THE APPLICATION OF MARKOV CHAIN AND MONTE CARLO SIMULATION ON THE DETERMINATION OF THE RISK PREMIUM OF A CYBER INSURANCE WITH A LOGIT-NORMAL DISTRIBUTION APPROACH

• Main Author: Adrian Halim, Sebastian
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/55197
• Institution: Institut Teknologi Bandung

Along with the development of information technology, the need for the internet has become one of the most important things. With the increasing number of internet users today, the insurance industry is exposed to new opportunities relating to the protection of digital data. A new type of insurance emerged called cyber insurance. This final project discusses a financial loss model for cyber insurance using Markov Chain. In this final project, the financial losses due to cyber-attacks is assumed to be a proportion of a certain value. A probability distribution for the proportion used in this final project is a Logit-normal distribution. The cyber insurance model is then used to determine the risk premium with the help of the Monte Carlo simulation. The determination of the risk premium uses three risk measures, namely: the Standard Deviation Principle; Value-at-Risk or VaR; and Tail Value-at-Risk or TVaR.