Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: The recent global pandemic, coupled with a rising interest rate environment, has emphasized the need for life insurers to be well-prepared for challenges on multiple fronts. Join industry experts in actuarial science and asset-liability management (ALM) as they discuss the significance of incorporating comprehensive pandemic modeling, mortality modeling, lapse modeling, liquidity analysis, and ALM strategies into the risk management framework of life insurance companies. Through case studies and interactive discussions, learn how to utilize systems and APIs from disparate risk models to integrate into an overall analysis to assess the short and long term impacts of the confluence of risk factors on your business, identify vulnerabilities, and implement proactive risk management strategies to enhance resilience and maintain long-term profitability.

Description: Gather with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, pension, investment, product strategy, and more.

Description: An attorney and a statistician will discuss the latest on GenAI, the AI regulation and litigation affecting the use of AI in insurance.

Description: An attorney and a statistician will discuss the latest on GenAI, the AI regulation and litigation affecting the use of AI in insurance.

Description: Internal modernization initiatives and external changes of the financial reporting regulations (e.g., LDTI, IFRS 17) increase the amount of validation activities for actuarial models. In addition, the use of predictive analytics in actuarial modeling requires a different set of validation skillsets/techniques in model validation. These internal and external forces call for a structured, streamlined and robust model validation practice.