices to new

information (Antoniou et al, 1998). In fact, by attracting additional traders, futures

markets can increase the possible channels of cross-market information flow (Cox,

1976). This may well determine the way information is incorporated into both futures

and spot market prices. The extent of the impact will depend upon the types of traders

active in the two markets (Antoniou et al, 1998). Noise traders, in particular, react to

information in a way that would not occur in a fully rational model because they trade

on noise as if it were information (Black, 1986). Similarly, Shiller (1984) claims that

some investors use a trend-chasing strategy based upon so-called “popular models” that

can be related to fundamentals, but involve an element of overreaction to news. In the

same vein, Shleifer and Summers (1990) show that uninformed investors are likely to

overreact to news. More recently, Kumar (2009) has shown empirically that individual

investors exhibit stronger behavioural biases when assets are more difficult to value and

when market-level uncertainty is higher. In contrast, several authors have shown that

institutional investors are sophisticated traders, emphasizing that their superior capacity

to acquire and process information gives them an advantage over other types of traders.

Their presence contributes to efficient asset pricing (see Bartov et al, 2000, Jiambalvo et

al, 2002, Collins et al, 2003 and Lewellen, 2011).

Thus, factors that might influence the investor mix, in either or both of these

markets, could also provoke changes in the dynamics of information transmission

between spot and futures markets. The literature has in fact shown that regulatory

reform or changes in the overall economic environment have had considerable impact

on these dynamics1. In this respect, investor sentiment can be a key variable. Noise

traders tend to be more active in bullish than in bearish markets (Baker and Stein, 2004)

and to have less capacity to react to news, since their overconfidence and self-attribution

biases increase in the presence of high market sentiment. Yu and Yuan (2011) also

1 See, for example, the effect of variation in the transaction costs of futures markets (Aragó et al, 2003) or the

changing nature of volatility contagion between financial markets (Saha and Chakrabati, 2011).

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argue that sentiment-driven investors participate and trade more aggressively in high-

sentiment periods, due to their reluctance to take short positions in low-sentiment

periods. More recently, Antoniou et al (2012) also state that noise traders are less active

during pessimistic periods than optimistic ones. Indeed investors usually take short

positions during bad times but such positions are more difficult to initiate for noise

traders than long positions (which they actively take during good times). In addition,

behavioural finance has shown that the arbitrage activity of informed traders is limited

when investor sentiment is high because of noise trading risk, that is, the risk that arises

from the unpredictability of noise traders’ behaviour. In periods such as these, informed

investors will stay out of the market (Shleifer and Vishny, 2003). Sophisticated traders,

aware of the overpricing that accompanies moments of high market sentiment, may also

significantly reduce their exposure at such times, thereby increasing the role of noise

traders in price setting. This difference in trading behaviour can affect the trading

volume and investor mix in both these markets. The extent of the effect on trading

volume in the spot market is unclear, because the reduced activity of institutional

investors may be offset, wholly or in part, by a significant influx of noise traders. In the

futures market, however, the predominance of institutional investors (Kavussanos et al

2008 and Bohl et al, 2011) means that trading volume is likely to drop significantly.

Change in the investor mix, however, will be more marked in the spot market, which

will see a more significant increase in the presence of noise traders, while the futures

market will continue to be dominated by institutional investors. Finally, the reduced

activity of institutional investors in both these markets may have a significant impact on

the level of arbitrage between them.

These circumstances raise the interest in examining the impact of the level of

investor sentiment on the contemporaneous dynamics of the spot and futures markets,

and on volatility spillovers between the two. The focus of the study is to analyse the

joint dynamics of several stock indexes and their respective futures contracts,

specifically, S&P500 index for the US market, and the CAC40, the DAX30, the

IBEX35, the FTSE100, and the Eurostoxx50 for the European market.

This study makes several contributions to the literature. Firstly, this, as far as we

know, is the first attempt to analyse the impact of investor sentiment, as a latent variable

affecting trading behaviour and the investor mix, on the contemporaneous dynamics of

the spot and futures markets and on volatility spillovers between them. In more detailed

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terms, this study attempts to investigate issues such as the significance of changes in the

contemporaneous correlation between the two markets during periods of high market

sentiment; the impact of own-market or other-market news on volatility; and the extent

of the asymmetric effect on volatility of good or bad news from either market. These

issues will be explored using bivariate GJR models to examine the time-varying

correlation between financial markets taking into account the investor sentiment level.

As well as for academics, this study holds interest for practitioners, because

knowledge and understanding of the variables influencing the degree of integration

between the two markets and the mechanisms by which news is incorporated into spot

and futures prices and transmitted across markets are important when considering

trading or hedge positions.

The rest of this article comprises four more sections. Section 2 discusses the

theoretical framework for the analysis and the formulation of the hypotheses to be

tested. Section 3 describes the data; section 4 presents the empirical model and the

results, and section 5 summarizes the main conclusions.

2. Theoretical Framework and Testable Hypotheses

If interest rates and dividend yields were non-stochastic, in a perfectly frictionless

world, price movements in the spot and futures markets would be contemporaneously

perfectly correlated and non-cross autocorrelated (Chan, 1992). Relationship between

price movements in the futures index and underlying spot markets should be

instantaneous, because they are both driven by the same market information and both

reflect the aggregate value of the underlying shares. Thus, in efficient market

conditions, it would make no difference to trade in one market or the other. Under

certain market conditions (liquidity, transaction costs, investor typology), however, one

market may assimilate new information more quickly than the other, thereby affecting

volatility spillovers.

Classical finance theory neglects the role of investor sentiment assuming investors

to be rational. Even if some investors are not rational, arbitrageurs can exploit their

irrational behaviour, thus causing prices to reflect future discount cash flows. The

behavioural finance literature suggests, however, that investor sentiment, defined as

investors’ opinions regarding future cash flows and investment risk (Chang et al, 2012),

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affects trading decisions. The influence of investors’ future expectations may result in

mispricing that will affect pricing models.

Early US empirical studies focused on the role of sentiment in predicting stock

returns (Kothari and Shanken, 1997; Neal and Wheatley, 1998; Shiller, 1981, 2000;

Baker and Wurgler, 2000; and Brown and Cliff, 2005) and on the effect of sentiment on

small-stock premiums (Lee et al, 1991; Swaminathan, 1996; Neal and Wheatley, 1998;

Brown and Cliff, 2004; and Lemmon and Portniaguina, 2006). Research on the

sentiment-return relationship in other financial markets includes, Wang (2001) on the

futures market; Han (2008), Lemmon and Ni (2011) on the options market; Ahn et al

(2002) on the currency market; and Burghardt et al (2008) and Schmitz et al (2009) on

the warrants market. A more modest amount of research has been conducted on the

effect of sentiment on volatility (Brown (1999) or Lee et al (2002)) finding them to be

inversely related.

To the best of our knowledge, however, there is no research examining the effect

of sentiment on the interaction between the spot and futures markets. The key question

is whether it is reasonable to expect the level of investor sentiment to affect the joint

dynamics of these two markets.

A possible argument to support such an idea is variation in the mix of traders who

are active when market sentiment is high. For example, noise traders tend to trade more

when markets are bullish than when they are bearish (Baker and Stein, 2004; Yu and

Yuan, 2011; and Antoniou et al, 2012). Barber and Odean (2008) argue that individual

traders are more prone to cognitive biases and Kumar (2009) finds empirical evidence

to support this, especially in assets that are hard to value and during periods of higher

market-level uncertainty. This noise trader risk pushes asset prices away from

equilibrium (Barberis et al, 1998 or De Long et al, 1990) and makes institutional traders

less inclined to engage in arbitrage trading. They may also prefer less exposure in the

equity market in the knowledge that this kind of assets, especially those that are hard to

value or present limited arbitrage opportunities, are over-priced and will tend towards

medium- to long-term reversion (see Baker and Wurgler, 2006). Institutional trading

will not affect spot and futures markets to the same degree, however. In fact, De Long et

al (1990) report a higher percentage of this type of trader in markets dealing in complex

assets, such as the futures market. Kavussanos et al (2008) argue that the futures market

is less prone to noise trader risk, and Bohl et al (2011) find futures markets to be

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dominated by institutional investors, who are assumed to be informed or rational. These

sophisticated traders, may reduce their arbitrage activity and their exposure in equity

markets, and thereby reduce trading volume to a greater extent in futures markets than

in spot markets. At the same time, however, the larger increase in noise trading that

occurs in spot markets will probably cause a more significant change in investor mix

than it does in futures markets.

During periods of market optimism, these changes in the investor mix, together

with less arbitrage activity due to lower participation of institutional investors, could

reduce the price correlation of these two markets within the no-arbitrage band2, by

lowering the pressure for price movements within that band. Indeed, any drop in

investor activity will, in itself, reduce the correlation between the two markets because

trading volume and correlation are directly related (see Stoll and Whaley, 1990, Chan,

1992). In the same vein, Bohl et al (2011) show that derivatives and spot markets will

correlate increasingly as institutional investors become more active. With this in mind,

we test the following hypothesis:

H1: Periods of high market sentiment reduce the correlation between spot and

futures markets.

According to the noise trading hypothesis, order flow is less informative when

investors are optimistic. Daniel et al (1998) assume that investors are overconfident

about their private information. If investors are also affected by self-attribution bias,

they will react asymmetrically to confirming versus disconfirming pieces of news and

become even more over confident after receiving confirming news. Self-attribution bias

leads investors to under react to the release of public information. The conservatism bias

hypothesis states that investors do not fully adjust their priors to the arrival of new

information (Barberis et al, 1998). During periods of high investor sentiment, these

biases will make investors in general, and noise traders in particular, less alert to

information coming from their own market, thus reducing the impact of volatility

shocks. By the same token, they will also pay less attention to information coming from

the other market. Furthermore, noise traders’ reaction to bad news that contradicts their

2This band is given by ( , ) where and ; where t

is the current date; T is the expiration date of the futures contract; S is the price of the underlying asset at time t; ρ= ln(1 + i), i being the riskless interest rate; is the time T value of dividends paid on the component stocks between

t and T. Finally, and are the present values of the sum of transaction costs involved in the arbitrage strategies.

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prior beliefs will have less impact on price formation. This means that, during periods

of high investor sentiment, the impact of news from either market will be less

asymmetric. Thus, the hypotheses to be tested are as follows:

H2: During periods of high market sentiment, the impact of own-market news on

volatility will be weaker and less asymmetric.

H3: During periods of high market sentiment, the impact of other-market news on

volatility will be weaker and less asymmetric.

3. Database

For the implementation of the analysis, this study uses daily closing prices and

trading volume of the spot and futures markets for a period running from February 2001

to December 2011. The data are taken from the US stock market and four European

markets: namely France, Germany, the United Kingdom and Spain. The EuroStoxx50 is

also included in order to represent the Euro zone. The reason for this choice of

European markets is that the UK, France and Germany are considered, along with the

US and Japan3, as extremely prominent economies on the global stage (Chang et al,

2012). According to the World Federation of Exchanges classification for 2011, the

London SE Group is the largest European stock exchange grouping in terms of

capitalization followed by NYSE Euronext (Europe), the Deutsche Börse and the BME

Spanish Exchanges. The homogeneity of their financial development levels does not

rule out some variation in shareholder structure, corporate governance (see La Porta et

al, 1998) and cultural dimensions (see Hofstede, 2001) between the selected European

countries, however. The market sample also includes representatives of both the Anglo

Saxon and Continental financial systems. This combination of similarity and diversity

strengthens the relevance of our findings by allowing us to determine whether

institutional factors, unrelated to financial development, play a significant role in the

impact of investor sentiment on cross-market correlation and volatility spillovers.

The closing prices data, taken from the Datastream database (Thomson Financial),

refer to the S&P500 index for the US stock market and to the five key European stock

market indexes, namely, the CAC40 for France, the DAX30 for Germany, the FTSE-

100 for the UK, the IBEX35 for Spain, and the EuroStoxx50 index. The closing prices

3 Although it would have been interesting to include Japan, the necessary data were unavailable.

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of the respective futures contracts were drawn from the Bloomberg database. The

returns for the spot index (St,i) and the futures index (Ft,,i) computed each day t for each

index i are defined as Rs,t,i=Ln(St,i/St-1,i) and Rf,t,i=Ln(Ft,i/Ft-1,i).

The trading volume data on these markets (spot and futures) are drawn from the

Datastream and the Bloomberg database, respectively. The variable used in the analysis

is abnormal trading volume4, calculated for each index as:

(1)

where is the ordinary trading volume of each index i (S&P500, CAC40, DAX30,

IBEX35, FTSE100 and EuroStoxx50) on day t, for each market m (s=spot and

f=futures).

Another of the variables considered in this analysis is investor sentiment. Previous

studies have used a variety of sentiment indicators, and there is no consensus as to the

best means of representing this unobservable variable. Indicators used in previous

research include: investor survey findings (Jansen and Nahuis, 2003; Brown and Cliff,

2005; Lemmon and Portniaguina, 2006; Schmeling, 2009; and Stambaugh, et al, 2012),

investor mood (Kamstra et al, 2003), retail investor trades (Barber et al, 2006;

Greenwood and Nagel, 2009; and Kumar and Lee, 2006), mutual fund flows (Brown et

al, 2003; Frazzini and Lamont, 2008 and Ben-Rephael et al, 2012)), the dividend

premium (Baker and Wurgler, 2004a and b), the closed-end fund discount (Zweig,

1973; Lee et al, 1991; Swaminathan, 1996; Neal and Wheatley, 1998 and Doukas and

Milonas, 2004), option implied volatility (Whaley, 2000), the number of IPOs and

average first-day IPO returns (Ritter, 2003 and Ljungqvist et al, 2006), turnover or

trading volume (Jones, 2002; Sheinkman and Xiong, 2003; and Baker and Stein, 2004),

the share of equity issues in total equity and debt issues (Baker and Wurgler, 2000),

insider trading (Seyhun, 1998) or composite sentiment indexes (Brown and Cliff, 2004;

Baker and Wurgler, 2006, 2007; Ho and Hung, 2009; Baker et al, 2012; and Chang et

al, 2012) among others.

4 The selected measure is similar to that used in papers such as Llorente et al (2002), Dennis and Strikland (2002) or

Covrig and Ng (2004). Given that our interest is in trading volume in futures markets, we use trading volume instead

of turnover.

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For the purposes of our proposed analysis, we require a short-term measure of

sentiment. The majority of the above references describe long-term timing measures

used to test their predictive power on future stock returns. The majority, moreover, are

market-based measures whose construction requires complementary techniques that

may bias the final results. The measure selected will also need to be a frequently-

published and updated indicator, the mode of construction and date of which are known

and understood by traders.

To obtain a measure fulfilling all these requirements, we select two surveys that

directly measure the sentiment of market participants. For the U.S. market, we follow

DeBondt (1993), Fisher and Statman (2000) and Brown and Cliff (2004) whose

sentiment measure is based on the American Association of Individual Investor (AAII)

survey data. Originally started, in 1987, as a weekly survey of randomly selected AAII

members, this survey asks participants to predict the likely direction of the stock market

during the next six months and measures the percentages of individual investors that

respond “up”, “down”, and “the same”. The AAII then labels these responses as a

bullish, bearish or neutral on the stock market, respectively.

For a measure of investor sentiment in the European indexes analysed, we use

survey data from SentixEuroStoxx 505. Since this survey began in February 2001, it has

surveyed Sentix investors weekly, and currently has over 3100 registered participants,

more than 77% of whom are individual investors. Participants are asked whether they

are bullish, bearish, neutral, or have no opinion with regard to the future trend of the

EuroStoxx50 stock index over the following one- and six-month periods6.

We use the two surveys measures (Sentix and AAII) as the spread between the

percentages of bullish and bearish investors. Every week is sorted according to the level

of investor sentiment as either a bullish (above-the-median) sentiment week or a bearish

(below-the-median) sentiment week7. Both the AAII and the Sentix survey meet the

necessary criteria with respect to frequency and trader awareness and both indexes

capture market sentiment well because they are calculated from a direct survey on the

5 In the absence of any sentiment measure of this kind for the UK, we consider this a valid approximation. 6 The results shown are those obtained using the Sentix 6 month-ESX 50 Index to be consistent with the AAII. For

robustness checks, we later repeat the analysis using the Sentix 1 month-ESX 50 Index. 7 Replies to the weekly survey are accepted up to Friday of the week in question, but the results are not published

until the following Monday before trading opens. For the purposes of our study, we take the moment of

optimism/pessimism to be Friday when the survey replies are being given. Repetition of the analysis using a dummy

variable beginning the day after close of survey produced similar results. The results are available upon request.

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expected future state of the market. The survey results are also comparable because of

the homogeneity similarity of the question they put to the participants. The investor

sentiment data were also drawn from Datastream.

4. Methodology and Results

4.1 Preliminary results: Trading volume analysis

The argument to support the idea of possible variation in the contemporaneous

dynamics between the futures and spot markets is based, not only on the activity of

noise traders or institutional investors, but also on the change in the trader mix that

occurs during periods of high market sentiment. Firstly, as noted by Baker and Stein

(2004), Yu and Yuan (2011) and Antoniou et al (2012) there is an increase in the

number of noise traders in bullish markets. Given that the highest concentration of noise

traders is found in less complex assets, their activities will be more noticeable in spot

markets. Furthermore, seeing the market to be overpriced, institutional traders are likely

to limit their activity until prices to revert to their fundamentals. This drop in trading by

institutional investors will occur in both markets, but more significantly in the futures

markets where they dominate (Bohl et al, 2011) and where their activity will not be

offset by an increase in that of noise traders.

To obtain empirical evidence to support these arguments, we begin by testing for a

variation in the abnormal trading volume in both the spot and the futures market at

times of high investor sentiment. In addition, because the Engle’s test results reveal the

presence of ARCH effects, the variance is modelled by means of a GARCH(1,1)

specification, which takes the following form:

(2)

where follows a N(0, );

where is the abnormal daily trading volume for market m (spot or futures) and

index i. As independent variables, we include a dummy (SENT), which takes a value of

1 if investor sentiment is above the median level and 0 otherwise and 4 day-of-the-week

dummies ( which take a value of 1 if it is Monday, Tuesday,

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Thursday or Friday, respectively and 0 otherwise. The equation is estimated using an

AR(5).

As shown in table I, Panel A, during periods of high investor sentiment, overall

trading volume in spot markets is not significantly affected. This may be because the

drop in trading by rational investors is offset by the increase in noise trading at such

times (Baker and Stein, 2004; Yu and Yuan, 2011; and Antoniou et al, 2012). These

markets do, nevertheless, see a major change in the investor mix due to the large influx

of noise traders.

The results for the futures market, shown in table I, Panel B, show a negative

effect which is clearly significant in all indexes considered8. In an optimistic market,

trading volume decreases as institutional investors, who are the principal agents in these

markets, decide to close their positions and temporarily cease trading in order to avoid

exposure to the arbitrage risk created by irrational investors, and await the subsequent

reversion of prices to fundamentals. Given the strong presence of institutional investors

in these markets, their reduced activity will have more impact on trading volume.

However, since it is not accompanied in futures markets by an increase in noise trading,

it may not have same the impact on the investor mix as it does in spot markets.

This observed difference in the trading volume and, probably, also in the investor

mix in both markets strengthens the rationale for testing their capacity to trigger

alterations in price dynamics between spot and futures markets.

4.2 Empirical model

In order to model the effects of investor sentiment on the correlation between spot

and futures index returns and between the linkage in the second moments of the two

markets, we propose a bivariate Glosten-Jagannathan-Runkle (1993) (GJR) process.

The model (henceforth, Model 1) for each index i (i = S&P500, CAC40, DAX30,

IBEX35, FTSE100 and EuroStoxx50) takes the following form9:

8 The exception is the Spanish index. Nevertheless, this negative effect is only significant at a 14% significance level. 9 Although not reported in the tables, some diagnostic tests of the residuals were performed. No indications of model

misspecification were observed. The autocorrelations and partial correlations for the squared standardized residuals

for stock index and index futures returns are all insignificantly different from zero.

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; (3)

where is the error correction term imposing the long-term equilibrium

on index i in the two markets; ( ) is the innovation in the spot (futures) market

at day t for index i; =var( /Ωt-1,i) is the conditional variance of the spot market

and =var( /Ωt-1,i) is the conditional variance of the futures market, where Ωt,i is

the information set available at t for index i.

As shown, in the above variance equation the cross-market innovations have been

added to a GJR specification. It is interesting to note that the innovation ( ) is

used instead of ( ). The reason for this choice is the intense cross-correlation

between and which could lead to misleading estimates. The innovation

( ) is the information from the spot (futures) market which is transmitted to the

futures (spot) market and is not included in ( ). Thus, ( ) is orthogonal

to ( )10

. and have been incorporated into the and equations

respectively, to analyse the volatility spillover between the two markets on each index

i. ( ) is a dummy variable which is 1 if <0 ( <0) y 0 otherwise;

( ) is a dummy variable which is 1 if <0 ( <0) and 0 otherwise, and is the

returns correlation between the two markets. In the specification of the covariance, the

constant correlation implied in the cost-of-carry model is11assumed.

In order to test hypothesis 1, we introduce the dummy variable (SENT) into the

model to allow this correlation to change as a function of investor sentiment. As already

stated, this variable takes a value of 1 when the sentiment index is above the median

10 ( ) se calculan como los residuos de la siguiente regresión ( )=k0,i+k1,i ( )+ ( )

11 The covariance specification is similar to that used in Koutmos and Tucker (1996) which is based on the

specification in Bollerslev, (1990).

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level and 0 otherwise. As sentiment proxies, we use the AAII for the US index and the

Sentix index for the European indexes under analysis. The coefficient of these

indexes indicates whether there is a change in the contemporaneous correlation between

the futures and spot markets. Consistent parameter estimates are obtained using the

BHHH algorithm.

Furthermore, these equations allow these innovations ( and ) to influence

the conditional volatility asymmetrically, as do their own innovations ( and ).

Thus, and measure the magnitude effect, whereas and measure the sign

effect. The intuitive interpretation of these coefficients is very similar to that of their

own innovations, but they are relative to cross-market volatility spillovers.

4.3 Impact of investor sentiment on correlation between spot and futures markets

The estimates from Model 1 are shown in table II. With respect to the means, it is

worth noting the significantly negative sign of the coefficient on the lagged return in all

spot and futures markets analysed. Meanwhile, the error correction term parameter is

significant in all of the markets. The parameter data for the conditional variance

equation show that volatility is affected by own-market shocks12 ( and ). Both the

persistence coefficients ( and ) and the asymmetry coefficients ( and ), are

positive and significant with values that fall within the usual ranges, thus confirming

that negative shocks increase volatility within a given market.

The model also captures other parameters affected by global volatility spillovers

and negative shocks. We find that both the parameters involved in global information

transfer from the other market ( and ) are, as expected, positive and significant

overall13. In the case of the parameters involved in the asymmetric impact of negative

shocks ( and ) the results are less clear because only some of them are positive and

significant14.

12 In the case of DAX30 and S&P500 these parameters are not significantly different from zero. 13 Given the availability of the sentiment indicator affecting the trend of the German market DAX30, we performed a

robustness test by repeating the analysis using this measure. Since the findings were practically the same as for the

Sentix Eurostoxx50, we decided to adopt the latter for its consistency with other European markets. The results are

available from the authors upon request. 14Note that the figures of these parameters ( , and ) are not comparable to ( , and ) because they

are obtained using instead of

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With respect to hypothesis H1, given that the model permits the correlation to vary

as a function of market sentiment, we need to examine the parameter that is associated

with this change ( ). The results reveal that, when investor sentiment is high,

correlation decreases in all the markets analysed. This decrease is significant at the 1%

level in all cases. This finding appears to support the hypothesis that, when sentiment is

high, noise traders become more active, while institutional investors decrease their

activity. These changes do not have the same impact in both types of market, however.

The significant influx of noise traders to spot markets widens the gap in terms of

investor mix between these and futures markets, thereby reducing the contemporaneous

price correlation between the two. Moreover, the decrease in the proportion of

institutional investors in both markets reduces arbitrage activity and allows prices to

deviate further from their fundamentals. This obviously results in lower correlation

between the two markets, thus confirming Hypothesis 1.

4.4 Effects of investor sentiment on the volatility of its own market

In order to analyse the effect of sentiment on the information, we adjust Model 1

to include the dummy variable (SENT) described earlier, but now also associated to any

information coming from the market under analysis (Model 2) and to negative news

coming from its own market (Model 3). We also include the SENT variable as it affects

information coming from the other market (Model 4) and the asymmetric response of

volatility to news coming from the other market (Model 5). The unrestricted model onto

which we impose different restrictions to create the rest of the above-mentioned models

is presented below:

(4)

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Given that the aim of this section is to analyse the effect of sentiment on

information coming from its own market, the models to be analysed are, specifically,

Model 2 and Model 3, which impose the following restrictions:

Model 2: = 0; and =0


The estimates from Model 2 are given in table III. The variables it shares with

Model 1 behave, overall, as described earlier. Observation of the coefficients associated

to the influence of sentiment on information (α6 for the spot market and β6 for the

futures market), shows that they are in 5 of 6 cases negative and significant. The

negative sign tells us that, during periods of high investor sentiment, information

reaching the market has a lower impact on prices, consistent with over-confidence and

self-attribution among uninformed investors, and thus less impact on volatility. These

arguments are confirmed by the results for both types of markets.

The data on the effect of sentiment on the asymmetric impact on volatility of own-

market bad news are given in table IV. The coefficients on the variable used to capture

the effect of sentiment on volatility asymmetry (α8 and β8) are clearly significant. In

fact, all six indexes analysed show a significant decrease in volatility asymmetry in the

presence of negative shocks. Once again, we observe this pattern in both types of

markets.

This set of results confirms hypothesis H2 and suggests that when investor

sentiment is high, news plays a somewhat less important role in price setting driven by

the biases of noise traders, whose percentage presence at such times is higher than when

investor sentiment is low. As expected, this less prominent role of information is

particularly noticeable in the asymmetric effect on volatility, probably as a consequence

of noise traders’ failure to react to bad news that contradicts their prior beliefs.

4.5 Effects of investor sentiment on volatility spillovers

The next step is to test the effect of sentiment on volatility spillovers. For this,

starting from the unrestricted model described in the previous section, we devise two

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new models, Model 4 which analyses the impact on information coming from the other

market and Model 5 which examines the asymmetric effect of that information on

volatility. In more specific terms, the said models impose the following restrictions on

the general model:



The estimates from Model 4 are shown in table V. Coefficients α7 and β7 capture

the impact of sentiment on information coming from the other market. It should be

noted that the cross-market shocks considered, including those affected by sentiment,

are orthogonal to the information originating in their own markets. As can be seen,

when sentiment is high, we find a generalized decrease in cross-market volatility

spillovers. In fact, both coefficients in all 6 indexes analysed are highly statistically

significant. These results are consistent with those obtained for the effect on own-

market volatility. It is also important to note that news can originate not only from the

release of exogenous information, but also from that of endogenous information

conveyed through trading. If, during periods of high sentiment, there is a drop in

trading, there will also be a drop in trading news and, presumably, in the amount of

trading news being transmitted to the other market.

Table VI gives the coefficient estimates obtained from the estimation of Model 5.

Coefficients α9 and β9 associated with the effect of sentiment on the transmission of

negative shocks in the spot and futures markets, respectively, are nearly all negatively

signed, although significant only in the case of futures on FTSE100. This means that, in

this case, the level of investor sentiment does not affect the asymmetric reaction of

volatility to negative shocks coming from the other market.

These results only partially confirm H3, since although we observe, as predicted,

that, during periods of high investor sentiment, volatility in one market is less affected

by news coming from the other, the decrease in volatility asymmetry following bad

news from the other market lacks statistical significance.

17

Finally, the findings vary very little across the cases analysed, allowing us to

conclude that they are robust to possible country-specific institutional or cultural

factors, at least, that is, in the developed market context in which this paper is situated.

4.6 Robustness

Our purpose in this next section is to analyse the robustness of the results reported

above, by examining two issues: a) their sensitivity to the selected dummy for

extremely bullish sentiment and b) their sensitivity to the time horizon for the sentiment

measure15.

The first test is to adjust the sentiment dummy in order to check the robustness of

the results to its mode of construction. This variable was initially defined to identify a

period in which market sentiment had risen above the median level. In this new

analysis, the variable is adjusted to capture periods of more extreme levels of sentiment.

Taking the top 25% to be high sentiment periods, the variable takes a value of 1 in these

periods and 0 otherwise. Table VII summarizes the coefficient estimates for this

analysis. The results show that cross-market correlation drops significantly during

periods of high investor sentiment, thus confirming H1. They also reveal that volatility

is less affected by news from either market. At the same time, volatility asymmetry

during such periods is found to be less affected by own-market news, while the effect of

other-market news remains unchanged. This confirms H2 and partially confirms H3.

This consistency with the results of the initial analysis confirms their robustness to the

construction of the sentiment variables.

Finally, as already stated, the Sentix survey issues two EuroStoxx forecasts: a one-

month forecast and a six-month forecast. The results given in the tables shown so far are

based on the six-month forecast. However, since the AAII issues only a six-month

forecast, we repeated the analysis using the Sentix EuroStoxx one-month forecast. The

resulting coefficient estimates, given in table VIII, are similar to those reported above,

in that high market sentiment triggers a significant decrease in correlation, the reaction

of volatility to own-market news (models 2 and 3) and volatility spillovers (models 4

and 5). This clear drop in correlation allows us to confirm H1. The results for H2 and

H3, however, differ slightly from those reported in the earlier analyses. Although the

impact of news on volatility decreases, as predicted in both these hypotheses, there is a

15 The robustness test will be available only for the Sentix measure, since AAII does not consider horizons of less

than 6 months.

18

difference in the asymmetric impact of bad news on volatility. While there is barely any

significant change in the effect of “own-market” bad news, a large number of the

markets analysed show a significant reduction in the impact of “other-market” negative

news. This enables confirmation of H3 and partial confirmation of H2.

Overall, the results obtained, both in terms of correlation and the information

effect show no major variations attributable to the choice of time horizon for estimation

of the sentiment variable or to its mode of construction, and can therefore be considered

highly robust. The only difference worth noting is that which can be observed in the

asymmetric impact of news on volatility. When we use the six-month sentiment index,

asymmetric volatility decreases only as a reaction to shocks in its own market, whereas,

when we use the one-month sentiment index, it is found to decrease in response to news

from the other market. These findings confirm the impact of investor sentiment on

volatility asymmetry, although the type of information that produces the effect appears

to depend on the time horizon.

5. Conclusions

This study establishes a link between the published research on volatility

dynamics and investor sentiment. Through its potential influence on investor behaviour,

high sentiment can have a significant impact on volatility dynamics. Noise traders, in

particular, will show an increased presence in the market, while sophisticated investors,

faced with higher arbitraging risk driven by the irrational behaviour of noise traders, and

conscious of over-pricing, will reduce their activity until prices revert to their

fundamental values. Due to the characteristic differences between spot and futures

markets, these changes affect their trading volume and investor mix in different ways

and may therefore significantly alter the contemporaneous dynamics between them. To

explore this issue, we analyse spot and futures markets on stock market indexes in

different countries: the S&P500 for the US, and a representative set of European

indexes (CAC40, DAX30, FTSE100, IBEX35 and Eurostoxx50).

Consistent with expectations, during periods of high investor sentiment in all of the

countries considered, trading volume drops notably in the futures markets due to the

significant reduction in the activity of institutional investors. The effect in spot markets

is not significant because the reduction in the activity of institutional investors is offset

by an increase in the participation of noise traders. This variation in the investor mix

19

can have a major impact on the joint volatility dynamics between the two markets. In

fact, the results show that the level of cross-market correlation decreases significantly in

all the countries analysed. This is due not only to the imbalance created by the activity

of noise traders themselves but also to institutional investors slackening their arbitrage

activity, unless prices deviate considerably from the no-arbitrage bands. Consistent with

the impact of overconfidence and self-attribution bias, which is stronger in individual

investors and during periods of higher market-level uncertainty, prices take longer to

adjust news. In fact, shocks on volatility in either market have significantly less impact

during periods of high sentiment. To a lesser degree, the same can be said of the

asymmetric impact of negative shocks on volatility, although it is worth noting that the

results are sensitive to the time horizon employed in the estimation of investor

sentiment. This issue, while exceeding the scope of the present paper, might be an

interesting avenue of future research.

Finally, the results obtained are very similar across all the markets analysed,

suggesting that cultural and institutional frameworks do not play a crucial role in this

issue, or at least not in the developed market context in which this paper is situated.

These findings reveal that the joint dynamics of the spot and futures markets is

strongly influenced by the diversity and mix of investors at any given moment and also

by variables affecting trading behaviour, one being investor sentiment. The latter’s

usefulness in describing cross-market conditional correlation and the reaction of stock

prices to news justifies examination of its role in the dynamics of these two markets.

Acknowledgements: This paper has received financial support from the Spanish

Ministry of Science and Innovation (ECO2009-12819) and Spanish Ministry of

Economy and Competitiveness (ECO2012-35946-C02-01)

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Table I. Effect of sentiment on abnormal volume in the spot and futures markets. 2001-2011

Panel A: Spot Market

CAC40 DAX30 EUROSTOXX50 IBEX35 FTSE100 S&P500

α 0.097*** 0.056*** 0.066*** 0.064*** 0.057*** 0.055***

β -0.012 0.017 -0.001 0.002 -0.003 -0.004

γ1 -0.286*** -0.279*** -0.236*** -0.213*** -0.239*** -0.142***

γ2 -0.012 -0.026 -0.015 -0.019 0.027** -0.007

γ3 -0.009 -0.014 -0.011 -0.036** -0.025** -0.026***

γ4 -0.050*** 0.020 -0.020 -0.022 -0.070*** -0.078***

α0 0.026*** 0.007*** 0.036*** 0.005*** 0.015*** 0.010***

α1 0.249*** 0.196*** 0.172*** 0.048*** 0.204*** 0.256***

α2 0.354*** 0.749*** 0.123 0.869*** 0.477*** 0.380***

Panel B: Futures Market

CAC40 DAX30 EUROSTOXX50 IBEX35 FTSE100 S&P500

α 0.155*** 0.099*** 0.133*** 0.098*** 0.026 0.044***

β -0.031* -0.019* -0.029** -0.017 -0.029*** -0.025**

γ1 -0.178*** -0.203*** -0.271*** -0.099*** -0.141*** -0.161***

γ2 0.079** 0.019 0.041 0.105*** 0.056** 0.066***

γ3 -0.033 -0.012 -0.043 -0.133*** -0.007 -0.053***

γ4 -0.366*** -0.093*** -0.229*** -0.349*** -0.099*** -0.087***

α0 0.074*** 0.098*** 0.042*** 0.061* 0.017*** 0.003***

α1 0.084*** 0.423*** 0.062*** 0.042** 0.358*** 0.056***

α2 0.522*** 0.081 0.600*** 0.517** 0.601*** 0.914***

The sentiment effect (coefficient β) on abnormal trading volume in the spot market (Panel A) and the futures market (Panel B). AV is the abnormal volume of index i and market m (spot or futures). SENT is the dummy variable that

takes a value of 1 if sentiment is above the median level and 0 otherwise. DM, DT, DTh y DF are dummy variables that

take a value of 1 on Mondays, Tuesdays, Thursdays and Fridays, respectively. The estimation includes an AR(5) process.***, ** and * indicate 1%, 5%, and 10% levels of significance, respectively.

26

Table II. Impact of investor sentiment on the correlation between spot market and futures market (Model 1).

2001-2011

CAC40 DAX30 EUROSTOXX50 FTSE100 IBEX35 S&P500

A0 -0.227 0.018 0.720** -0.129 0.130 1.459***

A1 -0.183*** -0.164*** -0.214*** -0.220*** -0.137*** -0.190***

A2 0.224*** 0.006 -0.102** 0.024 -0.077 -0.165***

A3 0.999*** 0.999*** 0.991*** 0.994*** 0.999*** 0.987***

B0 -0.506* -0.420** -0.119 -0.848** -0.126 -0.551

B1 -0.186*** -0.151*** -0.231*** -0.207*** -0.134*** -0.179***

B2 0.468*** 0.366*** 0.019 0.155*** 0.150** 0.061

α0 0.125*** 0.026*** 0.049*** 0.029*** 0.067*** 0.015***

α1 0.019*** 0.006 0.012* 0.016** 0.037*** 0.002

α2 0.782*** 0.879*** 0.839*** 0.891*** 0.831*** 0.898***

α3 0.071** 0.122*** 0.156*** 0.098*** 0.144*** 0.135***

α4 1.297*** 0.679*** 0.550*** 0.398*** 0.807*** 0.262***

α5 0.134*** 0.654*** 0.211** -0.060 -0.055 0.332***

β0 0.131*** 0.027*** 0.050*** 0.027*** 0.071*** 0.016***

β1 0.028*** 0.002 0.017** 0.019*** 0.038*** -0.006

β2 0.772*** 0.879*** 0.828*** 0.890*** 0.823*** 0.907***

β3 0.193*** 0.118*** 0.106*** 0.002 -0.084*** 0.144***

β4 1.624*** 0.945*** 0.960*** 0.326*** 1.107*** 0.335***

β5 -0.137 0.010 0.044* 0.091*** 0.238*** -0.116**

γ0 0.989*** 0.983*** 0.969*** 0.980*** 0.990*** 0.981***

γ1 -0.003*** -0.009*** -0.004*** -0.004*** -0.004*** -0.007***

Model 1

where is the error correction term imposing the long-term equilibrium on index i in the two

markets; ( ) is the innovation in the spot (futures) market at day t for index i; =var( /Ωt-1,i) is the

conditional variance of the spot market and =var( /Ωt-1,i) is the conditional variance of the futures market,

where Ωt,i is the information set available at t for index i. The innovation ( ) is the information from the spot

(futures) market which is transmitted to the futures (spot) market and is not included in ( ). The dummy

variable SENT has a value of 1 if sentiment is above the median level and 0 otherwise. We use the Sentix 6 month-

ESX 50 Index as the sentiment proxy for the European indices and AAII for the US index. The dummy

variable ( ) is equal to 1 if ( ) <0. The dummy variable ( ) is equal to 1 if ( ) <0.

***, ** and *indicate 1%, 5%, and 10% levels of significance, respectively.

27

Table III. Effect of investor sentiment on spot (futures) volatility (Model 2). 2001-2011


A0 -0.222 0.043 0.771** -0.078 0.105 1.239**

A1 -0.184*** -0.163*** -0.212*** -0.224*** -0.140*** -0.191***

A2 0.219*** -0.010 -0.106*** 0.017 -0.045 -0.141***

A3 0.999*** 0.998*** 0.991*** 0.994*** 0.999*** 0.988***

B0 -0.503* -0.393** -0.081 -0.754** -0.167 -0.731*

B1 -0.187*** -0.150*** -0.230*** -0.210*** -0.139*** -0.179***

B2 0.462*** 0.348*** 0.014 0.144*** 0.177** 0.084*

α0 0.133*** 0.030*** 0.055*** 0.034*** 0.062*** 0.013***

α1 0.033*** 0.016*** 0.015** 0.025*** 0.059*** 0.001

α2 0.772*** 0.872*** 0.839*** 0.880*** 0.836*** 0.900***

α3 0.065** 0.119*** 0.158*** 0.010*** 0.125*** 0.135***

α4 1.344*** 0.738*** 0.536*** 0.459*** 0.780*** 0.281***

α5 0.135*** 0.689*** 0.224** 0.031 -0.032 0.317***

α6 -0.016** -0.016*** -0.021*** -0.017*** -0.027*** 0.011

β0 0.138*** 0.031*** 0.054*** 0.033*** 0.064*** 0.015***

β1 0.041*** 0.012* 0.022*** 0.030*** 0.059*** -0.004

β2 0.762*** 0.872*** 0.827*** 0.878*** 0.826*** 0.908***

β3 0.189*** 0.116*** 0.112*** 0.005 -0.091*** 0.134***

β4 1.712*** 1.017*** 0.952*** 0.426*** 1.128*** 0.362***

β5 -0.175 0.009 0.038 0.087*** 0.228*** -0.145**

β6 -0.014* -0.018*** -0.019*** -0.021*** -0.025*** -0.006

γ0 0.990*** 0.983*** 0.969*** 0.981*** 0.989*** 0.981***

γ1 -0.003*** -0.010*** -0.006*** -0.005*** -0.003*** -0.007***

Model 2: = = = 0 and = = = 0




where Ωt-1,i is the information set available at t-1 for index i. The innovation ( ) is the information from the

spot (futures) market which is transmitted to the futures (spot) market and is not included in ( ). The dummy

variable SENT has a value of 1 if sentiment is above the median level and 0 otherwise. We use the Sentix 6 month-

ESX 50 Index as the sentiment proxy for the European indices and AAII for the US index. The dummy

variable ( ) is equal to 1 if ( ) <0. The dummy variable ( ) is equal to 1 if ( ) <0.

***, ** and *indicate 1%, 5%, and 10% levels of significance, respectively.

28

Table IV. Effect of investor sentiment on asymmetries in spot (futures) volatility (Model 3). 2001-2011


A0 -0.198 0.102 0.757** -0.110 0.166 1.375***

A1 -0.183*** -0.162*** -0.211*** -0.221*** -0.140*** -0.191***

A2 0.189** -0.050 -0.103** 0.023 -0.088 -0.153***

A3 0.999*** 0.998*** 0.991*** 0.994*** 0.999*** 0.987***

B0 -0.490* -0.370** -0.099 -0.815** -0.116 -0.647

B1 -0.187*** -0.148*** -0.229*** -0.208*** -0.139*** -0.180***

B2 0.428*** 0.321*** 0.016 0.152*** 0.132* 0.071

α0 0.142*** 0.030*** 0.055*** 0.034*** 0.071*** 0.014***

α1 0.025*** 0.010* 0.015** 0.017*** 0.043*** 0.005

α2 0.767*** 0.872*** 0.829*** 0.876*** 0.824*** 0.889***

α3 0.085*** 0.133*** 0.168*** 0.120*** 0.185*** 0.132***

α4 1.412*** 0.746*** 0.605*** 0.452*** 0.917*** 0.298***

α5 0.135*** 0.712*** 0.230** 0.010 -0.037 0.226**

α8 -0.047*** -0.040*** -0.023** -0.044*** -0.097*** -0.036*

β0 0.147*** 0.031*** 0.057*** 0.033*** 0.073*** 0.016***

β1 0.035*** 0.004 0.020** 0.021*** 0.044*** -0.005

β2 0.756*** 0.871*** 0.816*** 0.875*** 0.815*** 0.908***

β3 0.208*** 0.135*** 0.124*** 0.032* -0.037 0.141***

β4 1.794*** 1.041*** 1.064*** 0.400*** 1.283*** 0.240***

β5 -0.173 0.007 0.040 0.085*** 0.231*** 0.018

β8 -0.043*** -0.044*** -0.027* -0.050*** -0.090*** -0.045**

γ0 0.990*** 0.984*** 0.969*** 0.980*** 0.990*** 0.981***

γ1 -0.003*** -0.010*** -0.005*** -0.006*** -0.004*** -0.007***

Model 3: = = = 0 and = = = 0

where is the error correction term imposing the long-term equilibrium on index i in the two markets;

( ) is the innovation in the spot (futures) market at day t for index i; =var( /Ωt-1,i) is the conditional

variance of the spot market and =var( /Ωt-1,i) is the conditional variance of the futures market, where Ωt-1,i is the

information set available at t-1 for index i. is the conditional covariance between spot and futures markets. The

innovation ( ) is the information from the spot (futures) market which is transmitted to the futures (spot) market

and is not included in ( ). The dummy variable SENT has a value of 1 if sentiment is above the median level and

0 otherwise. We use the Sentix 6 month-ESX 50 Index as the sentiment proxy for the European indices and AAII for the

US index. The dummy variable ( ) is equal to 1 if ( ) <0. The dummy variable ( ) is equal to 1 if

( ) <0. ***, ** and *indicate 1%, 5%, and 10% levels of significance, respectively.

29

Table V. Effect of investor sentiment on volatility spillovers (Model 4). 2001-2011


A0 -0.211 0.067 0.799** -0.127 0.108 1.612***

A1 -0.184*** -0.162*** -0.212*** -0.219*** -0.137*** -0.195***

A2 0.213*** 0.029 -0.107*** 0.024 -0.051 -0.180***

A3 0.999*** 0.998*** 0.991*** 0.993*** 0.999*** 0.987***

B0 -0.486 -0.412** -0.069 -0.891** -0.171 -0.451

B1 -0.188*** -0.148*** -0.229*** -0.206*** -0.136*** -0.183***

B2 0.454*** 0.336*** 0.012 0.157*** 0.173** 0.051

α0 0.128*** 0.031*** 0.054*** 0.027*** 0.062*** 0.020***

α1 0.019*** 0.008 0.014** 0.011** 0.038*** 0.008

α2 0.779*** 0.868*** 0.830*** 0.895*** 0.832*** 0.889***

α3 0.075*** 0.121*** 0.156*** 0.097*** 0.143*** 0.168***

α4 1.521*** 1.212*** 0.952*** 0.552*** 1.221*** 0.307***

α5 0.130*** 0.792*** 0.197** -0.031 -0.034 0.367***

α7 -0.339* -0.718*** -0.579*** -0.319*** -0.619*** -0.094***

β0 0.133*** 0.032*** 0.057*** 0.027*** 0.066*** 0.020***

β1 0.028*** 0.001 0.019** 0.016*** 0.041*** -0.005

β2 0.769*** 0.869*** 0.816*** 0.892*** 0.822*** 0.902***

β3 0.194*** 0.140*** 0.122*** -0.007 -0.087*** 0.174***

β4 1.809*** 1.478*** 1.570*** 0.550*** 1.616*** 0.390***

β5 -0.091 -0.010 0.027 0.097*** 0.241*** -0.164**

β7 -0.291* -0.662*** -0.889*** -0.408*** -0.694*** -0.074***

γ0 0.990*** 0.984*** 0.972*** 0.980*** 0.989*** 0.983***

γ1 -0.003*** -0.012*** -0.012*** -0.007*** -0.003*** -0.009***

Model 4: = = = 0 and = = = 0




where Ωt-1,i is the information set available at t-1 for index i. is the conditional covariance between spot and

futures markets. The innovation ( ) is the information from the spot (futures) market which is transmitted to the

futures (spot) market and is not included in ( ). The dummy variable SENT has a value of 1 if sentiment is

above the median level and 0 otherwise. We use the Sentix 6 month-ESX 50 Index as the sentiment proxy for the

European indices and AAII for the US index. The dummy variable ( ) is equal to 1 if ( ) <0. The

dummy variable ( ) is equal to 1 if ( ) <0. ***, ** and *indicate 1%, 5%, and 10% levels of

significance, respectively.

30

Table VI. Effect of investor sentiment on asymmetries in spot (futures) volatility spillovers (Model 5). 2001-

2011


A0 -0.187 0.013 0.730** -0.191 0.084 1.228**

A1 -0.186*** -0.164*** -0.214*** -0.222*** -0.139*** -0.191***

A2 0.196*** 0.011 -0.103** 0.036 -0.030 -0.139**

A3 0.999*** 0.998*** 0.991*** 0.994*** 0.999*** 0.987***

B0 -0.450 -0.420** -0.111 -0.912** -0.184 -0.770*

B1 -0.189*** -0.151*** -0.231*** -0.208*** -0.138*** -0.180***

B2 0.431*** 0.374*** 0.018 0.170*** 0.193*** 0.086*

α0 0.115*** 0.026*** 0.052*** 0.023*** 0.057*** 0.014***

α1 0.013** 0.007 0.013** 0.009 0.037*** 0.005

α2 0.796*** 0.879*** 0.833*** 0.908*** 0.841*** 0.897***

α3 0.082*** 0.121*** 0.159*** 0.095*** 0.138*** 0.132***

α4 1.135*** 0.688*** 0.585*** 0.297*** 0.740*** 0.276***

α5 0.120*** 0.676*** 0.240** -0.046 0.000 0.360***

α9 -0.007 -0.002 -0.034 0.054 -0.075 -0.087

β0 0.118*** 0.027*** 0.053*** 0.023*** 0.059*** 0.015***

β1 0.022*** 0.002 0.018** 0.012** 0.040*** -0.005

β2 0.789*** 0.879*** 0.822*** 0.906*** 0.831*** 0.908***

β3 0.190*** 0.119*** 0.110*** -0.002 -0.075*** 0.141***

β4 1.285*** 0.968*** 1.030*** 0.250*** 1.079*** 0.326***

β5 0.079 0.009 0.045* 0.093*** 0.220*** -0.121*

β9 0.012 -0.005 -0.006 -0.009** 0.006 0.027

γ0 0.990*** 0.983*** 0.969*** 0.979*** 0.989*** 0.982***

γ1 -0.004*** -0.009*** -0.004*** -0.005*** -0.002*** -0.007***

Model 5: = = = 0 and = = = 0





futures markets. The innovation ( ) is the information from the spot (futures) market which is transmitted to

the futures (spot) market and is not included in ( ). The dummy variable SENT has a value of 1 if sentiment

is above the median level and 0 otherwise. We use the Sentix 6 month-ESX 50 Index as the sentiment proxy for the




31

Table VII. Effect of extremely bullish sentiment on correlation between markets and on volatility

spillovers, Six-month Sentix index and AAII. 2001-2011


Coeff. Model 2

α6 -0.085*** -0.038*** -0.045*** -0.049*** -0.036*** -0.012**

β6 -0.095*** -0.040*** -0.047*** -0.058*** -0.031*** -0.011**

γ1 -0.003*** -0.013*** -0.013*** -0.011*** -0.001** -0.004***

Model 3

α8 0.028 -0.066*** -0.036*** -0.058*** -0.033** -0.039***

β8 0.029 -0.074*** -0.046*** -0.073*** -0.026* -0.029***

γ1 -0.002*** -0.012*** -0.009*** -0.007*** -0.001 -0.004***

Model 4

α7 0.014 -0.606*** -0.528*** -0.342*** -0.653*** -0.144

β7 -0.055 -0.636*** -0.920*** -0.376*** -0.868*** -0.140

γ1 -0.002*** -0.015*** -0.017*** -0.008*** -0.001** -0.004***

Model 5

α9 -0.003 0.021 -0.041 -0.169* -0.025 -0.027

β9 -0.179 -0.0111 -0.016 -0.025*** 0.008 0.002

γ1 -0.002*** -0.010*** -0.008*** -0.007*** 0.000 -0.003***

Unrestricted Model:

Model 2: = = = 0 and = = = 0

Model 3: = = = 0 and = = = 0

Model 4: = = = 0 and = = = 0

Model 5: = = = 0 and = = = 0





futures markets. The innovation ( ) is the information from the spot (futures) market which is transmitted to the

futures (spot) market and is not included in ( ). The dummy variable SENT has a value of 1 for sentiment

scores within the top 25% and 0 otherwise. We use the Sentix 6 month-ESX 50 Index as the sentiment proxy for the




32

Table VIII. Effect of sentiment on correlation between markets and on volatility spillovers, One-month Sentix

index. 2001-2011

CAC40 DAX30 EUROSTOXX50 FTSE100 IBEX35

Coeff. Model 2

α6 0.051*** -0.007 -0.039*** -0.009* -0.010**

β6 0.054*** -0.018*** -0.054*** -0.011** -0.007*

γ1 -0.005*** -0.007*** -0.013*** -0.012*** -0.003***

Model 3

α8 -0.009 -0.005 -0.076*** 0.011 0.010

β8 -0.005 -0.015 -0.093*** 0.009 0.006

γ1 -0.005*** -0.007*** -0.012*** -0.011*** -0.004***

Model 4

α7 -0.005 -0.647*** -0.090 -0.362*** -1.268***

β7 -0.066 -0.758*** -0.273** -0.364*** -1.388***

γ1 -0.005*** -0.009*** -0.012*** -0.013*** -0.007***

Model 5

α9 -0.006 0.303** 0.084 -0.256*** -0.218**

β9 -0.314*** -0.004 -0.018 -0.011** -0.007

γ1 -0.005*** -0.007*** -0.010*** -0.011*** -0.005***

Unrestricted Model:

Model 2: = = = 0 and = = = 0

Model 3: = = = 0 and = = = 0

Model 4: = = = 0 and = = = 0

Model 5: = = = 0 and = = = 0



conditional variance of the spot market and =var( /Ωt-1,i) is the conditional variance of the futures

market, where Ωt-1,i is the information set available at t-1 for index i. is the conditional covariance between

spot and futures markets. The innovation ( ) is the information from the spot (futures) market which is

transmitted to the futures (spot) market and is not included in ( ). The dummy variable SENT has a

value of 1 if sentiment is above the median level and 0 otherwise. We use the Sentix 1 month-ESX 50 Index as

the sentiment proxy for the European indices. The dummy variable ( ) is equal to 1 if ( ) <0.

The dummy variable ( ) is equal to 1 if ( ) <0. ***, ** and *indicate 1%, 5%, and 10% levels of


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Sentiment-prone investors and volatility dynamics between ... · Sentiment-prone investors and volatility dynamics between spot and futures markets 1. -Introduction The introduction