Cyber risk modeling: a discrete multivariate count process approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past decade, cyber risk has raised much interest in the economy, and cyber risk has evolved from a type of pure operational risk to both operational and liability risk. However, the modeling of cyber risk is still in its infancy. Compared with other financial risks, cyber risk has some unique features. In particular, discrete variables regularly arise both in the frequency component (e.g. number of events per unit time), and the severity component (e.g. the number of data breaches for each cyber event). In addition, the modeling of these count variables are further complicated by nonstandard properties such as zero inflation, serial and cross-sectional correlations, as well as heavy tails. Previous cyber risk models have largely focused on continuous models that are incompatible with many of these characteristics. This paper introduces a new count-based frequency-severity framework to model cyber risk, with a dynamic multivariate negative binomial autoregressive process for the frequency component, and the generalized Poisson inverse-Gaussian distribution for the severity component. We unify these new modeling tools by proposing a tractable Generalized Method of Moments for their estimation and applying them to the Privacy Rights Clearinghouse (PRC) dataset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it