Volatility Feedback Effect Research Articles

The impact of external shocks on volatility persistence and market efficiency of the foreign exchange rate regime: evidence from Malawi

This study examines the nexus between external shocks and micro-transmission aspects of volatility persistence and market efficiency for the Malawian Foreign exchange market. Garch-type models are employed on monthly data stretching back to June 2011 through to October 2021. Results of the study show the presence of two structural breaks for the volatility series and one structural break for the return series that fall within the rolling window for external shock associated with the Russia-Ukrainian war. The negative nature of the shock leads to a significant additive impact on volatility persistence, asymmetry, and threshold at different levels of significance in the three models of Garch, E-Garch and T-Garch respectively. Relaxing the models confirms the presence of domestic influence. However, the external influence remains significant especially for the negative shock. As such, the leverage and volatility feedback effects of external shocks are corroborated. Augmenting the approach with a FRUH analysis reveals that the foreign exchange market does not follow a random walk as it departs from the 1:1 relationship between the spot and forward rate thus being weak-form inefficient.

Volatility feedback and dealership position: Evidence from the CDS Index, Corporate Bonds, and Government Bonds

Our study comprehensively confirms the existence of the volatility feedback effect for corporate bonds and the Credit Default Swap (CDS) indexes. We also find that dealers’ net protection-buying position of the CDS indexes relates to high credit spreads for the CDS indexes and the representative corporate bonds, and this relationship is associated with dealers’ compensation. This study is a pioneer in investigating CDS index swaptions and finding an implied volatility smirk and that hedging activities of dealers’ net positions of the underlying CDS index relate to the swaption premiums.

Why do firms with no leverage still have leverage and volatility feedback effects?

The leverage effect hypothesis of Black (1976) and Christie (1982) posits that time-series variation in debt causes an inverse relation between stock return volatility and stock returns. Hasanhodzic and Lo (2019) test this hypothesis in a novel sample of firms with no debt and yet they still find an inverse relation, motivating them to espouse volatility feedback as an alternative. Under standard assumptions governing the risk-return relation from the asset pricing literature, I explain why the stock returns of all-equity-financed firms will still have leverage effects on par with those of debt-financed firms and why the absence of debt at the firm level has no bearing on the leverage and volatility feedback hypotheses.

Implementing Intraday Model-Free Implied Volatility for Individual Equities to Analyze the Return–Volatility Relationship

We implement the VIX methodology on intraday data of a large set of individual equity options. We thereby consider approaches based on monthly option contracts, weekly option contracts, and a cubic spline interpolation approach. Relying on 1 min, 10 min, and 60 min model-free implied volatility measures, we empirically examine the individual equity return–volatility relationship on the intraday level using quantile regressions. The results confirm a negative contemporaneous link between stock returns and volatility, which is more pronounced in the tails of the distributions. Our findings hint at behavioral biases causing the asymmetric return–volatility link rather than the leverage and volatility-feedback effects.

State-dependent volatility feedback effect in the ICAPM

Considering that risk-averse investors revise their expectations in response to changes in both expected and unexpected volatility, we hypothesize and demonstrate that an unexpectedly high (low) volatility shock causes an increase (decrease) in risk premium. Using a novel approach to endogeneity issues, we utilize a state dependent, ICAPM to measure volatility effects on risk-return relationships. Our empirical results show that the volatility feedback effect strengthens (attenuates) the positive risk-return relation under bad (good) news. Furthermore, the volatility feedback effect under the combined conditions of bad news and a high unexpected volatility causes an extremely heightened level of the risk-return tradeoff.

Volatility feedback effect and risk-return tradeoff

Using the two alternative measures of the ex-ante unexpected volatility shock, we show that the volatility feedback effect plays an important role in the intertemporal risk-return tradeoff. The empirical results indicate that the volatility feedback effect reinforces the positive risk-return relation conditional on bad market news but attenuates the relation under good market news. The results provide strong evidence that an extremely heightened risk-return tradeoff caused by a high level of volatility feedback effect might lead to a market crash, even with no macroeconomic uncertainties in the markets. Also, the results show that the asymmetric volatility feedback effect is attributable to the negative correlation between the concurrent volatility and a price change. The state-dependent volatility feedback effect is observed for different market conditions such as high and low market sentiments and business cycles.

The value-growth premium in a time-varying risk return framework

In recent years, growth firms outperformed value firms, which led investors to doubt value strategy in their investments. Reworking the Euler equation shows that time-varying risk is responsible for the reverting risk-return relation from negativity contemporaneously to positivity with a time lag. The negative relation is due to the volatility feedback effect, while the positive relation is the risk premium effect. Because of the negative volatility feedback effect, growth firms outperform value firms, particularly in heightened stock market volatility, such as in the first year of the Covid-19 pandemic. However, we find value firms earn a higher average return than growth firms over long horizons.

The volatility index and volatility risk premium in China

We introduce the China Volatility Index (CNVIX), a model-free volatility index for the Chinese equity market based on ETF options. To construct the CNVIX, we extend the Chicago Board Options Exchange (CBOE) methodology in the emerging Chinese options market. We examine the leverage effect and volatility feedback effect between the CNVIX and the underlying asset, as well as the CNVIX’s return forecastability. Our findings indicate a significant negative asymmetric leverage effect, insignificant volatility feedback effect in the CNVIX, and a positive mean volatility risk premium (VRP), which can forecast the underlying asset’s returns over various horizons.

Portfolio optimization under distribution uncertainty with a feature fusion of conditional skewness GARCH model

In this study, the currency exchange portfolios are optimized by applying an amalgamation of Coskewness and GARCH (1,1) models to ensure conditional skewness based on the GARCH (1,1) model, a new analysis technique is anticipated for the Conditional Skewness in auto-regressive uncertain instability. Applications of Mean–Variance through financial statistics confirm the validation of the projected evaluation process. The conditional skewness dependence on conditional variance is due to the proposed model’s effectiveness with the volatility feedback effect. For the Japanese stocks and the currency exchange rates, conditional risk-minimizing hedge strategies are estimated from July 2000 to June 2020. The empirical results illustrate that the best vibrant hedging strategies can moderately detain the currency rate variations seriously and decrease the currency rate risk by increasing and the portfolios’ risk-adjusted returns. Conditional skewness in the daily post-war Japanese stock returns confirms the importance of the model’s capability to optimize portfolios. All stock and currency exchange return portfolios are positively correlated with each other. Ultimately, our study results present that the exchange rate volatility can be modeled effectively by the GARCH model in a straightforward and uncomplicated method.

A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model

The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. The estimation of the model is difficult, since the unobservable surplus demand is a time-varying stochastic variable in the return equation, and the market liquidity arises both in the mean term and in the variance term of the return equation in the MM model. A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. Since the simulation smoother algorithm makes use of the band diagonal structure and positive definition of Hessian matrix of the logarithmic density, it can quickly draw the market liquidity. In addition, we discuss the MM model with Student-t heavy tail distribution that can be utilized to address the presence of outliers in typical financial time series. Using the presented modeling method to make analysis of daily income of the S&P 500 index through the point forecast and the density forecast, we find clear support for time-varying volatility, volatility feedback effect, market microstructure theory, and Student-t heavy tails in the financial time series. Through this method, one can use the estimated market liquidity and surplus demand which is much smoother than the strong stochastic return process to assist the transaction decision making in the financial market.

Investor attention and oil market volatility: Does economic policy uncertainty matter?

This paper aims to investigate the impact of investor attention on the oil market volatility by using the Google search volume index as the measurement of investor attention. In particular, we decompose aggregate volatility into good volatility and bad volatility to gain a deeper insight into this issue. Additionally, we discuss whether the relationship between investor attention and oil market volatility changes with the adjustment of economic policy uncertainty in G7 and BRIC. The empirical results show that changes in investor attention mainly affect bad volatility rather than good volatility, and this impact is positive, symmetric, and transient. Interestingly, this impact can also lead to changes in future oil prices through the volatility feedback effect channel. Moreover, we find that the economic uncertainty policy can enhance the positive relationship between investor attention and bad volatility, but this relationship seems to be only sensitive to economic policy uncertainty in the US and Canada

Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods

The Stochastic Volatility in Mean (SVM) model of Koopman and Uspensky (2002) is revisited. An empirical study of five Latin American indexes in order to see the impact of the volatility in the mean of the returns is performed. Markov Chain Monte Carlo (MCMC) Hamiltonian dynamics is used to estimate latent volatilities and parameters. Our findings show that volatility has a negative impact on returns, indicating that volatility feedback effect is stronger than the effect related to the expected volatility. This result is clear and opposite to the finding of Koopman and Uspensky (2002).

Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration

Given that the United States is an engine of global stock market while China is the largest emerging market with a cornucopia of anomalies in particular, it is vital to investigate the risk-return relationship in the two markets. This paper brings new insights not only into risk-return tradeoff, but also to the leverage effect, with the application of the fractionally co-integrated vector auto-regression (FCVAR) model capturing the fractional cointegrated relationship and long memory property. Results show that China stock markets own the property of double long memory but the US markets don’t. Most of all, in the US market, a positive risk-return tradeoff exists for the whole sample while after the crisis, even we find the negative relation, it’s not a volatility feedback effect but low risk and high returns. However, there is only a volatility feedback effect in China stock markets. Besides, there is a leverage effect in the US market, while Chinese market exhibits a reverse one, another anomaly, indicating significant difference in the two markets again.

Realized Measures to Explain Volatility Changes over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.

Nearly unbiased estimation of sample skewness

In this paper we examine the finite sample bias of sample skewness estimator for financial returns. We show that the bias of conventional sample skewness comes from two sources: the covariance between past return and future volatility, known as the leverage effect, and the covariance between past volatility and future return, commonly referred to as the volatility feedback effect. We derive explicit expressions for this bias and propose a nearly unbiased skewness estimator under mild assumptions. Our simulation study shows that the proposed estimator leads to almost unbiased skewness estimates with a sightly elevated mean squared error, and can reduce the bias of the skewness coefficient estimates by 40%. In our empirical application, we find that bias-corrected average skewness can better predict future market returns comparing to the case without bias-correction. This leads to an improved performance of skewness-based portfolios in terms of Sharpe ratio, certainty equivalence and transaction cost.

The leverage effect puzzle revisited: Identification in discrete time

The term “leverage effect,” as coined by Black (1976), refers to the tendency of an asset’s volatility to be negatively correlated with the asset’s return. Aït-Sahalia et al. (2013) refer to the “leverage effect puzzle” as the fact that, in spite of a broad agreement that the effect should be present, it is hard to identify empirically. For this purpose, we propose an extension with leverage effect of the discrete time stochastic volatility model of Darolles et al. (2006). This extension is shown to be the natural discrete time analog of the Heston (1993) option pricing model. It shares with Heston (1993) the advantage of structure preserving change of measure: with an exponentially affine stochastic discount factor, the historical and the risk neutral models belong to the same family of joint probability distributions for return and volatility processes. The discrete time approach allows us to make the role of various parameters more transparent: leverage versus volatility feedback effect, connection with daily realized volatility, impact of leverage on the volatility smile, etc. Even more importantly it sheds some new light on the identification of leverage effect and of the various risk premium parameters through link functions in closed form. The price of volatility risk is identified from underlying asset return data, even without option price data, if and only if leverage effect is present. However, the link functions are almost flat if the leverage effect is close to zero, making estimation of the volatility risk price difficult and paving the way for identification robust inference.

The Relation between Implied Volatility Index and Crude Oil Prices

Crude oil is a global commodity traded across the world market. The prices of the commodity over an extended period for crude oil have been analyzed using daily prices of crude oil futures and the implied volatility index (OVX). This paper aims to find the predictability of various parameters on the basis of time using neural network and quantile regression methods. Several estimates have been shown based on Barone, Adesi, and Whaley’s (BAW) model of neural network. Estimation parameters include opening, closing, highest and lowest price of the commodity and volumes traded for a given commodity on each trading day. The neural network estimates explain that future prices of the WTI/USO can be predicted with minimal error, and similar can be used to predict future volatility. The quantile regression results suggest that crude oil prices and OVX are strongly associated. The asymmetric association between the WTI/USO and OVX explains that the volatility feedback effect holds good for the OVX market. Bai and Perron least squares estimate evidence of the presence of a break in the time series. The main results uncover several interesting facts that implied volatility tends to remain calm during the global financial crises and higher throughout the post crisis period. The empirical outcome on the OVX market provides some practical implications for the trader and investor, in which oil futures can serve better to hedge the crude price volatility. The crude oil producer can short hedge enough through volatility futures and options to maintain the future quantity of crude to be produced.

Extreme downside risk and market turbulence

We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali et al. [Is there an intertemporal relation between downside risk and expected returns? Journal of Financial and Quantitative Analysis, 2009, 44, 883–909] with a Markov switching mechanism. We show that the risk-return relationship identified by Bali et al. (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship in all states of market volatility.

The Effect of Market Quality on the Causality between Returns and Volatilities: Evidence from CSI 300 Index Futures

This paper investigates the impact of market quality on volatility asymmetry of CSI 300 index futures by using short- and long-run causality measures proposed by Dufour et al. (2012). We use a high-frequency-based noise variance estimator as the comprehensive proxy for market quality and find that volatility asymmetry is closely related to market quality. Specifically, in the period of poor market quality, the volatility asymmetry will vanish or even be reversed, which is mainly due to the sharp decline of the leverage effects. Moreover, the volatility feedback effect will be enhanced while the leverage effect will be weakened if the noise variance is taken into consideration in the causal analysis. Finally, we use other market quality indices as auxiliary variables in the robustness analysis and get similar results.

On the Ex-Ante Cross-Sectional Relation between Risk and Return Using Option-Implied Information

This paper examines cross-sectional relations between ex ante expected returns and betas. As a proxy for ex ante expected returns, we use implied returns obtained from the risk-adjusted option pricing model suggested in this paper. We find that implied returns have a positive and significant cross-sectional relation with implied betas in all maturity groups considered. This significant relation is maintained regardless of the inclusion of the well-known CAPM-anomaly variables such as firm size, book-to-market, past returns, and earnings-to-price ratio. Ex ante market risk premium estimates have a statistical significance as well as an economic significance in that they contain significant forward-looking information on future macroeconomic conditions. We also find that implied betas have a significant negative cross-sectional relation with ex post realized returns measured over the option life. This negative relation is consistent with the volatility feedback effect. We thus argue that implied market betas are priced in ex ante basis.