This document summarizes a presentation given by Arthur Charpentier at the Rencontres Mutualistes conference in November 2018. The presentation discussed actuarial pricing, segmentation, and mutualization in insurance. It defined key concepts of actuarial pricing like rate classes and introduced tools to evaluate pricing models like Lorenz curves. It also described a field experiment conducted on "actuarial pricing games" to understand how models influence pricing.
The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transp...ijtsrd
In this paper a new ranking method is proposed for finding an optimal solution for intuitionistic fuzzy unbalanced transportation problem, in which the costs, supplies and demands are octagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. Dr. P. Rajarajeswari | G. Menaka "The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transportation Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31675.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31675/the-new-ranking-method-using-octagonal-intuitionistic-fuzzy-unbalanced-transportation-problem/dr-p-rajarajeswari
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (P...AMIDST Toolbox
Maximum a posteriori (MAP) inference is a particularly complex type of probabilistic inference in Bayesian networks. It consists of finding the most probable configuration of a set of variables of interest given observations on a collection of other variables. In this paper we study scalable solutions to the MAP problem in hybrid Bayesian networks parameterized using conditional linear Gaussian distributions. We propose scalable solutions based on hill climbing and simulated anneal- ing, built on the Apache Flink framework for big data processing. We analyze the scalability of the solution through a series of experiments on large synthetic networks.
Full text paper: http://www.jmlr.org/proceedings/papers/v52/ramos-lopez16.pdf
Risk Management and Statistics: Reflections on a Large Asset PortfolioHein Aucamp
My experience with a large Rio Tinto building asset database led me to develop new directions of thought about uncertainty and risk management in data quality for asset management.
First, a renewed relationship between philosophy and mathematics has important methodological implications for educational research. Though it has largely been forgotten today, philosophy is fundamentally mathematical. We need to revive, reinvigorate, and reinvent the ancient connection between philosophy and mathematics because something vitally important has been lost.
Second, we’re going to apply what we learn from the connection between mathematics and philosophy to educational measurement. Not all quantitative methods are reductionistic. And some might be surprised to learn how much good math there can be in qualitative methods.
Third, with our new appreciation for mathematically meaningful measurement in hand, we are going to bring out the whole range of relevant applications. We are witnessing the birth of a new human science of caring that will support a new era of socially and environmentally sustainable economic activity.
Talk at the modcov19 CNRS workshop, en France, to present our article COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
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2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
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Lecture slide titled Fraud Risk Mitigation, Webinar Lecture Delivered at the Society for West African Internal Audit Practitioners (SWAIAP) on Wednesday, November 8, 2023.
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There is no set date for when Pi coins will enter the market.
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BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
1. Arthur Charpentier, Rencontres Mutualistes - November 2018
Segmentation et mutualisation,
les deux faces d’une mˆeme pi`ece ?
A. Charpentier (Universit´e du Qu´ebec `a Montr´eal)
Rencontres Mutualistes, Beaune, November 2018.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 1
2. Arthur Charpentier, Rencontres Mutualistes - November 2018
A. Charpentier (Universit´e du Qu´ebec `a Montr´eal)
Professor Mathematics Department, Universit´e du Qu´ebec `a Montr´eal
previously Econ. Dept, Universit´e de Rennes & ENSAE Paristech
actuary in Hong Kong, IT & Stats FFA
director Data Science for Actuaries Program, Institute of Actuaries
PhD in Statistics (KU Leuven), Fellow of the Institute of Actuaries
MSc in Financial Mathematics (Paris Dauphine) & ENSAE
Research Chair :
ACTINFO (valorisation et nouveaux usages actuariels de l’information)
Editor of the freakonometrics.hypotheses.org’s blog
Editor of Computational Actuarial Science, CRC
Author of Math´ematiques de l’Assurance Non-Vie (2 vol.), Economica
@freakonometrics freakonometrics freakonometrics.hypotheses.org 2
3. Arthur Charpentier, Rencontres Mutualistes - November 2018
Insurance, “segmentation” & “mutualisation”
Insurance is the contribution of the many
to the misfortune of the few
• what is actuarial pricing ?
• why a “spirale de la segmentation” ?
• how to compare actuarial models ?
• field experiment : pricing games
@freakonometrics freakonometrics freakonometrics.hypotheses.org 3
4. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
Consider some portfolio with n insured
@freakonometrics freakonometrics freakonometrics.hypotheses.org 4
5. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
Consider some portfolio with n insured
@freakonometrics freakonometrics freakonometrics.hypotheses.org 5
6. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
The n insured have heterogeneous unobservable risk factors ω1, · · · , ωn
@freakonometrics freakonometrics freakonometrics.hypotheses.org 6
7. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
Nevertheless, they have characteristics x1, · · · , xn (that can approximate ωi’s)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 7
8. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
e.g. x1 can be the color of the car, x2 can the gender, etc.
Those characteristics are observable, a priori.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 8
9. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
Actuaries can use a regression tree to create homogeneous rate classes (or GLMs)
Here classes are based on x1 (only)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 9
10. Arthur Charpentier, Rencontres Mutualistes - November 2018
Actuarial Pricing in a Nutshell
Actuaries can use a regression tree to create homogeneous rate classes (or GLMs)
But classes can be based on x1 and x2 – see Bailey (1963)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 10
11. Arthur Charpentier, Rencontres Mutualistes - November 2018
Risk Transfert without Segmentation
Insured Insurer
Loss E[S] S − E[S]
Average Loss E[S] 0
Variance 0 Var[S]
All the risk - Var[S] - is kept by the insurance company.
Remark: all those interpretation are discussed in Denuit & Charpentier (2004).
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12. Arthur Charpentier, Rencontres Mutualistes - November 2018
Risk Transfert with Segmentation and Perfect Information
Assume that information Ω is observable,
Insured Insurer
Loss E[S|Ω] S − E[S|Ω]
Average Loss E[S] 0
Variance Var E[S|Ω] Var S − E[S|Ω]
Observe that Var S − E[S|Ω] = E Var[S|Ω] , so that
Var[S] = E Var[S|Ω]
→ insurer
+ Var E[S|Ω]
→ insured
.
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13. Arthur Charpentier, Rencontres Mutualistes - November 2018
Risk Transfert with Segmentation and Imperfect Information
Assume that X ⊂ Ω is observable
Insured Insurer
Loss E[S|X] S − E[S|X]
Average Loss E[S] 0
Variance Var E[S|X] E Var[S|X]
Now
E Var[S|X] = E E Var[S|Ω] X + E Var E[S|Ω] X
= E Var[S|Ω]
perfect pricing
+ E Var E[S|Ω] X
misfit
.
@freakonometrics freakonometrics freakonometrics.hypotheses.org 13
14. Arthur Charpentier, Rencontres Mutualistes - November 2018
How can we visualize the goodness of a model ?
Source : https://www.progressive.com/jobs/analyst-program/
@freakonometrics freakonometrics freakonometrics.hypotheses.org 14
15. Arthur Charpentier, Rencontres Mutualistes - November 2018
Constructing the (pseudo)-Lorenz curve
Sort the n risks according to the model m(x1) ≥ m(x2) ≥ · · · m(xn)
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16. Arthur Charpentier, Rencontres Mutualistes - November 2018
Constructing the (pseudo)-Lorenz curve
On the x-axis, xi = i/n, on the y-axis, yi =
i
j=1 yj/
n
j=1 yj
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17. Arthur Charpentier, Rencontres Mutualistes - November 2018
Constructing the (pseudo)-Lorenz curve
Connect points (xi, yi)
@freakonometrics freakonometrics freakonometrics.hypotheses.org 17
18. Arthur Charpentier, Rencontres Mutualistes - November 2018
Constructing the (pseudo)-Lorenz curve
see Frees, Meyers & Cummins (2014).
@freakonometrics freakonometrics freakonometrics.hypotheses.org 18
19. Arthur Charpentier, Rencontres Mutualistes - November 2018
Practice of (pseudo)-Lorenz curves
What if m and m are not perfectly correctly correlated...?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 19
20. Arthur Charpentier, Rencontres Mutualistes - November 2018
Practice of (pseudo)-Lorenz curves
What if m and m are not perfectly correctly correlated...?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 20
21. Arthur Charpentier, Rencontres Mutualistes - November 2018
Practice of (pseudo)-Lorenz curves
What if m and m are not perfectly correctly correlated...?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 21
22. Arthur Charpentier, Rencontres Mutualistes - November 2018
Practice of (pseudo)-Lorenz curves
What if m and m are not perfectly correctly correlated...?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 22
23. Arthur Charpentier, Rencontres Mutualistes - November 2018
What is the “average” model ?
What is this “average pricing” ?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 23
24. Arthur Charpentier, Rencontres Mutualistes - November 2018
Can it be worst than the “average” model ?
Is it a lower bond ? Is it possible to be below that curve ?
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25. Arthur Charpentier, Rencontres Mutualistes - November 2018
What is in the upper corner ?
What is the upper bond ? Ex-post pricing...
@freakonometrics freakonometrics freakonometrics.hypotheses.org 25
26. Arthur Charpentier, Rencontres Mutualistes - November 2018
How to understand this (pseudo)-Lorenz curve ?
Is there a continuity between mutualization and hyper-segmentation ?
@freakonometrics freakonometrics freakonometrics.hypotheses.org 26
27. Arthur Charpentier, Rencontres Mutualistes - November 2018
Insurance, Risk Pooling and Solidarity
Consider flood risk
One can look at the “Lorenz curve”
@freakonometrics freakonometrics freakonometrics.hypotheses.org 27
28. Arthur Charpentier, Rencontres Mutualistes - November 2018
Field experiment: the actuarial pricing games
Actuarial pricing is data based, and model based
To understand how model influence pricing
we ran some actuarial pricing games
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