Volatility clustering in data breach counts
- Title
- Volatility clustering in data breach counts
- Author
- 심현우
- Keywords
- data breach; cyber risk; volatility clustering; INGARCH; covariate
- Issue Date
- 2020-07
- Publisher
- Korean Statistical Society
- Citation
- Communications for Statistical Applications and Methods, v. 27, no. 4, page. 487-500
- 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.
- URI
- https://kiss.kstudy.com/thesis/thesis-view.asp?key=3817383https://repository.hanyang.ac.kr/handle/20.500.11754/164773
- ISSN
- 2287-7843
- Appears in Collections:
- COLLEGE OF BUSINESS AND ECONOMICS[E](경상대학) > ACTUARIAL SCIENCE(보험계리학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML