A Breakthrough of Digital Assets Security in Crypto Insurance: Cyber Risk Prediction Models | Chapter 5 | Research and Applications Towards Mathematics and Computer Science Vol. 5

 We use alone cluster analysis to separate the world into geographically distinct groups and therefore use our proposed model to resolve the Privacy Right Clearinghouse (PRC) data breach chronicle. We model zero deficits using a covariate-dependent anticipation, moderate losses utilizing a finite mixture classification, and large misfortunes using an extreme value allocation to capture the heavy-tailed character of the loss data. The risks and moment that digital sciences, devices and media cause us are manifest. Cyber risk is never a matter purely for the IT group. An organisation's risk management function needs a thorough understanding of the uniformly evolving risks, in addition to the practical tools and methods available to address bureaucracy. It is challenging to model the whole range of misfortunes using a usual loss dispersion when considering cyber deficits in terms of the number of records unprotected as a result of cyber occurrences since these deficits frequently include a important share of zeros, distinct traits of mid-range losses, and extreme losses. By suggesting a three-component splicing reversion model that can concurrently simulate zeros, moderate, and solid losses in addition to take into account heterogeneous belongings in mixture parts, we attempt to solve this modeling dificulty. Parameters and coeffcients are supposed using the Expectation Maximization (EM) treasure. Combining with our frequency model (statement linear assorted model) for data breaches, aggregate loss distributions are examined and applications on high-tech insurance pricing and risk administration are discussed.

Author(s) Details:

Meng Sun,
Simon Fraser University, Burnaby, Canada.

Please see the link here: https://stm.bookpi.org/RATMCS-V5/article/view/12215

If You Can’t Measure it You Can’t Manage it - Quantitative Analysis of Cyber Risk Prediction and Mitigation | Chapter 7 | Current Topics on Business, Economics and Finance Vol. 5

 Cyber rupture incidents have increased severely during COVID-19 pandemic and maintain a cyclical trend skilled after. Data breach occurrence result in harsh financial loss and reputational damage to trade, government, healthcare and educational organizations. Compared to sufficient amount of cyber risk case in economic and IT system rule, seldom investigations of high-tech risk have been made in all-inclusive perspective, In order to fill this gap, we suggest a Bayesian generalized linear assorted model to analyze data rift incidents chronicle since 2001. Our model captures the dependency 'tween frequency and severity of high-tech losses, and the behavior of high-tech attacks on entities across time. Risk traits such as types of breach, types of institution, entity locations in chronicle, as well as time current effects are taken into concern when investigating breach repetitions. A statistical predicting model is generated under actuarial mathematics frame, accompanying flexible input usable such as location and arranging types. Predictions and implications of the proposed model in adventure risk management and cyber protection rate filing are discussed and pictorial. Our results show that both geological area and business type play significant duties in measuring cyber risks. The consequences of our predictive data provide numerical bills loss level that can be took advantage of by various kinds of arrangements and design their risk mitigation strategies.

Author(s) Details:

Meng Sun,
Simon Fraser University, BC, Canada.

Please see the link here: https://stm.bookpi.org/CTBEF-V5/article/view/10490

How to Forecast the Weight of Cyber-attacks Events on Stock Returns: Models, Correlations and VaR Backtest | Chapter 3 | Insights into Economics and Management Vol. 2

 This paper aims to examine the effect of cyber-risk on the economy , in particular on the returns of companies experiencing breaches of data. In recent years , the issue has become very important in the literature for the wide reliance of business on the cyber world , especially nowadays due to the pandemic situation of COVID-19 for the difficult need for IT solutions. The research focuses on a case study analysing cyber-attacks on stock prices suffered by selected firms. In order to analyse their association, cyber-risk phenomena are processed according to a portfolio of targeted properties. Risk thresholds, such as VaR, will be assessed and backtested using various methods to monitor which can best capture this form of riskiness..

Author(s) Details

Ilaria Colivicchi
Department of Economics and Management, University of Florence, Florence, Italy.

Riccardo Vignaroli
European Central Bank, Frankfurt, Germany.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/315