Volatility Feedback Effect Research Articles

Speculative dynamics and price behavior in the Shanghai Stock Exchange

This article examines the extent to which the trading behavior of heterogeneous investors manifests in stock price changes of asset portfolios which constitute the Shanghai Stock Exchange. There are three major findings that materialize. Firstly, reliable statistical evidence of a negative relation between the conditional first and second moments of the return distributions of stock prices lends support to the volatility feedback effect. Secondly, ‘feedback’, or momentum-type investors, are not present in this market as is often detected from the daily price changes of other industrialized markets. Finally, trade volume as a proxy for ‘information-driven’ trading suggests that such investors play a statistically significant role in stock price movements. Parameter estimates from this latter group of investors imply that a rise in stock prices from a high volume trading day is more likely than a rise resulting from a low volume trading day.

Analysis of volatility feedback and leverage effects on the ISE30 index using high frequency data

In this study, we employ the techniques of Malliavin calculus to analyze the volatility feedback and leverage effects for a better understanding of financial market dynamics. We estimate both effects for a general semimartingale model applying Fourier analysis developed in Malliavin and Mancino (2002) [10]. We further investigate their joint behaviour using 5 min data of the ISE30 index. On the basis of these estimations, we look for the evidence that volatility feedback effect rate can be employed in the stability analysis of financial markets.

Stock price dynamics and option valuations under volatility feedback effect

According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price–dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price, dividends, and volatility in continuous time. Most importantly, our model predicts the negative effect of an increase in squared return volatility on the value of deep-in-the-money call options and, furthermore, attempts to explain the volatility puzzle. We theoretically demonstrate a mechanism by which the market price of diffusion return risk, or an equity risk-premium, affects option prices and empirically illustrate how to identify that mechanism using forward-looking information on option contracts. Our theoretical and empirical results support the relevance of the volatility feedback effect. Overall, the results indicate that the prevailing practice of ignoring the time-varying dividend yield in option pricing can lead to oversimplification of the stock market dynamics.

Volatility and return spillovers in Canadian and U.S. industry ETFs

Exchange-traded funds (ETFs) are now an important source of information dissemination in Canadian and U.S. equity markets, and we provide new evidence regarding price discovery and volatility spillovers in these securities. We find that price discovery flows consistently from the U.S. to Canada for these securities, while volatility spillovers are largely bi-directional. Information is impounded more rapidly into returns through trading in U.S securities, and the combination of negative U.S. return spillovers and asymmetric volatility creates bi-directional volatility feedback effects. The results are relevant to market participants and Canadian market regulators since Canadian circuit-breakers are tied to U.S. market conditions.

The Fine-Structure of Volatility Feedback

We attempt to unveil the fine structure of volatility feedback effects in the context of general quadratic autoregressive (QARCH) models, which assume that today’s volatility can be expressed as a general quadratic form of the past daily returns. The standard ARCH or GARCH framework is recovered when the quadratic kernel is diagonal. The calibration of these models on US stock returns reveals several unexpected features. The off-diagonal (non ARCH) coefficients of the quadratic kernel are found to behighly significant both InSample and Out-of-Sample, but all these coefficients turn outto be one order of magnitude smaller than the diagonal elements. This confirms that daily returns play a special role in the volatility feedback mechanism, as postulated by ARCH models. The feedback kernel exhibits a surprisingly complex structure, incompatible with models proposed so far in the literature. Its spectral properties suggest the existence of volatility-neutral patterns of past returns. The diagonal part of the quadratic kernel is found to decay as a power-law of the lag, in line with the long-memory of volatility. Finally, QARCH models suggest some violations of Time Reversal Symmetry in financial time series, which are indeed observed empirically, although of much smaller amplitude than predicted. We speculate that a faithful volatility model should include both ARCH feedback effects and a stochastic component.

Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality

This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establishes local power properties, and motivates the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the size and power properties of the test. We illustrate the practical relevance of our test by considering two empirical applications where we examine the Granger noncausality between financial variables. In a first application and contrary to the general findings in the literature, we provide evidence on two alternative mechanisms of nonlinear interaction between returns and volatilities: nonlinear leverage and volatility feedback effects. This can help better understand the well known asymmetric volatility phenomenon. In a second application, we investigate the Granger causality between stock index returns and trading volume. We find convincing evidence of linear and nonlinear feedback effects from stock returns to volume, but a weak evidence of nonlinear feedback effect from volume to stock returns.

Asymmetric Volatility in the Gold Market

The volatility of equity returns generally exhibits an asymmetric reaction to positive and negative shocks. Economic explanations for this phenomenon are leverage and a volatility feedback effect. This article studies the volatility of gold and demonstrates that there is an inverted asymmetric reaction to positive and negative shocks—positive shocks increase volatility more than negative shocks. The author argues that this effect is related to the safe-haven property of gold. Investors interpret positive gold price changes as a signal for future adverse conditions and uncertainty in other asset markets. This introduces uncertainty in the gold market and thus higher volatility. The empirical results hold for gold bullion and gold coins denominated in different currencies and for different return frequencies, sample periods, and distributional assumptions. Finally, the author shows that the inverted volatility effect of gold can lower the aggregate risk of a portfolio for specific correlation levels.

Stock Price Dynamics, Dividends, and Option Prices with Volatility Feedback

In this paper, we provide a new framework for stock and options valuations by characterizing the joint dynamics of stock price, dividends, and volatility with the volatility feedback effect in continuous-time. Within our framework, we consider the properties of stock price and its dynamics with volatility feedback casting light on the excess volatility and the correlation between volatility and stock price. Most importantly, we discover a channel through which the market price of return risk, or equity risk-premium, affects option prices. One implication is that an increase in squared return volatility can be unfavorable to the holder of in-the-money call options. Finally, we illustrate the use of our framework to identifying the risk-return relation using forward-looking option data.

Measuring High-Frequency Causality Between Returns, Realized Volatility, and Implied Volatility

We provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage ef fect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility and find that implied volatilities are essential for assessing the volatility feedback effect. We also study the impact of news on returns and volatility. We introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and find that a positive variance risk premium has more impact on returns than a negative variance risk premium. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Volatility Forecasting: Downside Risk, Jumps and Leverage Effect

We provide new empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Leverage and volatility feedback effects of the S&P 500 price and volatility dynamics are examined using recently developed methodologies to detect jumps and to disentangle their size from continuous return and continuous volatility. Granted that jumps in both return and volatility are important components for generating the two effects, we find jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improve the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative then positive jump semivariance. The model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated.

Firm size and volatility analysis in the Spanish stock market

Using Spanish stock market data, this paper examines volatility spillovers between large and small firms and their impact on expected returns. By using a conditional capital asset pricing model (CAPM) with an asymmetric multivariate GARCH-M covariance structure, it is shown that there exist bidirectional volatility spillovers between both types of companies, especially after bad news. After estimating the model, a positive and significant price of risk is obtained. This result is consistent with the volatility feedback effect, one of the most popular explanations of the asymmetric volatility phenomenon, and explains why risk premiums are much more sensitive to negative return shocks coming from the whole market or other related markets.

The Role of People’s Expectation in the Recent US Housing Boom and Bust

This paper investigates how an important driver of the recent housing boom and bust, people’s expectation, influences housing asset returns. Specifically, it extends the volatility feedback model to study the relationship between housing volatility and asset returns during 19632007. The analysis considers two alternative breakpoints, 1984Q1 and 1999Q1, in order to distinguish the permanent structural break from temporary Markov-switching volatility. The novelty of this study lies in its insightful investigations into the recent U.S. housing boom and bust in the post-1999 period in four dimensions. First, the significantly negative volatility feedback effect in the housing market suggests a positive relationship between housing volatility and expected asset returns, and highly supports the important role of people’s expectations in the recent housing boom and bust. Second, the high-volatility regimes of the housing market delivered by this study indicate a strong association between housing cycles and business cycles, as well as a remarkable uncertainty in the U.S. housing market after the recession 2001. Third, the violated fundamental which refers to the broken negative relationship between housing volatility and realized asset returns during 2001–2004 implies the possible presence of a housing bubble during this period. Finally, volatility feedback anticipates the recent bubble-like housing market dynamics because high volatility during 2002–2003 implies low realized returns in the early housing-boom stage (2002–2003), as well as high expected returns in the second stage of the housing boom (2004–2005).

Firm level return–volatility analysis using dynamic panels

This paper examines “leverage” and volatility feedback effects at the firm level by considering both market and firm level effects, using 242 individual firm stock data in the US market. We adopt a panel vector autoregressive framework which allows us to control simultaneously for common business cycle effects, unobserved cross correlation effects in return and volatility via industry effects, and heterogeneity across firms. Our results suggest that volatility feedback effects at the firm level are present due to both market and firm effects, though the market volatility feedback effect is stronger than the corresponding firm level effect. We also find that the leverage effect at the firm level is persistent, significant and negative, while the effect of market return on firm volatility is persistent, significant and positive. The presence of these effects is further explored through the responses of the model's variables to market-wide return and volatility shocks.

The Role of Realised Volatility in the Athens Stock Exchange

Using a newly developed dataset of daily, value-weighted market returns we construct and analyze the monthly realized volatility of the Athens Stock Exchange (A.S.E.) from 1985 to 2003. Our analysis focuses on the distributional and time series properties of the realized volatility series and on assessing the connection between realized volatility and returns through a multi-factor asset pricing model. In particular, we find strong evidence on the existence of a volatility feedback effect and a leverage effect, and on the existence of asymmetries between lagged returns and volatility. Furthermore, we examine the cross-sectional distribution of unconditional loadings on the realized risk factor(s) for different sets of characteristics-sorted common stock portfolios. We find that realized risk is a significantly priced factor in A.S.E. and its high explanatory power for the cross-section of portfolio average returns is independent of any return variation related to the market (CAPM) or size and book-to-market (Fama-French) factors. We discuss our findings in the context of the recent literature on realized volatility and feedback effects, as well as the literature on the pricing power of realized risk.

Relation between VIX’s Return and Volatility: A Behavioral Explanation

I investigate the relation between returns and volatility at daily to 1-min intervals for VIX ETNs (like ETFs) and futures. As VIX is the implied volatility index and also known as “fear gauge”, this study is on relation between returns of volatility and volatilities of volatility. I find that, contrary to equity markets, volatility’s return and volatility exhibit positive relation at daily basis. However, negative relation appears at the finer-grained 5-min and 1-min intervals, with the latter stronger. This paper discovers a stronger behavioral case than Hibbert, Daigler and Dupoyet (2008) and shows that behavioral explanation is the only feasible one, among the three explanations of leverage, volatility feedback and behavioral effect. First, unlike equity, VIX ETNs and futures do not involve financial leverage. Second, the chance of any event having shocks to volatility of volatility is slim. Moreover, I observe negative relation only at very short intervals (less than 15-min) and a monotonic relation change from daily positive to intraday negative, while leverage and volatility feedback models suggest longer delayed effects and no such pattern of relation change. My findings imply that there are further behavioral effects when market deals with fear, which itself has already been a behavioral outcome.

Measuring High-Frequency Causality between Returns, Realized Volatility and Implied Volatility

In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On observing that a central difference between these alternative explanations lies in the direction of causality, we consider vector autoregressive models of returns and realized volatility and we measure these effects along with the time lags involved through short-run and long-run causality measures proposed in Dufour and Taamouti (2010), as opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures contracts, the associated realized volatilities (before and after filtering jumps through the bispectrum) and implied volatilities. Using only returns and realized volatility, we find a strong dynamic leverage effect over the first three days. The volatility feedback effect appears to be negligible at all horizons. By contrast, when implied volatility is considered, a volatility feedback becomes apparent, whereas the leverage effect is almost the same. These results can be explained by the fact that volatility feedback effect works through implied volatility which contains important information on future volatility, through its nonlinear relation with option prices which are themselves forward-looking. In addition, we study the dynamic impact of news on returns and volatility. First, to detect possible dynamic asymmetry, we separate good from bad return news and find a much stronger impact of bad return news (as opposed to good return news) on volatility. Second, we introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and we find that a positive variance risk premium (an anticipated increase in variance) has more impact on returns than a negative variance risk premium.

The Relation between Expected Returns and Volatility in the Brazilian Stock Market

This paper applies the Markov switching regression model of stock returns with volatility feedback of Turner, Startz and Nelson (1989), suitably extended to incorporate endogenous regime shifts, as in Kim, Piger and Startz (2008), to examine the intertemporal relationship between the risk premium and volatility in the Brazilian stock market over the period 1995-2011. The results suggest that there is a positive relationship between the risk premium and the expected volatility once the volatility feedback effect is taken into account. Unanticipated increases in volatility, in contrast, have a negative impact on the risk premium. This negative impact increasesby 50% when I account for endogeneity of regime shifts.

Asymmetric herding as a source of asymmetric return volatility

As a considerable source of asymmetry in return volatility, this paper introduces asymmetric herding and extends the continuous beliefs system to account for its asymmetry and derive the asymmetric herding parameters that are easily estimated by using a maximum likelihood method based on the GARCH-type econometric model. This paper presents new empirical evidence for asymmetry in the exchange rates volatility of major currencies against the US dollar, which have bilateral nature. Interestingly, the asymmetry of Japanese yen is the opposite of that of others and the global financial crisis highlights the opposite asymmetry. Some of traditional hypotheses, such as the leverage effect and the volatility feedback effect, do not adequately explain these findings; however, a significant asymmetric herding effect is observed and appears to be time-varying. Further, the clear link between asymmetric herding and volatility strongly supports the hypothesis of the asymmetric herding effect.

Asymmetric Volatility in the Gold Market

The volatility of equity returns generally exhibits an asymmetric reaction to positive and negative shocks. Economic explanations for this phenomenon are leverage and a volatility feedback effect. This paper studies the volatility of gold and demonstrates that there is an inverted asymmetric reaction to positive and negative shocks, i.e. positive shocks increase the volatility by more than negative shocks. The paper argues that this effect is related to the safe haven property of gold. Investors interpret positive gold price changes as a signal for future adverse conditions and uncertainty in other asset markets. This introduces uncertainty in the gold market and thus higher volatility. The empirical results hold for gold bullion and gold coins denominated in different currencies, for different return frequencies, sample periods and distributional assumptions. Finally, we show that the inverted volatility effect of gold can lower the aggregate risk of a portfolio for specific correlation levels.

A High‐Frequency Investigation of the Interaction between Volatility and DAX Returns

Abstract One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead‐lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse‐response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5‐minute intervals, we find that the relationship is return‐driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns.