The leverage effect without leverage

Hens, Thorsten; Steude, Sven C.

doi:10.1016/j.frl.2009.01.002

Cited by 52 publications

(21 citation statements)

References 12 publications

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“…There is also a growing number of studies that propose behavioral explanations (Shefrin 2005, Avramov, et al 2006, Hens & Steude 2009, Talpsepp & Rieger 2010, Dzielinski, et al 2011. Avramov et al (2006) show that herding can have effects on volatility asymmetry.…”

Section: Numerical Measurement Of Volatility Asymmetrymentioning

confidence: 99%

“…However, experimental and empirical evidence shows that even assets with zero leverage experience volatility asymmetry (Hens & Steude 2009, Hasanhodzic & Lo 2011. While a number of alternative explanations for this effect have been given, it is still "largely unexplained" (Talpsepp & Rieger 2010), see Bekaert & Wu (2000) for an overview and- Jayasuriya, et al (2009) for further ideas.…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric attention and volatility asymmetry

Dzieliński

Rieger

Talpsepp

2018

Journal of Empirical Finance

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Analyzing a large sample of U.S. firms, we show that the asymmetry of stock return volatility is positively related to investor attention and differences of opinion.Using the number of analysts following a given firm to capture attention and the dispersion in analyst forecasts as a common proxy for differences of opinion, we show that the two effects are complementary. Furthermore, the effect of attention is strongest among stocks with low institutional ownership and high idiosyncratic volatility. Our results are robust to the traditional "leverage effect" explanation of volatility asymmetry. The findings relate to the previously documented relationship between attention and volatility and suggest that volatility asymmetry is driven by asymmetric attention.JEL classification: G11,G12,G14.

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Section: Numerical Measurement Of Volatility Asymmetrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asymmetric attention and volatility asymmetry

Dzieliński

Rieger

Talpsepp

2018

Journal of Empirical Finance

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“…As we will use market values of equity for estimating returns, one might argue that market values of debt would be better for comparison. Although the use of market values of debt can have its advantages over book value [34,35,36], we consider what measures of debt are available.…”

Section: Measures Of Leveragementioning

confidence: 99%

The Impact of Financial Leverage and Market Size on Stock Returns on the Dhaka Stock Exchange: Evidence from Selected Stocks in the Manufacturing Sector

Abdullah¹

2015

IJEFM

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This paper examines the impact of financial leverage and market size of selected stocks on stock returns. Ordinary Least Square (OLS) regression models were used to examine the relationship between the dependent and independent variables. The leverage of the selected firms was estimated from the annual financial reports covering a period of five years from 2008 to 2012 of five corporations operating in the manufacturing sector. Furthermore, average monthly stock prices of the selected stocks between 2008-2012 for Fu-Wang Ceramic, Fine Foods Limited, Olympic Industries, Metro Spinning and Rahim Textiles. The study established a significantly negative relationship between leverage and stock return when the overall industrial data is used. However at the individual firm level the relationship was not stable. Four out of the five selected companies (i.e. Fu-Wang Ceramic, Fine Foods Limited, Olympic Industries and Metro Spinning) all had negative leverage coefficients. Rahim Textile however, had a positive leverage coefficient. The paper also found the relationship between size and stock returns to be significantly positive. However, the size effect within the manufacturing sector was limited.

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“…For example, Hasanhodzic and Lo (2011) find the leverage effect also present in all-equity financed companies and report its effect even stronger than for leveraged firms. Similarly, Hens and Steude (2009) find the effect in the laboratory environment absent of any leverage implying that the inverse relationship between price and volatility is not driven by financial leverage. In addition, Figlewski and Wang (2000) present evidence that the leverage effect is largely independent of a change in the firms' capital structure.…”

Section: Introductionmentioning

confidence: 76%

Time-varying mixture GARCH models and asymmetric volatility

Haas¹,

Krause

Paolella

et al. 2013

The North American Journal of Economics and Finance

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The class of mixed normal conditional heteroskedastic (MixN-GARCH) models, which couples a mixed normal distributional structure with GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as excellent out-of-sample forecasting performance, for financial asset returns. In this paper, we generalize the MixN-GARCH model by relaxing the assumption of constant mixing weights. Two different specifications with time-varying mixing weights are considered. In particular, by relating current weights to past returns and realized (component-wise) likelihood values, an empirically reasonable representation of Engle and Ng's (1993) news impact curve with an asymmetric impact of unexpected return shocks on future volatility is obtained. An empirical out-of-sample study confirms the usefulness of the new approach and gives evidence that the leverage effect in financial returns data is closely connected, in a non-linear fashion, to the time-varying interplay of mixture components representing, for example, various groups of market participants. The class of mixed normal conditional heteroskedastic (MixN-GARCH) models, which couples a mixed normal distributional structure with GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as excellent out-of-sample forecasting performance, for financial asset returns. In this paper, we generalize the MixN-GARCH model by relaxing the assumption of constant mixing weights. Two different specifications with time-varying mixing weights are considered. In particular, by relating current weights to past returns and realized (component-wise) likelihood values, an empirically reasonable representation of Engle and Ng's (1993) news impact curve with an asymmetric impact of unexpected return shocks on future volatility is obtained. An empirical out-of-sample study confirms the usefulness of the new approach and gives evidence that the leverage effect in financial returns data is closely connected, in a non-linear fashion, to the time-varying interplay of mixture components representing, for example, various groups of market participants.

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The leverage effect without leverage

Cited by 52 publications

References 12 publications

Asymmetric attention and volatility asymmetry

Asymmetric attention and volatility asymmetry

The Impact of Financial Leverage and Market Size on Stock Returns on the Dhaka Stock Exchange: Evidence from Selected Stocks in the Manufacturing Sector

Time-varying mixture GARCH models and asymmetric volatility

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