Regression Models and Loss Reserving

Jul 22, 2012

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2. Outline. Introductory ExampleLinear (or Regression) ModelsThe Problem of Stochastic RegressorsReserving Methods as Linear ModelsCovariance. 3. Introductory Example. A pilot is flying straight from X to Y. Halfway along (s)he realizes that (s)he's ten miles off course. What does (s)he do?. .

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Regression Models and Loss Reserving

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Loss Reserving in Japan

The Institute of Actuaries of Japan. Loss Reserving in Japan. Junji FURUKI FIAJ. Agenda. Non-life market in Japan Regulation and Accounting Loss reserving (before revisions) Loss reserving (after revisions) Role of Actuaries. Non-life market in Japan. World premium volume ranking.

633 views • 32 slides

Regression Models

Regression Models. Chapter 4. To accompany Quantitative Analysis for Management , Tenth Edition , by Render, Stair, and Hanna Power Point slides created by Jeff Heyl. © 2009 Prentice-Hall, Inc. . Learning Objectives. After completing this chapter, students will be able to:.

1.46k views • 71 slides

Regression Models and Loss Reserving: Decision Points in Model Design

Regression Models and Loss Reserving: Decision Points in Model Design . Casualty Loss Reserve Seminar September 10-12, 2001 Dave Clark American Re-Insurance. Preliminary Question. Why create a regression model? Smooth out development pattern Impose “objectivity” on reserving process

518 views • 28 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 8 – Multicollinearity, Diagnostics. Multiple Regression Models. Multicollinearity

601 views • 35 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 7 – Multiple Regression Analysis. Model Assumptions.

769 views • 54 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 3 – Model Fit and Correlation. Correlation and Linear Association. Height (inches) and Income ($/mo.) in first post-MBA

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Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models . Part 5 – Elasticities and Functional Form. Linear Regression Models. Model building Linear models – cost functions

788 views • 44 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Statistics and Data Analysis. Part 10 – Qualitative Data. Modeling Qualitative Data. A Binary Outcome Yes or No – Bernoulli Survey Responses: Preference Scales

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Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 1 – Simple Linear Model. Theory. Demand Theory: Q = f(Price) “The Law of Demand” Demand curves slope downward

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Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models . Part 4 – Prediction. Prediction. Use of the model for prediction Use “x” to predict y based on y = β 0 + β 1 x + ε Sources of uncertainty

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Loss Reserving

Anatomy of a claim 12/15/99 Auto accident 12/20/99 Insured reports accident to agent 1/7/00 Claim recorded 2/3/00 $10,000 reserve set 1/8/01 Pay $3000 medical expenses; offer $15,000 settlement; $15,000 reserve set

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Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 6 – Multiple Regression. Multiple Regression Agenda. The concept of multiple regression Computing the regression equation

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Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 2 – Inference About the Regression. The Linear Regression Model. 1. The linear regression model

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Regression Models & Loss Reserve Variability

Regression Models & Loss Reserve Variability. Prakash Narayan Ph.D., ACAS 2001 Casualty Loss Reserve Seminar. Regression Models and Loss Reserve Variability. Range for Loss Reserve:. Ultimate loss will be different from any estimate

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Capital Models and Their Relationship With Loss Reserving

Capital Models and Their Relationship With Loss Reserving. Joanne Balling, ACAS Director. Standard & Poor’s Capital Adequacy Ratio. Asset-Related Risk Charges (C-1). Credit-Related Risk Charges (C-2). -. -. Total Adjusted Capital. Underwriting Risk (C-3). Reserve Risk (C-4 ).

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Regression Models

Regression Models. Fit data Time-series data: Forecast Other data: Predict. Use in Data Mining. One of major analytic models Linear regression The standard – ordinary least squares regression Can use for discriminant analysis Can apply stepwise regression

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Loss Adjustment Expense Reserving

Loss Adjustment Expense Reserving. Adam D. Hartman, ACAS Casualty Loss Reserve Seminar September 14, 1999. Purpose . To introduce basic reserving methodologies for loss adjustment expenses that are not tracked on an accident year basis

691 views • 53 slides

Regression Models

Population Deterministic Regression Model Y i =  0 +  1 X i Y i only depends on the value of X i and no other factor can affect Y i . Population Probabilistic Regression Model Y i =  0 +  1 X i +  i , i = 1, 2, ... , n. E(Y |X i )=  0 +  1 X i ,

444 views • 33 slides

Regression Models

Regression Models. Chapter 4. Learning Objectives. After completing this chapter, students will be able to:. Identify variables and use them in a regression model Develop simple linear regression equations from sample data and interpret the slope and intercept

759 views • 71 slides

Regression Models

Regression Models. Residuals and Diagnosing the Quality of a Model. Visualizing Regression Models. Criteria of quality. Residuals (or what we don’t explain) should be “noise” Independent variables measure different phenomena We haven’t left out something important.

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Loss Reserving in Mexico

Loss Reserving in Mexico. Eduardo Esteva Minneapolis, Minnesota September 18, 2000. Background of IBNR Estimation in Mexico Methodology Accounting Rules Data Prevision and Catastrophic Reserves Role of the Actuary Final Comments. Index.

292 views • 27 slides

Regression Models and Loss Reserving: Decision Points in Model Design

Regression Models and Loss Reserving: Decision Points in Model Design. Casualty Loss Reserve Seminar September 10-12, 2001 Dave Clark American Re-Insurance. Preliminary Question. Why create a regression model? Smooth out development pattern Impose “objectivity” on reserving process

300 views • 28 slides

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Regression Models and Loss Reserving

Regression Models and Loss Reserving

Presentation Transcript

Loss Reserving in Japan

Regression Models

Regression Models and Loss Reserving: Decision Points in Model Design

Regression Models

Regression Models

Regression Models

Regression Models

Regression Models

Regression Models

Regression Models

Loss Reserving

Regression Models

Regression Models

Regression Models &amp; Loss Reserve Variability

Capital Models and Their Relationship With Loss Reserving

Regression Models

Loss Adjustment Expense Reserving

Regression Models

Regression Models

Regression Models

Loss Reserving in Mexico

Regression Models and Loss Reserving: Decision Points in Model Design

Regression Models & Loss Reserve Variability