The impact of liquidity on option prices

Chou, Robin K.; Chung, San‐Lin; Hsiao, Yu Jen; Wang, Yaw‐Huei

doi:10.1002/fut.20531

Cited by 27 publications

(5 citation statements)

References 42 publications

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“…Chou et al (2011) find a clear link between the level of IV curve and options liquidity. Further, Nordẻn and Xu (2012) show that the option "happiness" (the steepness of the volatility smirk) is significantly dependent on the options liquidity.…”

Section: Intra-daily Implied Volatilitymentioning

confidence: 83%

Pricing Currency Options with Intra-Daily Implied Volatility

Hoque¹,

Kalev²

2015

AABFJ

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This paper introduces the intra-daily implied volatility (IDIV), a new volatility measure to price currency option accurately. The IDIV is developed based on the implied volatility estimated on equally spaced intradaily intervals. This model captures the intra-daily level aggregate information related to foreign exchange (FX) behavior, which changes every five minutes. The implied volatility (IV) and realized volatility (RV) are widely accepted as good estimates of daily and intra-daily price volatility, respectively. Therefore, using the options pricing framework, we assess the capability of IDIV against IV and RV in pricing foreign currency options. A comparison of out-of-sample forecasts under both the F-test and Diebold-Mariano test reveals that the IDIV outperforms both the IV and the RV in estimating one-day-ahead option prices. In other words, the IDIV estimation framework provides a more accurate and efficient volatility estimate for pricing currency options. The findings of this study indicate that the forward looking intra-daily information of IDIV is appropriate to price options correctly rather than forward looking daily and historical intra-daily information is obtained by the IV and RV, respectively. This paper introduces the intra-daily implied volatility (IDIV), a new volatility measure to price currency option accurately. The IDIV is developed based on the implied volatility estimated on equally spaced intra-daily intervals. This model captures the intra-daily level aggregate information related to foreign exchange (FX) behavior, which changes every five minutes. The implied volatility (IV) and realized volatility (RV) are widely accepted as good estimates of daily and intra-daily price volatility, respectively. Therefore, using the options pricing framework, we assess the capability of IDIV against IV and RV in pricing foreign currency options. A comparison of out-of-sample forecasts under both the F-test and Diebold-Mariano test reveals that the IDIV outperforms both the IV and the RV in estimating one-day-ahead option prices. In other words, the IDIV estimation framework provides a more accurate and efficient volatility estimate for pricing currency options. The findings of this study indicate that the forward looking intra-daily information of IDIV is appropriate to price options correctly rather than forward looking daily and historical intra-daily information is obtained by the IV and RV, respectively.

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Section: Intra-daily Implied Volatilitymentioning

confidence: 83%

Pricing Currency Options with Intra-Daily Implied Volatility

Hoque¹,

Kalev²

2015

AABFJ

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“…Topics include price discovery in intraday trading (Booth et al, 1999; Tse, 1999; Yang et al, 2012), the effect of intraday on the volatility of returns (Cai et al, 2001), informed trading in the index trading options (Ahn et al, 2008), and the role information in the limit order book in price discovery during trading (Cao et al, 2009). Other articles focus on the relationship between trade volumes and volatility and price variability (Foster, 1995; Najand & Yung, 1991), transaction costs (Locke & Venkatesh, 1997), the effect of trading strategies on market liquidity and volatility (Chou et al, 2015), and the impact of liquidity on option prices (Chou et al, 2011). Overall, this cluster's articles relate to trading and focus on topics related to information, price discovery, and market liquidity.…”

Section: Bibliographic Coupling Of Jfm Articlesmentioning

confidence: 99%

Forty years of the Journal of Futures Markets: A bibliometric overview

Baker

Kumar

Pandey

2021

Journal of Futures Markets

113

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This study uses bibliometrics to present a retrospective on the Journal of Futures Markets (JFM) on its 40th anniversary. The Journal's annual number of publications and citations grew substantially, with US‐affiliated authors being the dominant contributors. Authorship analysis reveals an increase in collaboration and diversity among JFM authors. Bibliographic coupling analysis reveals that the Journal's main themes include commodities, volatility, trading, hedging, arbitrage and pricing, forecasting volatility, and credit default swaps. Its primary citation drivers are article age, article length, number of authors, FT100 affiliation, and references.

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“…In order to rule out the possible effects on option prices from other firm-specific characteristics and factors, we include a number of control variables in our empirical tests. Duan and Wei (2009), Dennis and Mayhew (2002) and Chou et al (2011) document that systematic variance risk ratio, firm size, , stocks' trading volume, spot and option liquidity can explain option prices. Following Duan and Wei (2009), we assume a standard one-factor market model for stock .…”

Section: Control Variablesmentioning

confidence: 99%

“…Using data on 1,421 individual firms, Dennis and Mayhew (2002) find that firm size, β, and stock trading volume help explain the risk-neutral skewness. Chou et al (2011) illustrate the impact of both spot and option liquidity levels on option prices using options on component stocks of the DJIA Index. With a decrease (increase) in stock (stock option) liquidity, there is an increase in the level of the implied volatility curve.…”

mentioning

confidence: 99%

Stock Return Autocorrelation and Individual Equity Option Prices

Fang¹

2021

JBTP

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This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.

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The impact of liquidity on option prices

Cited by 27 publications

References 42 publications

Pricing Currency Options with Intra-Daily Implied Volatility

Pricing Currency Options with Intra-Daily Implied Volatility

Forty years of the Journal of Futures Markets: A bibliometric overview

Stock Return Autocorrelation and Individual Equity Option Prices

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