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Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.

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We analyze various jumps for Heston model, non-IID model and three Lévy jump models for S&P 500 index options. The Lévy jump for the S&P 500 index options is inevitable from empirical studies. We estimate parameters from in-sample pricing through SSE for the BS, SV, SVJ, non-IID and Lévy (GH, NIG, CGMY) models by the method of , and utilize them for out-of-sample pricing and compare these models. The sensitivities of the call option pricing for the Lévy models with respect to parameters are presented. Empirically, we show that the NIG model, SV and SVJ models with estimated volatilities outperform other models for both in-sample and out-of-sample periods. Using the in-sample optimized parameters, we find that the NIG model has the least SSE and outperforms the rest models on one-day prediction.