​Module 8: Valuation of Non-Life Insurance provisions ​ Objective: The Guidelines on the valuation of Non-Life insurance technical provisions are shown to increase the coherence and convergence of the professional practice of all types and sizes of insurance companies. Some traditional and modern techniques for calculating the reserve are explained. The technical provision for benefits. Regulations in Solvency 2 Aggregate Claims Modeling Frequency Distributions Distributions of the claim amount Analytical methods Monte Carlo Simulation Triangle-based methods for calculating Loss Reserving Provisions Grossing up link ratio Chain Ladder Bornhuetter Ferguson Stochastic methods for calculating the Provision for benefits. Mack's method Bootstapp Method Machine Learning in Non-Life Insurance Claims reserving based on Bayesian neural networks Chain Ladder Neural Network Exercise 12: Fit frequency using negative binomial and Poisson Exercise 14: Claims amount adjustment using lognormal, gamma, weibull, exponential and G-H in python and R Exercise 15: Estimation of accident rate distribution with Monte Carlo simulation in R Exercise 16: Chain-Ladder Neural Network Exercise 17: Estimating provisions using the Run Off Chain Ladder Exercise 18: Estimating provisions using Bootstrap in R Exercise 19: Generative AI in Valuation of Non-Life Insurance provisions ​ ​MARKET RISK ​ Module 9: Value at Risk (VaR) and Expected Shorfall in life and non-life lines ​ Objective: Explain the concept of the Value at Risk VaR and the Expected Shortfall in the life and non-life branches. The treatment of returns and volatility using GARCH models is explained. Introduction to VAR VAR in life insurance VAR in the non-life business Volatility Estimation GARCH(1,1) GARCH Multivariate EWMA Volatility Forecasting Parametric Models Normal VaR t-student distribution lognormal distribution Linear Model for Stocks and Bonds Quadratic model for options VaR extensions Expected Shortfall or Tail VaR Conditional VaR Cash flow mapping Exercise 20: Simulation and forecasting volatility using GARCH(1,1) and multivariate model in R Exercise 21: estimation of the internal model of VaR and Expected Shortfall in life and non-life insurance Module 10: Parametric VaR with Extreme Value Theory ​ Objective: Explain the theory of extreme value to apply it to internal models. This distribution allows estimating the probability of truly extreme events. The pros and cons of these distributions in insurance practice are explained. EVT Extreme Value Distributions gumbel Frenchet Weibull Generalized Pareto distributions Exponential Pareto Beta Threshold estimate Model Selection Hill and Mean Excess Plot Generation of random EVT values EVT estimation under Bayesian approach Disadvantages of EVT Exercise 22: Estimation of Graphs: Mean Excess, Q-Q and Hill plot in R Exercise 23: Maximum likelihood parameter estimation of GDP in SAS and R Exercise 24: VaR estimation by EVT in R​ Exercise 25: Quantum VaR estimation  ​ Module 11: Historical Simulation and Monte Carlo ​ Objective: VaR is explained by Monte Carlo simulation applied to insurance companies. Being the best methodology to estimate the VaR in a time horizon of one year. VaR Historical Simulation Adjust for volatility VaR Monte Carlo simulation Simulation with a risk factor Simulation with multiple risk factors Variance Reduction Methods Normal Multivariate Distribution and T-Student VaR Monte Carlo based on Gaussian copula and t-student copula Exercise 23: VaR estimation: using Monte Carlo Simulation and Historical Simulation in Excel and R Exercise 24: Historical Simulation Backtesting Exercise 25: VaR using Gaussian copula and tStudent in SAS and R ​ Module 11: Market Risk ​ Objective: Good market risk practices for insurance companies are explained. The results of the SCR under the standard formula are compared against the internal models. Standard Formula on Market Risk Sub-modules in market risk Interest rate risk Equity risk real estate risk Spread Risk Concentration Risk liquidity risk SCR VaR 99.5% Internal and partial models Internal Market Risk Model VaR for interest rate risk Stochastic Process Selection VaR using stochastic process of an asset Simulation with principal components Scenario Simulation VaR for interest rate risk with principal components Spread VaR exchange rate VaR Equity VaR Concentration and correlation risk modeling VaR of Options Delta Normal VaR Delta Gamma VaR Monte Carlo simulation Boundary Structure Exercise 26: Estimating the VaR of options with Monte Carlo simulation in Excel and R Exercise 27: Cash Flow mapping and VaR estimation of a bond portfolio Exercise 28: VaR estimation of non-life and life risk ​ ​Module 12: Stress Testing and Backtesting ​ Objective: Stress testing is one of the best tools for managing market risk. Consider exceptional but plausible events. Insurance companies will also need to validate internal models with backtesting. Stress Testing Approaches Historical Stress Testing Reverse Stress Test Stress testing in correlation Stress testing on volatility Multivariate stress testing Backtesting Kupiec`s Test Frequency Conditional Coverage Analysis of losses in the tail of the distribution Clean and dirty backtesting Exercise 29: Stress testing on a correlation matrix in Excel Exercise 30: Backtesting of VaR in Excel ​ CREDIT RISK ​ Module 15: Credit Risk Structural and Reduced Form Models ​ Objective: Structural credit risk models require financial information from the company and have proven to be efficient during the pandemic. These models help measure the credit risk of fixed income investments, particularly bonds. ​ Structural Models Merton's model KMV model reduced form models Jarrow-Turnbull Model Duffie and Singleton Model Neutral default probabilities Conversion of default currents into discrete PDs Adjustment of reduced form models to historical databases Construction of default probability curves Validation with Falkenstein and Boral Test Jump to default zero coupon bonds voucher with coupons convertible bonds CDS Valuation Exercise 31: Structural model in R and Excel Exercise 32: Construction of default probability and hazard rate curves in Excel and SAS Exercise 33: Bonus and CDS valuation in Excel with VB Exercise 34: Internal model of market and credit risk CIR simulation of fixed and variable income interest rates Jarrow-Turnbull-Lando model for credit risk with transition matrices. Comparison against standard formulas. Module 14: Credit Risk Portfolio Models ​ Objective: It explains how to model the credit risk of investment portfolios of bonds, loans and credit derivatives. The credit risk of reinsurers is explained. The creditmetrics and Creditrisk+ approaches to economic capital estimation are shown. Rating Models PD and LGD estimation default correlation asset mapping Economic Capital Models creditmetrics Credit risk + One-factor model Reduced Form Models Counterparty Risk Reinsurance Counterparty Default Risk Credit Risk in Reinsurance portfolio approach Concentration Risk Credit Risk in the Credit Insurance portfolio approach Exercises 35: Economic capital with a unifactorial model using Monte Carlo Simulation in Excel and SAS. Exercise 36: Economic capital: CreditRisk+ in SAS Exercise 37: Economic capital: Creditmetrics in Excel Exercise 38: Economic Capital of the bond portfolio ​ LIFE AND NON-LIFE INSURANCE RISK ​ Module 15: Non-Life Underwriting Risk ​ Objective: This risk is divided into three large blocks, the risk for premiums: it refers to future claims that may arise during and after the period for which the solvency calculation is made, that is, that the expenses plus the losses due claims are greater than the premiums received. The risk due to reserves due to two causes, the miscalculation of provisions and fluctuations in the actual number of claims around the midpoint. The third is catastrophic risk. ​ Analysis Standard Formulas Underwriting risk: Non-life Reserve Risk Premium Risk catastrophic risk Health underwriting risk Non-Catastrophic Risk Internal models Premium Risk Internal model Reserve Risk catastrophe risk Internal model using Monte Carlo Simulation Internal model using Multiyear approach VaR estimate 99.5% Catastrophic Risk Modeling Frequency and Severity catastrophe science Tsunamis Hurricanes: Frequency, Regions Hurricane Modeling Earthquakes, frequency and severity Quantum computing to estimate insurance capital Introduction Fundamentals and Notations of Quantum Mechanics Classical surplus process with quantum mechanics Quantum algorithm to predict the insurance capital premium gate Claim Gate The expected reserve in an insurance company Exercise 39: VaR for Premium risk in Excel and Python Exercise 40: VaR for Reserve risk in R Exercise 41: Internal non-life underwriting risk model and comparison against standard formulas in SAS and R Exercise 42: Quantum computing to estimate capital ​ Module 16: Life Underwriting Risk ​ Objective: This risk is divided into Biometric Risk (mortality, longevity, disability/illness), Portfolio Fall Risk, Expense Risk, Revision Risk, and Catastrophe Risk. A comparison of results between the SCR by standard formula and internal models is shown. Traditional mortality models and others with advanced machine learning techniques are explained. ​ Biometric Risk Mortality Risk Longevity Risk Morbidity Risk Portfolio drop risk Expense risk Revision risk Catastrophic risk and pandemics Actuarial models for pricing Behavioral Risks Dynamic Mortality Tables Mortality models Model Lee Carter Singular Value Decomposition Stochastic Mortality Model Longevity Risk improvement factors longevity index Mortality Risk Models using Machine Learning Continuous Models: Cox-Net, Cox Tree, Cox XGBoost, Survival Tree, Random Survival Forest Discrete Models: Random Forest, LightGBM, XGBoost Logistic, GAM CatBoost Analytical Survival Distributions Internal Risk Model Life Insurance Risk management Gamification Behavioral risk analysis Insured Linked Securities Exercise 43: Stochastic Mortality Shock Model in SAS Exercise 44: Lee Carter, Makeham and Logit model in SAS Exercise 45: Internal Model Life Insurance Risk Monte Carlo simulation Lee Carter model Model and simulation of interest rate term structure Copulas in Excel and R Exercise 46: Discrete and Discrete Mortality Models: Cox XGBoost, Survival Tree, Random Survival Forest, and LightGBM ​ OPERATIONAL RISK Module 17: Operational Risk ​   Objective: Explain both the advanced management of operational risk in insurance companies and an introduction to the measurement of this risk to obtain a distribution of losses. Introduction Operational Risk Loss Event Management Risk Control Self Assessment Scenario Based Assessment Key Risk Indicators Capital estimation LDA approach Exercise 47: Estimation of Economic Capital of 5 business units, aggregated and individual, using the following Frequency and Severity distributions: Frequency Poisson Negative Binomial Severity lognormal burr gamma Weibull Inverse Gaussian GDP EVT LogLogistic G-H 4 parameters Mixture of Lognormals lognormal-EVT Alpha Stable Poisson-Gamma Bayesian approach Lognormal partition and GDP Scenarios with Expert criteria Exercise 48: Selection of the best distribution using goodness-of-fit tests in Excel Exercise 49: Estimation of economic capital with truncated data Exercise 50: Internal model using Monte Carlo Simulation with effect of deductible / insurance excess in R Exercise 51: Internal model with Monte Carlo Simulation with frequency distribution with Gaussian copulas in R Exercise 52: Internal model with Monte Carlo Simulation of aggregate osses of business units with t and frank copulas in R Exercise 53: Comparison of internal models with Recursive Panjer, Fast Fourier Transformation and Monte Carlo Simulation in R and Excel ​ RISK MANAGEMENT AND MITIGATION ​ ​Module 18: Insured Linked Securities (ILS) ​   Purpose: ILS are defined, broadly, as financial instruments whose values are driven by insurance loss events. The instruments are linked to catastrophes, mortality and longevity. They help the transfer and mitigation of risk as well as the diversification of capital. Definition Insurance-Linked Securities derivatives market Derivatives and bonds linked to Property and Casualty risk Weather Derivatives Catastrophe Bonds Catastrophe Derivatives Derivatives and bonds linked to longevity and mortality risk Longevity Swaps Longevity Bonds Risk management in ILS portfolios Exercise 54: Valuation Longevity Swap in Excel Exercise 55: Valuation derived from Climate in Excel ​ VALIDATION OF INTERNAL MODELS Module 21: Validation of Internal Models I ​   Objective: The validation process of internal models is explained, the most common techniques such as backtesting. The appropriate reporting to validate models is explained in general terms. ​ validation process modeling process modeling tools Backtesting Analysis Stress Testing Results stability Model limitation reporting Scoring model ​ Module 19: Validation of Internal Models II ​   Objective: The validation of detailed internal models for each type of risk is explained. Advanced SCR validation techniques calculated by internal models are explained. ​ Validation of Internal Models Market risk Credit risk Operational risk Underwriting risk: Non-life ​Underwriting Risk: Life Validation of: Model Design Model Output Processes, data and test of use Kupiec`s Test for market risk Loss aggregation validation Testing distributions using Berkowitz test loss distribution Simulation of the critical chi-square value Berkowitz test in subportfolios power assessment Scope and limits of the test Model risk due to uncertainty Exercise 56: implementation of the Berkowitz test in internal credit models Exercise 57: Simulation of losses and model risk in non-life underwriting risk ​ ASSET AND LIABILITY MANAGEMENT ​ Module 20: Quantum Portfolio Management ​​​ Portfolio diversification Allocation of financial assets in insurance companies Financial risk tolerance Asset Portfolio Optimization efficient frontier Financial portfolio simulation Financial portfolio simulation techniques Portfolio Management using Reinforcement Learning Portfolio Management using quantum algorithms Exercise 58: Portfolio optimization using quantum algorithms Module 21: Asset and Liability Management ​ Objective: The management of assets and liabilities is becoming more important for insurance companies every day due to the pandemic. Optimization models are explained, from the well-known cash flow matching, to advanced models of stochastic programming of assets and liabilities. Liquidity risk is explained. Tools to manage assets and liabilities Duration Gap analysis Interest rate risk Liquidity risk Cash Flow Testing Immunization Cash Flow Matching Optimization of assets and liabilities Dynamic Financial Analysis Stochastic and dynamic scenario trees in assets and liabilities dynamic programming Stochastic dynamic programming Maximization of the financial margin and economic value Application of recent economic and financial theories Conditioning factors of liquidity, capital and Solvency 2 Stress Testing Scenarios International financial reporting regulations and Solvency II Sensitivities in IFRS 4 Financial Statements​ Life and Non-Life Insurance Risk Financial Risks Exercise 59: Optimization of Cash Flow Matching in Excel with Solver Exercise 60: Portfolio optimization using stochastic dynamic programming in SAS Exercise 61: Impact on financial statements due to changes in insurance and financial risk sensitivities in Excel Exercise 62: Using Generative AI in ALM  ​ Module 22: Quantum ALM​​ ​ ALM Quantum Approach Quantitative Methods in ALM Bailey and Redington approach Operations Research Techniques classic optimization Quantum Computing in Asset–Liability Management Quadratic Unconstrained Binary Optimization (QUBO) Number of Qubits Exercise 45: Optimization of assets and liabilities using quantum algorithms ​ STRESS TESTING ​ Module 23: Scenario Analysis ​   Objective: Explains how to build risk scenarios. Activity that is becoming more pressing by the day due to the pandemic and its serious implications for the economy. ​​ Definition of the scenarios Using the scenarios scenario identification Scenario typology Scenario-based risk assessment Scenario Analysis Process Governance in the scenarios Definition of risk appetite Scenario evaluation Economic Scenario Generator (ESG) Exercise 63: Economic Scenario Generator in Excel ​ Module 24: Forecasting Models ​   Objective: In order to project scenarios for the future, it is necessary to have traditional tools such as VAR and ARIMA models and other more sophisticated and precise ones such as machine learning. ​ Data processing Non-Stationary Series Dickey-Fuller test Cointegration Tests Econometric Models ARIMA models VAR Autoregressive Vector Models GARCH models Machine Learning Models Supported Vector Machine LSTM Recurrent Neural Network Bayesian Neural Network LSTM Quantum LSTM Review of assumptions of econometric models stationary series heteroscedasticity Outliers serial correlation Collinearity detection Exercise 64: Tests of non-stationary series and cointegration Exercise 65: VAR models in R Exercise 66: Forecasting Machine Learning SPV and NN in R​​ Exercise 67: Forecasting LSTM Exercise 68: Forecasting Bayesian LSTM Exercise 69: Forecasting with Transformers of Generative AI ​ Module 25: Stress Testing for Insurance companies ​ Objective: Stress testing consists of generating for each scenario shocks to parameters such as the mortality rate, share price, interest rates, etc., and measuring the impact they would have on capital. ORSA's role in this matter is explained. And a global stress testing exercise for an insurance company is shown. ​ Stress testing aligned with ORSA Stress testing analysis 2011, 2014 and 2016 in EIOPA Quantitative and qualitative aspects of stress testing Stress testing scenarios Interest rate risk Low long-term interest rate double hit Credit Spread Risk Non-life insurance risk Reinsurers Credit Risk Catastrophe Risk life insurance risk Mortality Events Longevity improvements Liquidity risk Impact on assets and liabilities Impact on SCR and MCR Correlations and copulas to model dependency Stress Testing as a decision-making tool Global Exercise 70: Stress Testing in SAS, R, Excel with VBA and Gephi, includes: Risk Appetite and Business Plan Forecasting of the Income Statement and Balance Sheet capital planning Application of Scenarios and External Shocks Network analysis of main macroeconomic variables Impact on assets and liabilities Impact on SCR and MCR Using  Generative AI in stress testing