Asymmetric Volatility Phenomenon Research Articles

Asymmetric volatility dynamics in cryptocurrency markets on multi-time scales

This study investigates the scale-dependent structure of asymmetric volatility effect in six representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By developing the dynamical approach of DFA-based fractal regression analysis, we detect whether the volatility of price changes is positively or negatively related to return shocks at different time scales. We find that the asymmetric volatility phenomenon varies by scale and cryptocurrency, and the structure is time-varying. Contrary to what is typically observed in equity markets, minor currencies show an “inverse” asymmetric volatility effect at relatively large scales, where positive shocks (good news) have a greater impact on volatility than negative shocks (bad news). The consequences are discussed in the context of who is trading in the market and heterogeneity of the investors.

Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies

Forecasting high-dimensional covariance matrices is crucial for portfolio optimization and risk management. Recent studies focus on modelling the realized covariance matrices constructed with intraday prices using multivariate heterogeneous autoregressive (MHAR) models. Considering the mounting evidence of the asymmetric volatility phenomenon, this paper extends the literature by investigating whether volatility timing investors could achieve economic gains through incorporating asymmetry in the MHAR models. Empirical results in China's stock markets show that our proposed method of incorporating the asymmetric logistic smooth transition structure in the MHAR model achieves the highest economic values out-of-sample under various market conditions. Furthermore, we uncover that the most diversified portfolio strategy is more applicable when the market is in tranquil stage, while the global minimum variance strategy is more applicable during market turbulence.

Volatility Spillovers between Equity and Green Bond Markets

This study examines the market for green bonds, which have been in the spotlight as an eco-friendly investment product. We analyze the volatility dynamics and spillovers between the equity and green bond markets. As the return dynamics of financial products typically exhibit asymmetric volatility, we check whether green bonds also share this property. Our analyses confirm that although green bonds do exhibit the asymmetric volatility phenomenon, their volatility, unlike that of equity, is also sensitive to positive return shocks. An analysis of the association between the green bond and equity markets confirms that although the two markets have some volatility spillover effects, neither responds significantly to negative shocks in the other market.

Real options and asymmetric volatility in light of the firm’s growth opportunities

ABSTRACT This study proposes a real options exercise mechanism as a novel explanation for the asymmetric volatility phenomenon. We suggest that asymmetric volatility stems from the exercise of real call options following positive shocks and the exercise of real put options after negative shocks. Furthermore, we uniquely link asymmetric volatility to real options and firm’s growth opportunities. Using US market return data from the period spanning 1926–2018, this paper demonstrates that following a positive market shock generating return volatility, growth-firms exercise more real call options than value-firms. This further alleviates growth-firms’ volatility response, thereby resulting in higher asymmetric volatility. Book-to-market portfolio analyses provide significant empirical evidence that the firm’s growth opportunities intensify the asymmetric volatility phenomenon.

An Analysis of the Asymmetric Volatility under Asymmetric Information and Ambiguity

The asymmetric volatility phenomenon (‘the phenomenon’, henceforth), documented first by Black (1976), refers to the fact that the stock return and its conditional volatility are negatively correlated. To explain ‘the phenomenon’, this paper presents an asymmetric information model under ambiguity, and provides an empirical test of its result as well. We assume that in the Grossman and Stiglitz (1980), uninformed liquidity traders face ambiguity about the distribution of asset payoffs, and that their attitudes toward ambiguity vary depending on the state of the economy. In model I, their utility functions exhibit ambiguity aversion in the bad state and ambiguity neutrality in the good state. In model II, liquidity traders are still ambiguity-averse in the bad state but ambiguity-seeking in the good state. We find that ‘the phenomenon’ appears in model II when the degree of ambiguity is not large. Furthermore, we show that the possibility of ‘the phenomenon’ is higher as the proportion of liquidity traders increases. To perform an empirical analysis, we measure the degree of ambiguity by the Kolmogorov-Smirnov statistic and show that this measure has a positive relationship with the difference between the volatilities in the good and bad states. In addition, we find that the risk factor constructed by the ambiguity measure has explanatory power about returns on 25 portfolios of the Fama-French type in the Korean market.

A behavioural explanation to the asymmetric volatility phenomenon: Evidence from market volatility index

This study examines how the behavioural explanations, in particular loss aversion, can be used to explain the asymmetric volatility phenomenon by investigating the relationship between stock market returns and changes in investor perceptions of risk measured by the volatility index. We study the behaviour of India volatility index vis-à-vis Hong Kong, Australia and UK volatility index, and provide a comprehensive comparative analysis. Using Bai-Perron test, we identify structural breaks and volatility regimes in the time series of volatility index, and investigate the volatility index-return relation during high, medium and low volatility periods. Regardless of volatility regimes, we find that volatility index moves in opposite direction in response to stock index returns, and contemporaneous return is the most dominating across the four markets. The negative relation is strongest for UK followed by Australia, Hong Kong and India. Second, volatility index reacts significantly different to positive and negative returns; negative return has higher impact on changes in volatility index than positive return across the markets over full-sample and sub-sample periods. The asymmetric effect is stronger in low volatility regime than in high and medium volatility periods for all the markets except UK. The strength of asymmetric effect is strongest for Hong Kong and weakest for India. Finally, negative returns have exponentially increasing effect and positive returns have exponentially decreasing effect on the changes in volatility index.

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

In finance, the asymmetric volatility phenomenon (AVP) and volatility skew are two well‐known topics related to firm risk. In early research, the AVP was documented by Black (Proc 1976 Meetings Business Econ Stat Sect 1976, 177–181), who proposed two possible explanations. The veracity of one such explanation, the “leverage effect,” has long remained controversial. The volatility skew is considered to represent collective phenomena caused by heterogeneous beliefs regarding firm risk among investors. Although the relationship between leverage and firm size has been investigated, the investigations have yielded inconsistent conclusions. All related empirical evidence indicates that the relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect are complex. Regarding accounting principles, Ryan (Account Horizons 1997, 11, 85–95) proposed two concepts for describing firm risk. One of those concepts, “sources of operating risk versus leverage,” revealed the key to linking firm risk to leverage. Among the multidisciplinary methodologies, cybernetics plays a crucial role in complex systems research. The causal nets and feedback loop analysis of cybernetics offer accurate descriptions of realities in terms of causality, nonlinearity, and temporality. This study uses cybernetics to connect the accounting risk concept in microlevel analysis to volatility changes in macrolevel phenomena. This not only validates interdisciplinary analysis with different evidence but also enables the development of a managerial interpretation of different states of firm risk. This study contributes a novel approach that involves the application of a microlevel concept to macrolevel analysis and evidences the “less is more” art of modeling complex phenomena (Schuster, Complexity 2005, 11, 11–13). © 2016 Wiley Periodicals, Inc. Complexity 21: 329–341, 2016

Regime-Dependent Relationships Between the Implied Volatility Index and Stock Market Index

:We examine regime-dependent dynamics between Korea's representative implied volatility index (VKOSPI) and stock market index (KOSPI 200) using a two-regime threshold vector error correction model (TVECM). By analyzing high-quality daily data from January 2003 to June 2013, we make the following interesting observations based on a model with regime splitting. First, regardless of regime, we observe a negative contemporaneous correlation between the VKOSPI and KOSPI 200. Second, while the KOSPI 200 generally leads the VKOSPI under normal market conditions (lower regime), this relationship is overturned when market volatility measured by the VKOSPI level is extremely high (upper regime). Third, in the TVECM framework, the effects of lagged VKOSPI on the KOSPI 200 are positive only in the upper regime, while the effects of lagged KOSPI 200 on the VKOSPI are positive only in the lower regime; this cannot be explained by the traditional framework of an asymmetric volatility phenomenon. Fourth, the KOSPI 200 exhibits greater sensitivity to implied volatility shocks in the upper regime than it does to those in the lower regime.

Stock Returns and Implied Volatility: A New VAR Approach

Abstract The authors re-examine the return-volatility relationship and its dynamics under a new vector autoregression (VAR) identification framework. By analyzing two model-free impliedvolatility indices – the well-established VIX (in the United States) and the recently published VKOSPI (in Korea) – and their stock market indices, the authors find an asymmetric volatility phenomenon in both the developed and emerging markets. However, the VKOSPI shows impulse response dynamics that are quite different from those of the VIX. This finding can be attributed to the unique characteristics of the KOSPI200 options market, which determine the dynamics of the VKOSPI.

No news is not good news: evidence from the intra-day return volatility–volume relationship in Shanghai Stock Exchange

Through this research, we find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets. Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by the return-chasing behaviour of investors during bull markets. We also find that volatility increases after stock price declines in bear markets. After controlling for liquidity shifts, we observe similar patterns in volatility in both bull and bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in the Chinese stock market.

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 and negative return-volatility relationship: The case of the VKOSPI

ABSTRACTKOSPI 200 index options are the most actively traded exchange-listed derivative contracts in the world. And, unlike most other active options markets, trading is dominated by individual investors. This study examines the short-term relationship between stock market returns and implied volatility in the Korean financial market using high frequency data on the recently introduced volatility index (VKOSPI) implied by KOSPI 200 options. We find a strong asymmetric and negative return-volatility relationship at both the daily and intraday levels, which cannot be explained by either leverage or volatility feedback hypotheses on the asymmetric volatility phenomenon. Moreover, we also find that the asymmetric relationship is more pronounced for extremely negative stock market returns. We conjecture that behavioral factors better explain the observed asymmetric return-volatility relationship.

Free Cash Flow, Issuance Costs, and Stock Prices

ABSTRACTWe develop a dynamic model of a firm facing agency costs of free cash flow and external financing costs, and derive an explicit solution for the firm's optimal balance sheet dynamics. Financial frictions affect issuance and dividend policies, the value of cash holdings, and the dynamics of stock prices. The model predicts that the marginal value of cash varies negatively with the stock price, and positively with the volatility of the stock price. This yields novel insights on the asymmetric volatility phenomenon, on risk management policies, and on how business cycles and agency costs affect the volatility of stock returns.

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.

Volatility Stylized Facts

This paper investigates a variety of features exhibited by the amplitude of stock returns. Some of these have already attracted a great deal of attention from researchers, while some others have been documented only recently. - Horizontal dependence of volatility: Volatility is not constant, it is mean-reverting and it tends to cluster (meaning that high volatility is likely to be followed by high volatility periods, and vice-versa). Moreover, volatility exhibits a significant autocorrelation even for very long lags; it is therefore said to have long-memory. - Extreme events: The number of large (either positive or negative) returns is far bigger than what is expected on the basis of modern finance theory - stated differently, the returns distribution has fat tails. Moreover, shocks have a strong impact on volatility and generally lead to a number of aftershocks - this is again a feature that most financial models are unable to replicate. - The leverage effect (also known as the asymmetric volatility phenomenon): Volatility has a negative correlation with returns. Moreover, this relation is asymmetric: when returns are negative, volatility increases rapidly; but, when returns are positive, volatility decreases to a much lesser extent. - Vertical dependence and asymmetric vertical dependence: Volatility measured at different frequencies (e.g. intraday, daily or monthly) has different information content. Low-frequency volatility has a stronger explanatory power on subsequent high-frequency volatility than the other way around. Furthermore, an increase in volatility at low-frequencies has more impact on volatility at high-frequencies than a decrease. This paper extends the existing literature by studying all these stylized facts for both emerging and mature markets, and for both bull and bear market conditions. We are able to confirm the existence of all those stylized facts and to describe them in a more precise way than in the existing literature.

Volatility transmission patterns and terrorist attacks

The objective of this study is to analyse volatility transmission between the US and Eurozone stock markets considering the financial market responses to the September 11, March 11 and July 7 terrorist attacks. In order to do this, we use a multivariate GARCH model and take into account the asymmetric volatility phenomenon, the non-synchronous trading problem and the turmoil periods themselves. Moreover, a graphical analysis of the Asymmetric Volatility Impulse-Response Functions (AVIRF) is introduced, which takes into consideration the financial market responses to the terrorist attacks. Results suggest that there is bidirectional and asymmetric volatility transmission and show the different impacts that terrorist attacks had on both markets.

Comparison of nonnested asymmetric heteroskedastic models

The GJR-GARCH model is a popular choice among nonlinear models of the well-known asymmetric volatility phenomenon in financial market data. However, recent work employs double threshold nonlinear models to capture both mean and volatility asymmetry. A Bayesian model comparison procedure is proposed to compare the GJR-GARCH with various double threshold GARCH specifications, by designing a reversible jump Markov chain Monte Carlo algorithm. A simulation experiment illustrates good performance in estimation and model selection over reasonable sample sizes. In a study of seven markets strong evidence is found that the DTGARCH, with US market news as threshold variable, outperforms the GJR-GARCH and traditional self-exciting DTGARCH models. This result was consistent across six markets, excluding Canada.

Stock Returns, Implied Volatility Innovations, and the Asymmetric Volatility Phenomenon

AbstractWe study the dynamic relation between daily stock returns and daily innovations in optionderived implied volatilities. By simultaneously analyzing innovations in index- and firmlevel implied volatilities, we distinguish between innovations in systematic and idiosyncratic volatility in an effort to better understand the asymmetric volatility phenomenon. Our results indicate that the relation between stock returns and innovations in systematic volatility (idiosyncratic volatility) is substantially negative (near zero). These results suggest that asymmetric volatility is primarily attributed to systematic market-wide factors rather than aggregated firm-level effects. We also present evidence that supports our assumption that innovations in implied volatility are good proxies for innovations in expected stock volatility.

Volatility Transmission Patterns and Terrorist Attacks

The objective of this study is to analyze volatility transmission between the US and Eurozone stock markets considering the effects of the September 11, March 11 and July 7 financial crises. In order to do this, we use a multivariate GARCH model and take into account the asymmetric volatility phenomenon, the non-synchronous trading problem and the crises themselves. Moreover, a graphical analysis of the Asymmetric Volatility Impulse-Response Functions (AVIRF) is introduced, which takes into consideration the crisis effect. Results suggest that there is bidirectional and asymmetric volatility transmission and show the different impact that terrorist attacks had on both markets.