An empirical investigation of large trader market manipulation in derivatives markets

Jarrow, Robert A.; Fung, Scott

doi:10.1007/s11147-018-9143-0

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Here we assume that the market follows the parameters described in Table 1 and that the large trader is the seller of the call option with parameters given in Table 2. Our overall parameter assumptions on the market impact are in line with the empirical study of Jarrow et al (2018).…”

Section: Numerical Resultsmentioning

confidence: 91%

Derivatives under market impact: Disentangling cost and information

2022

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Section: Numerical Resultsmentioning

confidence: 91%

Derivatives under market impact: Disentangling cost and information

2022

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“…4 concludes. activity in the option market (Jarrow et al 2018), which justifies the use of option data to elicit sophisticated investors' opinions. 3 While Google Trends directly measures search activity for a specific company, other attention measures such as a firm's press coverage represent only indirect attention proxies that assume a positive relation between information demand and information supply.…”

Section: Introductionmentioning

confidence: 99%

Idiosyncratic volatility, option-based measures of informed trading, and investor attention

Mohrschladt

Schneider

2021

Rev Deriv Res

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We establish a direct link between sophisticated investors in the option market, private stock market investors, and the idiosyncratic volatility (IVol) puzzle. To do so, we employ three option-based volatility spreads and attention data from Google Trends. In line with the IVol puzzle, the volatility spreads indicate that sophisticated investors indeed consider high-IVol stocks as being overvalued. Moreover, the option measures help to distinguish overpriced from fairly priced high-IVol stocks. Thus, these measures are able to predict the IVol puzzle’s magnitude in the cross-section of stock returns. Further, we link the origin of the IVol puzzle to the trading activity of irrational private investors as the return predictability only exists among stocks that receive a high level of private investor attention. Overall, our joint examination of option and stock markets sheds light on the behavior of different investor groups and their contribution to the IVol puzzle. Thereby, our analyses support the intuitive idea that noise trading leads to mispricing, which is identified by sophisticated investors and exploited in the option market.

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Option Pricing Using Stochastic Volatility Model Under Fourier Transform of Nonlinear Differential Equation

Liu¹,

Wang²,

Cheng³

et al. 2022

Fractals

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The purposes are to solve the option pricing problem in financial derivatives and provide a practical basis for reducing the risk rate of the financial market. First, the classification of fractal theory and option pricing is outlined, the connotation of stochastic volatility is analyzed, and a stochastic volatility model is established. According to the Fourier transform and nonlinear differential equation, the pricing method in the volatility swap process and the pricing method of volatility derivatives are analyzed. Finally, the option pricing parameters are set, and the Origin 75 software is employed to visually analyze the mathematical results. The forward prices under stochastic volatility are compared monthly and weekly, and the resulted values are all close to 235. There is a gap between the forward prices of each month and each quarter, and the difference between the forward prices of each quarter is large, close to 50. The average relative percentage errors (ARPEs) within and out of the sample calculated by the stochastic volatility model are close to 16%, and the error gaps between different monetary intervals are also large. With the increase in strike price, the change rate of options under the random volatility model is faster than that of options under VIX index. When the strike price increases to 0.6, the two options are equal. The fitting trend of the stochastic volatility model for implied volatility at different maturity dates first decreases and then tends to a fixed value. The implied volatility of the 60-day-expiring option has changed significantly, showing a trend of increasing first and then tending to a fixed value (0.8). In contrast, the implied volatility of the 90-day-expiring option and the 120-day-expiring option almost have no change. When [Formula: see text] (fractional operator) takes 1 and [Formula: see text] takes 1.6 and 1.3, the solution to the stochastic volatility model is consistent with that to the classic CGMY model. When [Formula: see text] takes 1, the stochastic volatility model has the same solution as the classic CGMY and BS models. As the fractional order of time increases, the trend of asset price changes becomes smaller.

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An empirical investigation of large trader market manipulation in derivatives markets

Cited by 4 publications

References 35 publications

Derivatives under market impact: Disentangling cost and information

Derivatives under market impact: Disentangling cost and information

Idiosyncratic volatility, option-based measures of informed trading, and investor attention

Option Pricing Using Stochastic Volatility Model Under Fourier Transform of Nonlinear Differential Equation

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