The implied volatility smirk in SPY options

Guo, Wei; Gehricke, Sebastian A.; Ruan, Xinfeng; Zhang, Jin E.

doi:10.1080/00036846.2020.1866159

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…The SPX index, which combines the Dow Jones Industrial Average and the S&P 500, is a composite index based on the American stock market. Both indices are the most important stock markets in the world and reflect the biggest publicly listed corporations in the United States [9]. There have been several successes up to this point in pursuing this sort of transformation in various ways.…”

Section: Literature Reviewmentioning

confidence: 99%

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The performance and error analysis of LSTM model combined with various GARCH models in stock forecasting

Chen¹,

Yang²,

Zhu³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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Volatility is a measure of the asset return rate's estimated level of uncertainty and may be used to assess the riskiness of financial assets. We use the market capitalization of 200 stocks representing Korea as the primary analytical aim and evaluate the accuracy between them by analyzing the impacts of different hybrid models' hybrid neural networks, which are based on the returns of the KOSPI 200 stock index. By measuring the effectiveness of these models using four dissimilarity measures, we contrasted the performance of hybrid models that combine a single neural network and a single GARCH type model with that of hybrid neural networks that combine multiple GARCH models (MAE, MSE, HMAE, and HMSE). They are applied to anticipate the KOSPI 200 index data's actual volatility. Among these, hybrid neural networks that integrate more than one GARCH-type model have much better forecasting performance than neural network models that mix two or more or more GARCH-type models. GW-LSTM makes the least accurate forecast. We note that the hybrid model combining the three GARCH models shows a minor increase in predicting ability based on merging two and three models.

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Section: Literature Reviewmentioning

confidence: 99%

“…Volatility is the value derived from publicly traded stock prices variations over a predetermined period. The volatility RVt realization time t formula is provided in (9).…”

Section: Realized Volatilitymentioning

confidence: 99%

The performance and error analysis of LSTM model combined with various GARCH models in stock forecasting

Chen¹,

Yang²,

Zhu³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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“…Many studies show that the IV curves of equity options have transitioned from a smile shape to a smirk shape since the crash in 1987; the IV curves have become more skewed to the left (Corrado and Su, 1997;Skiadopoulous et al, 2000;Cont and da Fonseca, 2002;Carr and Wu, 2003;Foresi and Wu, 2005;Yan, 2011). This smirk shape is now well accepted in the literature for US equity options; however, Guo et al (2020) show that since 2005, the smirk shape found on average seems to be driven mainly by the GFC period and that outside of this time the IV curve of SPY options represents a linear downward sloping line with very minimal curvature. There is now a vast literature examining the IV curves of international equity options (Pe ña et al, 1999;Tanha and Dempsey, 2015;Shiu et al, 2010;Li et al, 2019;Yue et al, 2020), commodity options (Soini and Lorentzen, 2019;Jia et al, 2021;Aschakulporn and Zhang, 2020) and volatility derivative options (Gehricke and Zhang, 2020).…”

Section: Introductionmentioning

confidence: 98%

“…This smirk shape is now well accepted in the literature for US equity options; however, Guo et al . (2020) show that since 2005, the smirk shape found on average seems to be driven mainly by the GFC period and that outside of this time the IV curve of SPY options represents a linear downward sloping line with very minimal curvature. There is now a vast literature examining the IV curves of international equity options (Peña et al ., 1999; Tanha and Dempsey, 2015; Shiu et al ., 2010; Li et al ., 2019; Yue et al ., 2020), commodity options (Soini and Lorentzen, 2019; Jia et al ., 2021; Aschakulporn and Zhang, 2020) and volatility derivative options (Gehricke and Zhang, 2020).…”

Section: Introductionmentioning

confidence: 99%

Implied volatility smirk in the Australian dollar market

Stuart

Gehricke

Zhang

et al. 2021

Accounting & Finance

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This is the first paper to study the options written on the Invesco Currency Shares Australian Dollar Trust (FXA) exchange‐traded fund (ETF). We quantify the empirical characteristics of the FXA option implied volatility (IV) curve showing that it exhibits a smirk shape, as in US equity options, and the curves become more negatively sloped and exhibit more convexity as the time to maturity increases. During the Global Financial Crisis (GFC) period and a bullish period, IV dramatically increased and the slope became even steeper across all maturities indicating that downside insurance is relatively expensive. Further, the information in the quantified IV curve factors has some predictive power for monthly FXA returns.

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“…This method could be used to quantify IV curves and obtains risk‐neutral moments in a small sample. Compared with the method of Bakshi and Kapadia (2003), this method builds an intuitive relationship between IV curve shape and risk‐neutral moments and is widely used by researchers to obtain the level, slope, and curvature factors (Aschakulporn & Zhang, 2021; Gehricke & Zhang, 2020; Guo et al, 2021; Jia et al, 2021; J. Li et al, 2019). We calculate the Zhang and Xiang (2008) IV factors for different maturity option contracts and then use linear interpolation and extrapolation to create time series of constant maturity IV factors.…”

Section: Introductionmentioning

confidence: 99%

Term spreads of implied volatility smirk and variance risk premium

Guo

Ruan

Gehricke

et al. 2023

Journal of Futures Markets

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In this paper, we study the pattern of S&P 500 index options implied volatility (IV) curves and their predictive ability for the variance risk premium (VRP).We explore this predictability employing by the Zhang and Xiang IV factor estimation. We show that the level factor term spread significantly predicts the VRP, proxied by straddle returns and variance swap returns, in both in-sample and out-of-sample tests. The predictability is more pronounced for straddle returns rather than variance swap returns.

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The implied volatility smirk in SPY options

Cited by 6 publications

References 47 publications

The performance and error analysis of LSTM model combined with various GARCH models in stock forecasting

The performance and error analysis of LSTM model combined with various GARCH models in stock forecasting

Implied volatility smirk in the Australian dollar market

Term spreads of implied volatility smirk and variance risk premium

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