Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation

Badshah, Ihsan

doi:10.1002/fut.21551

Cited by 87 publications

(51 citation statements)

References 55 publications

(110 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low (2004) had already suggested that the cause of the asymmetric effect of the return-volatility relation could be behavioral. In the same sense, other studies provide evidence of this asymmetric relation betwen return and implied volatility (see, for example, Agbeyegbe, 2015;Badshah, 2013). …”

Section: The Return-volatilily Relationmentioning

confidence: 67%

An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations

Silva¹

2017

IJEF

View full text Add to dashboard Cite

Oil market movements have important implications for portfolio management and hedge strategies for investors who negotiate this commodity. Studies involving the relation of the CBO Crude Oil ETF Volatility Index (OVX) and the United States Oil Fund (USO) return are small in number and do not explore some aspects related to the asymmetry and nonlinearity of this relation. Therefore, this article proposes an analysis about the relation between return and volatility, using parametric and nonparametric methods. To do so, a daily data series from 2007 to 2016, ordinary least squares, quantile regressions and the nonparametric B-splines methods were used. The results indicated a negative, asymmetric and nonlinear contemporary relation between the variables. The effects of negative returns were more pronounced than the positive ones in volatility. In addition, it was found that the relation is not the same for different quantiles. Nonparametric estimates suggested that the positive returns have a convex profile and the negative returns have a concave profile. It indicated the downward-sloping reclined S-curve for the 0.05, 0.90 and 0.95 quantiles of volatility.

show abstract

Section: The Return-volatilily Relationmentioning

confidence: 67%

An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations

Silva¹

2017

IJEF

View full text Add to dashboard Cite

show abstract

“…2 gives the quantile–quantile plots for our series, and none of the data series shows a good fit to the normal distributions. It is well known that when the data distribution is not adequately described by a normal distribution, quantile regression (QR) can provide more efficient estimates for the return–volatility relationships (Badshah, 2012). Table 1 gives the descriptive statistics for all the variables.…”

Section: Data and Empirical Estimatesmentioning

confidence: 99%

An inverted U‐shaped crude oil price return‐implied volatility relationship

Agbeyegbe

2015

Rev Financ Econ

View full text Add to dashboard Cite

We examined the return–volatility relationship for USO ETF oil price return and CBOE Crude Oil ETF Volatility Index, OVX. The data for the USO and OVX covers the period covering May 11, 2007 to February 28, 2013. Our OLS regression results suggest evidence of regular feedback and leverage effects. When we employ linear quantile regression techniques, we find evidence of regular and inverse feedback effects. The inverse feedback effects being noticeable in the upper quantile region of the oil return distribution. There is also support for a regular leverage effect in USO prices. We also examined the return–volatility relationship using quantile regression copula methods for measuring the degree of asymmetry in the relationships between the oil price return and implied volatility. The results of the analysis indicate, first, that there exists a negative relationship between contemporaneous oil VIX and USO ETF oil returns. Second, that the relationship between oil returns and implied volatilities depends on the quartile at which the relationship is being investigated. Third, there exists an inverted U‐shaped dependency relationship between returns and implied volatilities across quantiles. Fourth, though an inverted U‐shape exists, the shape is different from those observed in stock markets.

show abstract

“…Frijns et al (2010), regressing the daily changes in volatility index on leads, lags, and contemporaneous and absolute market returns, documented a negative and asymmetric relation between S&P/ASX 200 VIX and its market index returns. Badshah (2013), using quantile regression models, has provided evidence of negative and asymmetric relations for the S&P 500, NASDAQ, DAX 30, STOXX index, and their corresponding volatility index. He found that asymmetry effect is more pronounced in uppermost quantile relative to median quantile.…”

Section: Related Literature and Hypothesesmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation

Cited by 87 publications

References 55 publications

An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations

An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations

An inverted U‐shaped crude oil price return‐implied volatility relationship

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

Contact Info

Product

Resources

About