Hursh Gupta

L30 Colab

Initializing search

Stochastic Reserving Framework

Home
Blog
Actuarial
Economics
Learn
ML
Music
Walkthroughs
Songs
Resources
Resume

Hursh Gupta

Stochastic Reserving Framework

Home
Blog
Writings on the Wall
Blog
- Archive
  Archive
  - 2025
- Categories
  Categories
Actuarial
Actuarial
- Exam 5
  Ratemaking & Reserving
  Exam 5
  - Foreward
    Foreward
    
    Tips
    
    AJ
    
    Wording
    
    Careful
  - Insurance Basics
    Insurance Basics
    
    The Insurance Product
    
    Data Aggregation
    
    Pre (While buying)
    Pre (While buying)
    
    Policy Purchasing Process
    Policy Purchasing Process
    
    Workers Comp
    
    Policy Data
    
    Post (While claiming)
    Post (While claiming)
    
    Claims Data
    
    Insurer Performance & Profit
  - Ratemaking
    Ratemaking
    
    Handling Imbalance in FIE
    
    Large Events
    
    One-time changes
    
    Development
    Development
    
    Diagnostic Triangles
    
    Expenses & Profits
    
    Overall Indication
    
    Classification
    Classification
    
    Risk Classification
    
    Univariate Classification
    
    Special Classification
    
    Individual Risk Rating
    
    Implementation
    Implementation
    
    Other Considerations
    
    Werner Appendices
    Werner Appendices
    
    Private Passenger Auto
    
    Homeowners
    
    Medical Malpractice
    
    Workers Compensation
  - Reserving
    Reserving
    
    Reserving Goals
    
    Chain Ladder
    
    Expected Claims
    
    Bornhuetter Ferguson
    
    Cape Cod
    
    Frequency Severity Techniques
    
    Case Outstanding
    
    Berquist-Sherman Techniques
    
    Evaluation
  - Other Components
    Other Components
    
    ALAE
    
    ULAE
- Exam 7
  Advanced Estimation of Claims Liabilities
  Exam 7
Economics
Economics
- PECO
  PECO
  - 301 GRR
    Monetary Theory & Policy
    
    301 GRR
    
    Intro to money and Institution
    Intro to money and Institution
    
    1.1 Introduction to Money
    
    1.2 History of Money
    
    1.3 Circular Flow of Money
    
    1.4 Financial Institutions
    
    Financial System of india
    Financial System of india
    
    2.1 Financial Systems of India
    
    2.2 Central Bank
    
    2.3 Financial Market
    
    2.4 Microfinance Institutions
    
    Week 0
    
    Week 1 Intro to money and Institution
    
    Week 2 Financial System of india
    
    Week 3: Commercial Banks
    
    Week 4: Supply of Money
    
    Week 5: Demand for Money.
    
    Week 6: Monetary Standards.
    
    Week 7 Theory of Money and Business Cycle
    
    Week 8 : NBFI, The Classical System, The Neutrality of Money
    
    Week 9 - Indian and International Monetary System
    
    Week 10 Demonitization
  - 302 RPR
    Time Series Modeling
    
    302 RPR
  - 303 SKG
    Economics of Growth & Development
    
    303 SKG
    
    Endogenous Growth Models
    
    Solow's Model
    
    MRW
    
    GDP Limitations
    
    Globalization
    
    Lewis
    
    Harris Torado
    
    Fei Rami's Model
  - 304 RBN
    Behavioral Economics & Finance
    
    304 RBN
    
    W1 Foundations of Behavioral Economics
    W1 Foundations of Behavioral Economics
    
    Lecture 1 BE What and Why
    
    Lecture 2 BE in Relation to other Sciences & Economics
    
    Lecture 3 Evolution of BE
    
    Lecture 4 The Neoclassical Tradition An Introduction
    
    Lecture 5 Methodology Types of Empirical Studies
    
    W2 Motivation & The Standard Model
    W2 Motivation & The Standard Model
    
    Lecture 6 Intro to Motivation and Hierarchical Needs
    
    Lecture 7 Achievement and Craftmanship Driving Forces in Economic Behavior
    
    Lecture 8 Social Drivers of Economic Decisions
    
    Lecture 9 The Standard Model in Neoclassical Economics
    
    Lecture 10 Axioms & Assumptions of NM
    
    W3 Bounded Rationality & Biases
    W3 Bounded Rationality & Biases
    
    Lecture 11 Utility
    
    Lecture 12 Types of Utility
    
    Lecture 13 Bounded Rationality & Heuristics
    
    Lecture 14 Biases
    
    Lecture 15 Heuristics & Framing Effects
    
    W4 Choice Architecture & Probability
    W4 Choice Architecture & Probability
    
    Lecture 16 Menu Effects
    
    Lecture 17 A few effects & Choice Architecture
    
    Lecture 18 Nudges
    
    Lecture 19 Fundamental of Probability Theory
    
    Lecture 20 Bayesian Updating and Confirmation Bias
    
    W5 Prospect Theory I
    W5 Prospect Theory I
    
    Lecture 21 Prospects & Risk Attitude
    
    Lecture 22 Risk Attitude & Axioms of EUT
    
    Lecture 23 Other Axioms & Violations of EUT
    
    Lecture 24 Anomalies of EUT & Prospect Theory
    
    Lecture 25 Prospect Theory Evaluation
    
    W6 Prospect Theory II & Criticisms
    W6 Prospect Theory II & Criticisms
    
    Lecture 26 The Value Function
    
    Lecture 27 The Weighting Function
    
    Lecture 28 Cumulative Prospect Theory I
    
    Lecture 29 Cumulative Prospect Theory II
    
    Lecture 30 Criticisms of Prospect Theory
    
    W7 Mental Accounting I: Framing & Hedonic Editing
    W7 Mental Accounting I: Framing & Hedonic Editing
    
    Lecture 31 Mental Accounting
    
    Lecture 32 Hedonic Editing Hypothesis
    
    Lecture 33 Hedonic Framing, Acquisition Utility, and Transaction Utility
    
    Lecture 34 Applications of Mental Accounting
    
    Lecture 35 Payment Decoupling & Budgeting
    
    W8 Mental Accounting II: Application & Life-Cycle Theories
    W8 Mental Accounting II: Application & Life-Cycle Theories
    
    Lecture 36 Behavioral & Classical Life Cycle Theories
    
    Lecture 37 Budgeting & Choice Bracketing
    
    Lecture 38 Choice Bracketing & Dynamic Mental Accounting
    
    Lecture 39 Policy Implications of Mental Accounting
    
    Lecture 40 Policy Implications of Mental Accounting II
    
    W9: Intertemporal Choice I: DUM Foundations
    W9: Intertemporal Choice I: DUM Foundations
    
    Lecture 41 Intertemporal Choice Models
    
    Lecture 42 Optimal Consumption in the Two Period Model
    
    Lecture 43 Projection Bias
    
    Lecture 44 Discounted Utility Model
    
    Lecture 45 Features of the Discounted Utility Model
    
    W10: Intertemporal Choice I: Anomalies & Hyperbolic Discounting
    W10: Intertemporal Choice I: Anomalies & Hyperbolic Discounting
    
    Lecture 46 Anomalies in DUM
    
    Lecture 47 Anomalies of DUM & Time Inconsistent Behavior
    
    Lecture 48 Naive Hyperbolic Discounting
    
    Lecture 49 Naive Quasi Hyperbolic Discounting
    
    Lecture 50 Some Special Discounting Cases
    
    W11 Game Theory I: Analytical & Sequential Games
    W11 Game Theory I: Analytical & Sequential Games
    
    Lecture 51 Introduction to Strategic Interactions
    
    Lecture 53 Sequential Game & Types of Games
    
    Lecture 54 Problems with Classical Game Theory I
    
    Lecture 55 Centipede Games
    
    W12 Game Theory II: Behavioral Games & Social Preferences
    W12 Game Theory II: Behavioral Games & Social Preferences
    
    Lecture 56 Problems with Classical Game Theory III
    
    Lecture 57 Introduction of Behavioral Games
    
    Lecture 58 Ultimatum & Dictator Games
    
    Lecture 59 Ultimatum & Public Goods Games
    
    Lecture 60 Trust Games
  - 305 SS
    Financial Risk Management
    
    305 SS
    
    Introduction
    
    Measurement of Risk
    
    Investors Risk Management
    
    Corporate Risk Management
    Corporate Risk Management
    
    Greek Letters
    
    VaR & CaR
    
    Derivatives
    Derivatives
    
    Futures Markets & Central Counter Parties
    
    Hedging Strategies Using Futures
    
    Forwards & Futures Pricing
    
    Forwards vs Futures
    
    Options
    
    Appendix
  - 404 TS
    Time Series and Forecasting
    
    404 TS
    
    L1 Time Series Intro
    
    L2 Examples of Time Series Data
    
    L3 Stationarity in Time Series
    
    L4 Weak vs strong stationarity
    
    L5 Colab
    
    L6 Time Series Decomposition
    
    L7 Basic Time Seires Processes
    
    L8 Autocorrelation and the Partial Autocorrelation Functions
    
    L9 ACF and PACF for Some Time Series Processes
    
    L10 Colab
    
    L11 Non stationary Time Series
    
    L12 Seasonality and its Features
    
    L13 Cyclicity and Test for Stationarity
    
    L14 Seasonality and SARIMA Model
    
    L15 Colab
    
    L16 Model Identification
    
    L17 Model Estimation
    
    L18 Diagnostic Checking 1
    
    L19 Diagnostic Checking 2
    
    L20 Colab
    
    L21 Forecasting Basics
    
    L22 Measuring Forecast Accuracy
    
    L23 Smoothing Techniques (SMA,EMA)
    
    L24 Double and Triple Exponential Smoothing
    
    L25 Colab
    
    L26 Persistent and Long Memory Processes
    
    L27 ARFIMA Processes
    
    L28 Hurst Exponent Estimation under ARFIMA
    
    L29 Estimation under ARFIMA
    
    L30 Colab
    
    L31 Multivariate Time Series Analysis
    
    L32 Cross covariance and Cross correlation
    
    L33 Some Specific Multivariate Time Series Models
    
    L34 Further Extensions and Use Cases
    
    L35 Colab
    
    L36 Cointegration & Further
    
    L37 Error Correction Models
    
    L38 Tests for Cointegration
    
    L39 Causality Tests and Further
    
    L40 Colab
    
    L41 Frequency Domain Analysis
    
    L42 Spectral Representation of a series
    
    L43 Spectral Density Estimation
    
    L44 Numerical Examples and Further
    
    L45 Colab
    
    L46 Stochastic Volatility Modelling
    
    L47 ARCH Models
    
    L48 ARCH LM Test & GARCH Models
    
    Extensions of the GARCH Model
    
    L50 Colab
    
    L51 Nonlinear Time Series Models
    
    L52 Regimes and Nonlinear Models
    
    L53 Nonlinear Model Extensions
    
    L54 Markov Switching Models
    
    L55 Colab
    
    L56 Machine Learning in TS
    
    L57 Linear Regression for TS
    
    L58 Other ML Models for TS
    
    L59 Neural Networks for TS
    
    L60 Colab
Learn
Learn
- Statistics
ML
Machine Learning
ML
- Books
  Books
  - Handson ML
    
    Handson ML
- Cluster
- Conventions
- How to start?
- Ideas
- Impute
- Linear Regression Model
- Linear model
- Metrics
- Model selection
- Neighbors
- Pandas
- Pipeline
- Preprocessing
- Python Tips
- Sklearn
- Tree
- References
  References
  - Statistical techniques
    
    Statistical techniques
    
    K-means clustering
  - Sklearn & Pandas
    Sklearn & Pandas
    
    Custom Classes in sklearn
    
    Package Notes
    
    Scikit Learn Notes
Music
Music
Walkthroughs
Walkthroughs
Songs
Songs that clicked
Resources
Worth Sharing
Resume