Short set of slides to describe the purpose and examples of the Actuarial Risk Dashboard. Most importantly, links are provided to the shiny app hosted on shinyapps.io. about 18 hours ago

The purpose is to use R to explore the dynamics of a volatility control index and obtain model prices. The volatility control index includes a mechanism that rebalances portfolio exposure to a risky asset based on the historical volatility of the risky asset. In this analysis the horizon is one year. Over this horizon the risky asset is projected, portfolio rebalancing occurs, and performance is measured. This document is presented as follows. Section 2 explains how historical data is collected, a valuation date is established, and a daily calendar of trading days is developed. In section 3 the model parameters are shown and the number of simulations and projection periods are defined. Section 4 includes the implementation of the Two-State Regime Switching Log-normal model including underlying asset prices, application of the volatility control mechanics, and finally the model prices of call options on the volatility control index- one at-the-money and another at 2.25% above the spot level. Similarly section 5 provides the same information but using the Heston-Nandi GARCH (1,1) model for projecting the underlying asset prices. Both models in sections 4 and 5 allow for stochastic volatility which is important due to the volatility path dependence. Section 6 is an Appendix that shows how the models perform in replicating Black-Scholes for a simple European call option. The Appendix also provides the code for the functions used in the analysis.

The purpose of this project is to outline a method to develop projections of best estimate and stressed mortality rates using R, the Lee-Carter mortality model, the ‘demography’ package, and data available through the Human Mortality Database. Risk managers and actuaries are often faced with developing projections of best estimate mortality rates and understanding the impact of the uncertainty around these projections on financials. Best estimates are often based on the specific underwriting characteristics of a product or block of business. It makes sense to tailor best estimates to fit the specific nature of the portfolio lives. However, when it comes to developing stresses in order to study their impacts, it is handy to view larger (years and lives) and publicly available datasets. This is true in terms of transparency and oddly enough to remove the idiosyncrasies of specific blocks. This latter point is important when combining many blocks of business across many business units where consistency is desired. It is also true in terms of working with regulators and ratings agencies where industry standards have developed along the lines of 99.5th percentile confidence intervals (analytical Value-at-Risk (VaR)) where longer time horizons and population data can allow for good fits and statistical measures of uncertainty. This analysis justifies usage of +20% / -20% stresses to mortality rates for purposes of risk management where analytical VaR is desired at the 99.5th percentile. These stresses should be interpreted as an immediate and permanent stress to mortality rates. The results do not differ too much from published industry standards such as Solvency II Standard Formula stresses of +15% / -20% which presumably are developed from a similar process but applied to European mortality data.

The project uses R to demonstrate a well-known risk management technique to capture the volatility of interest rates and develop shocks that can be applied to the current interest rate term structure. These shocked curves can then be applied to portfolios to measure the impact of changes in interest rates. The technique is based on principal component analysis.

A project for Data Science specialization course.