This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out-of-target-portfolio information that may be missed when one considers the value at risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation.

This study proposes a measure of the data breach risk’s probable maximum loss, which stands for the worst data breach loss likely to occur, using an alternative approach to estimating the potential loss degree of an extreme event with one of the largest private databases for data breach risk. We determine stationarity, the presence of autoregressive feature, and the Fréchet type of generalized extreme value distribution (GEV) as the best fit for data breach loss maxima series and check robustness of the model with a public dataset. We find that the predicted data breach loss likely to occur in the next five years is substantially larger than the loss estimated by the recent literature with a Pareto model. In particular, the comparison between the estimates from the recent data (after 2014) and those for the old data (before 2014) shows a significant increase with a break in the loss severity. We design a three-layer reinsurance scheme based on the probable maximum loss estimates with public–private partnership. Our findings are important for risk managers, actuaries, and policymakers concerned about the enormous cost of the next extreme cyber event.

Standard models for capital requirements restrict the correlation between risk factors to the linear measure and disregard undertaking-specific parameters. We consider an alternative framework for risk aggregation in non-life insurance using vine copulas that allow non-linear dependence and are estimated with undertaking-specific parameters. We empirically compare our alternative risk model with three regulatory standard models (Korean risk-based capital, Solvency II, Swiss Solvency Test) and show that the standard models lead to more than 50% higher capital requirements on average. Half of the overestimation results from the uniform parameter selection imposed by regulations and the other half comes from the linear correlation assumption. The differences might distort competition when both standard models and internal risk models are used in a single market.

Many experts claim that cyber risks are correlated, but there is not much supporting empirical evidence. We consider 3327 data breach events from 2005 to 2016 and identify a significant asymmetric dependence of monthly losses in two cross-sectional settings: cross-industry losses in four categories by breach types (hacking, lost electronic device, unintended disclosure and insider breach) and cross-breach type losses in five categories by industries (banking and insurance, government, medical service, retail/other business and educational institution). To identify the method that best fits the dependence structure of the dataset, we implement copula modeling by separating the dependence into pairwise non-zero losses and zero loss arrivals. We model the former by pair copula construction (PCC) allowing for the flexible choice of copula functions, whereas the latter is modeled by Gaussian copula. We illustrate the usefulness of our results in two applications to risk measurement and pricing. Our findings are important for risk managers and actuaries who are designing cyber-insurance policies.