Apophenia? Data Under-Mining the Volatility Leverage-Effect.

2014 ◽  
Author(s):  
Alessandro Palandri
Keyword(s):  
Leverage Effect

scholarly journals Approximate pricing formula to capture leverage effect and stochastic volatility of a financial asset

2021 ◽  
pp. 102072
Author(s):  
Youssef El-Khatib ◽  
Stephane Goutte ◽  
Zororo S. Makumbe ◽  
Josep Vives
Keyword(s):  
Stochastic Volatility ◽  
Leverage Effect ◽  
Financial Asset ◽  
Pricing Formula

FII, Stock Exchange Return, and the Leverage Effect: Evidence from India

2016 ◽  
Vol 19 (1) ◽  
pp. 103-119 ◽  
Author(s):  
Monica Singhania ◽  
Neha Saini
Keyword(s):  
Stock Exchange ◽  
Leverage Effect

scholarly journals Tail Behavior and Dependence Structure in the APARCH Model

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
Farrukh Javed ◽  
Krzysztof Podgórski
Keyword(s):  
Statistical Inference ◽  
Dependence Structure ◽  
Leverage Effect ◽  
Tail Behavior ◽  
News Shocks ◽  
Negative News ◽  
Asymmetric Responses ◽  
Heavy Tailed ◽  
European Economies ◽  
Theoretical Results

AbstractThe APARCH model attempts to capture asymmetric responses of volatility to positive and negative ‘news shocks’ – the phenomenon known as the leverage effect. Despite its potential, the model’s properties have not yet been fully investigated. While the capacity to account for the leverage is clear from the defining structure, little is known how the effect is quantified in terms of the model’s parameters. The same applies to the quantification of heavy-tailedness and dependence. To fill this void, we study the model in further detail. We study conditions of its existence in different metrics and obtain explicit characteristics: skewness, kurtosis, correlations and leverage. Utilizing these results, we analyze the roles of the parameters and discuss statistical inference. We also propose an extension of the model. Through theoretical results we demonstrate that the model can produce heavy-tailed data. We illustrate these properties using S&P500 data and country indices for dominant European economies.

Estimating and Forecasting Volatility Using Leverage Effect

2017 ◽  
Author(s):  
Christina Dan Wang ◽  
Per A. Mykland ◽  
Lan Zhang
Keyword(s):  
Leverage Effect ◽  
Forecasting Volatility

Testing for Leverage Effect in Financial Returns

2014 ◽  
Author(s):  
Christophe Chorro ◽  
Dominique Guegan ◽  
Florian Ielpo ◽  
Hanjarivo Lalaharison
Keyword(s):  
Leverage Effect ◽  
Financial Returns

The Impact of Volatility Targeting

2018 ◽  
Keyword(s):  
Sharpe Ratio ◽  
Leverage Effect ◽  
Left Tail ◽  
Risk Parity ◽  
Asset Classes ◽  
Equity Portfolios ◽  
Sharpe Ratios ◽  
Tail Events ◽  
The Impact ◽  
Extreme Returns

Recent studies show that volatility-managed equity portfolios realize higher Sharpe ratios than portfolios with a constant notional exposure. The authors show that this result only holds for risk assets, such as equity and credit, and they link this finding to the so-called leverage effect for those assets. In contrast, for bonds, currencies, and commodities, the impact of volatility targeting on the Sharpe ratio is negligible. However, the impact of volatility targeting goes beyond the Sharpe ratio: It reduces the likelihood of extreme returns across all asset classes. Particularly relevant for investors, left-tail events tend to be less severe because they typically occur at times of elevated volatility, when a target-volatility portfolio has a relatively small notional exposure. We also consider the popular 60–40 equity–bond balanced portfolio and an equity–bond–credit–commodity risk parity portfolio. Volatility scaling at both the asset and portfolio level improves Sharpe ratios and reduces the likelihood of tail events.

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