Volatility clustering in data breach counts

Hyunoo Shim, Changki Kim, Yang Ho Choi

Department of Actuarial Science, Hanyang University, Korea;

Korea University Business School, Korea University, Korea

   Abstract                                                                                             

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of

data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has

also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes

in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset

returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now

present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional

heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model

with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we

present that the firms’ volatilities are correlated between some they belong to and that such a clustering effect

remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500

that have their own volatility clustering.

Keywords: data breach, cyber risk, volatility clustering, INGARCH, covariate

논문 다운로드 : https://doi.org/10.29220/CSAM.2020.27.4.487

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