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CS2 – Risk Modeling And Survival Analysis

CS2 – This will be examined in a set of 2 papers. CS2A is 3 hours paper based assessment and CS2B is 1 hour 45 minutes’ online exam which R programming along with Statistics and Modelling. Syllabus topics covers – Stochastic processes, Markov chain, Survival models, Graduation, Time series, Loss distributions, Copulas,…

4Modules
21Chapters
160Classes
Register for CS2 Pricing
CS2
Risk Modeling And Survival Analysis
Curriculum

4 modules, 21 chapters, 160 classes

Open a chapter to see every class, or search for a topic.

Module 1Module 2Module 3Module 4

  1. Class 11Stochastic process
  2. Class 12Stochastic sample path and white noise
  3. Class 13Markov property

  1. Class 21Chapman kolmogorov equation
  2. Class 22Transition matrix
  3. Class 23NCD model
  4. Class 241Problem solving stationary probability distribution
  5. Class 25Problem solving estimating transition probabilities
  6. Class 251Problem solving estimating transition probabilities
  7. Class 24Stationary probability distribution

  1. Class 32Survival probabilities
  2. Class 33Joint density function
  3. Class 31Two state markov model
  4. Class 331Problem solving Joint density function
  5. Class 34Maximum likelihood estimation two state model
  6. Class 341Maximum liklihood estimation Poisson Model

  1. Class 41Poisson distribution
  2. Class 411Poisson process
  3. Class 42Generator matrix
  4. Class 43Kolmogorov forward differential equation
  5. Class 44Kolmogorov Backward differential equation
  6. Class 45Holding time and expected time to reach state k
  7. Class 46Maximum likelihood estimation general model
  8. Class 47Problem solving 1 holding time and Integrating factor method Kolmogorov forward differential equation
  9. Class 471Problem solving 2 occupancy probabilities and Kolmogorov forward differential equation
  10. Class 472Problem solving 3 boiler breakdown and Kolmogorov forward differential equation
  11. Class 473Problem solving 4 expected time and Kolmogorov forward differential equation

  1. Class 51Time Inhomogeneous Markov jump process
  2. Class 52Occupancy probabilities
  3. Class 53Kolmogorov’s backward differential equations
  4. Class 54Residual holding times
  5. Class 55Integrated form of Kolmogorov’s backward equations
  6. Class 56Integrated form of Kolmogorov’s Forward equations
  7. Class 57Current holding time and duration dependence- Marriage model
  8. Class 571Current holding time and duration dependence - HSD model
  9. Class 58Problem solving using first principles
  10. Class 581Problem solving using time Inhomogeneous Markov Jump process
Module 1 exam-type questions and solutions

  1. Class 61Survival function
  2. Class 62Force of mortality and uniform distribution of death
  3. Class 63Complete and curtate expectation of life
  4. Class 64Gompertz and Makeham laws of Mortality

  1. Class 71Censoring when estimating the life time - Type of censoring in survival function
  2. Class 72Kaplan - Meier model
  3. Class 73Nelson - Aalen Model
  4. Class 74Problem Solving 1 Reverse calculation of Kaplan - Meier survival model

  1. Class 81Fully Parametric Model
  2. Class 82Cox Proportional Hazard model
  3. Class 83Estimating Regression Parameter -Partial Likelihood Method
  4. Class 84Breslow’s approximation
  5. Class 85Model Fitting - Likelihood Ratio Test
  6. Class 86Problem Solving 1 Compare the Hazard Ratio
  7. Class 861Problem Solving 2 Effects of Interaction Covariates
  8. Class 862Problem Solving 3 Compare the Survival Probability

  1. Class 91Mortality rate calculation - heterogeneity and principle of correspondence
  2. Class 92Central exposed to risk
  3. Class 93Rate Interval - age last birthday and age next birthday
  4. Class 931Problem Solving1 age last birthday and age next birthday
  5. Class 94Rate Interval - age last birthday and age nearest birthday
  6. Class 941Problem Solving 2 age last birthday and age nearest birthday
  7. Class 95Problem Solving 3 age nearest birthday, age last birthday and age next birthday

  1. Class 101Graduation and testing smoothness
  2. Class 102Chi-squared test - over all goodness of fit and its deficiencies
  3. Class 103Standardised deviation test
  4. Class 104Signs test - test for over all Bias
  5. Class 105Cumulative deviations - test for over all Bias
  6. Class 106Grouping of signs test - test for over graduation- runs or clumps
  7. Class 107Serial correlations test - test for over graduation- runs or clumps

  1. Class 211Introduction of machine learning and different stages
  2. Class 212Branches of machine learning- Supervised and Un supervised learning
  3. Class 213Train validation test – Hyper-parameters and cross validation
  4. Class 214Model evaluation - confusion matrix and ROC
  5. Class 215Applications: supervised learning- Penalised generalised linear models
  6. Class 216Applications: supervised learning- Naive Bayes classification
  7. Class 2161Problem solving - Naive Bayes classification
  8. Class 217Applications: supervised learning- Decision Trees and Gini index
  9. Class 2171Problem solving - Decision Trees and Gini index
  10. Class 218Applications: Unsupervised learning- K means clustering
Module 2 exam-type questions and solutions

  1. Class 111Method of Graduation by parametric formula and standard table
  2. Class 112Method of graduation by Spline function
  3. Class 113Solving spline function- natural cubic spline

  1. Class 121Mortality projection by Reduction factor - method based on expectation
  2. Class 122Lee Carter model - method based on extrapolation
  3. Class 123Forecasting with the Lee Carter model
  4. Class 124Forecasting using P-Splines

  1. Class 131Time Series introduction
  2. Class 132Stationarity
  3. Class 133Autocovariance
  4. Class 134Autocorrelation function
  5. Class 1341Partial Autocorrelation function
  6. Class 135Autoregressive model (AR), AR(1), AR(2) and AR(p)
  7. Class 1351Backward shift operator and difference operator
  8. Class 1352Autocorrelation function and Partial Autocorrelation function - Yule walker equations
  9. Class 1353Problem solving Autocorrelation function and Partial Autocorrelation function - Yule walker equations
  10. Class 1354Stationarity AR(p) model - roots of characteristic equation
  11. Class 136Moving average model (MA), MA(1), MA(2) and MA(q)
  12. Class 1361Problem solving Moving average model - Autocovariance and Autocorrelation function
  13. Class 1362Condition for invertibility MA(q) model - roots of characteristic equation
  14. Class 137Autoregressive Moving average ARMA (p,q) - problem solving both stationary and invertible
  15. Class 1371Problem solving ARMA(p,q)- Autocovariance and Autocorrelation function
  16. Class 138The ARIMA (p,d,q) model
  17. Class 139The Markov Property

  1. Class 141Compensating the trend and seasonality - seasonal differencing
  2. Class 1411Method of moving average and seasonal means
  3. Class 142Ljung and Box Portmanteau Test - Identification of White noise
  4. Class 143The Box-Jenkins methodology: step 1 - Identification of model by Akaike’s Information Criterion (AIC)
  5. Class 1431The Box-Jenkins methodology: step 2 - estimation of parameter by method of moments
  6. Class 1432The Box-Jenkins methodology: step 3 - diagnostic test by Counting turning points
  7. Class 144Box Jenkins approach to Forcasting stationary time series ARMA process
  8. Class 1441Box Jenkins approach to Forcasting Non stationary time series ARIMA process
  9. Class 1442Forcasting by Exponential smoothing method
  10. Class 145Multivariate time series model
  11. Class 146Cointegrated time series model

  1. Class 151Loss Distribution - Lognormal Distribution
  2. Class 152Moment generating function MGF and CGF - Gamma Distribution
  3. Class 153Method of moments - Pareto Distribution
  4. Class 154Method of percentile - Weibull Distribution
  5. Class 155Maximum likelihood estimation
Module 3 exam-type questions and solutions

  1. Class 161Extreme Value Theory and block maximum
  2. Class 162Extreme Value Theory - CDF of Limiting Distribution
  3. Class 163Generalised Extreme Value (GEV) distribution
  4. Class 164Frechet-Weibull-Gumbel type GEV distribution
  5. Class 165Generalised Pareto distribution (GPD)
  6. Class 1651Generalised Pareto distribution (GPD)- CDF and PDF
  7. Class 166Measure of tail weight - the existence of moments and limiting density ratios
  8. Class 1661Measure of tail weight - the hazard rate
  9. Class 1662Measure of tail weight - the mean residual life

  1. Class 171Joint Probability Distribution
  2. Class 1711Spearman’s Rho - Measure of Concordance
  3. Class 172Introduction of Copula Function- Sklar’s Theorem
  4. Class 1721Upper and Lower Tail Dependence and Survival Copula
  5. Class 173Co monotonic and counter monotonic Copula - Frechet Hoeffding bounds
  6. Class 174Gumbel Copula
  7. Class 1741Clayton Copula
  8. Class 1742Frank Copula
  9. Class 175Archimedean Copula - Inverse generator function and example of Gumbel Copula
  10. Class 1751Archimedean copula - example of clayton and Frank copula
  11. Class 176Gaussian Copula and Students t Copula

  1. Class 181Proportional Reinsurance
  2. Class 182Excess of Loss Reinsurance
  3. Class 183The Reinsurer’s Conditional claims distribution
  4. Class 184Truncated mean of the normal distribution and Lognormal distribution
  5. Class 185Inflation
  6. Class 186Estimation
  7. Class 187Policy Excess

  1. Class 191Basic Model and Collective Risk Model
  2. Class 192Convolution
  3. Class 193Mean , Variance and MGF of compound distribution of S
  4. Class 194Compound Poisson Distribution
  5. Class 195Compound Binomial Distribution
  6. Class 196Compound Negative Binomial Distribution

  1. Class 201Aggregate claim distributions under proportional reinsurance
  2. Class 202Aggregate claim distributions under individual excess of loss reinsurance
  3. Class 2021Aggregate claim distributions - reinsurer conditional claim under XOL reinsurance
  4. Class 203Individual Risk Model - Compound Binomial Distribution- fixed number of risk
  5. Class 2031Problem Solving Individual Risk Model - Compound Binomial Distribution
  6. Class 204Variability/Uncertainty in a Heterogeneous Portfolio
  7. Class 205Variability/Uncertainty in a Homogeneous Portfolio
  8. Class 206Variability in claim numbers and claim amounts and Parameter uncertainty
  9. Class 208Problem solving 1
  10. Class 209Problem solving 2
Module 4 exam-type questions and solutions

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What’s included

  • Four Module covers entire 21 chapters
  • Each Module have 15 hours classes.
  • Explaining the theory and problems step by step
  • Each sessions notes available bottom of class.
  • Practice of problems available end of the Chapters
  • Evaluating the progress of the subject by assessment

Other subjects

CS1Actuarial Statistics162 classes
CM1Actuarial mathematics247 classes
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