|
|
Michael Millken
|
It is not fanciful to link the credit crunch of 2007 with the savings and loan problems two decades earlier. Both bubbles were related to home mortgage financing, and the first bubble turned destructive by seeking money to keep itself going. If dammed-up surpluses of the Middle East and China could be made available to American mortgage lenders, there seemed to be ample demand for them. Furthermore, while Michael Millken is mostly known for his prison sentence, he had nevertheless made an important observation. Risky mortgages were generally overpriced. That is, the aggregate extra cost of subprime defaults was appreciably less than the aggregate extra interest being charged for them. If some way could be found to make the risk premium more appropriate to the actual risk, home mortgages would get permanently cheaper, and mortgaging profits would likely be gratifying. Mortgages needed a better system for establishing appropriate interest rates, and they needed more of that underemployed wealth of the Orient. Derivatives suggested themselves as a solution for both issues.
|
|
Chairman Alan Greenspan
|
The unaccustomed wealth of Asia and the Persian Gulf was put under heavy pressure to migrate to America by lack of local investment opportunities but was bottled up by rudimentary banking systems in the developing world. As ways were found to get around obstacles for exporting this money, the danger increased of "asset bubbles" inflating whatever they touched, for example, the dot.com stocks in 2001. The pressure indeed needed to be deflated, but carefully. Furthermore, certain accords reached in Basle around 1982 made it even easier for banks to issue loans, while the favored tax treatment of interest from residential real estate loans directed lending to home mortgages. Indeed, the calculated cost comparison between buying a home or renting it had once remained identical for fifty years but began to diverge in 1982. By 2007, it was significantly more expensive to buy than to rent, even though many analyses suggested a housing surplus existed, particularly in California and the Southwest. While the interest-rate premise was correct, the earlier campaign against "redlining" probably did encourage loans to people who could not afford the house, and there was momentum to this idea. But the most obvious stimulus to continued high-priced home purchasing, in the face of a growing over-supply, was the momentum of abundant cheap money. To mop up a growing housing surplus, initially low "teaser" interest rates were offered for ARMs, or adjustable rate mortgages which could abruptly adjust upward after a few years. A growing problem was being set up to go over a cliff. Chairman Alan Greenspan fretted at his seat on the Federal Reserve Board that it was difficult -- a conundrum -- why market interest rates for long term borrowing did not rise enough to put a stop to this. In retrospect, it seems likely the risk premium had long been too high and was now reaching for more appropriate levels. Derivatives were the main instrument for bringing rates down, and they did it with breathtaking speed, perhaps overshooting in the process. As is often the case with innovation, the risk of failure was overemphasized, while the dangers of success received little attention.
Credit derivatives can also be viewed as a form of insurance, protecting the lender if the borrower defaults. That doesn't sound like a bad thing. True, all insurance creates a "moral hazard" that encourages risky behavior by reducing its pain. No one, it is said, washes a rental car. But in a housing surplus, the insurance protection allows banks to take more chances in marginal situations, using up the surplus. Young folk is allowed to get started in life; the poor are allowed to enjoy the American dream. Unfortunately, some will abuse the privilege by buying speculative houses in a rising market, and "flip" them. Many will buy bigger houses than their income can support. Some, who should more wisely rent because their employment prospects are not secure, will be tempted to buy. All of these considerations are wrapped up in the interest rate the lender charges, so eventually, interest rates will rise to a level that anticipates -- discounts -- them. Interest rates did not rise. The old levels of risk "premium" did not reappear.
It seems now that increased demand stimulated by derivatives was not resisted by a shrinking supply of money, with a balance maintained by the adjustment in interest prices. Indeed, a good even brilliant idea was crippled by a series of responses to the puzzling environment. Banks learned to sell pretty much any mortgage as quickly as it was created; after that, the extra risks were none of their concern. It has been suggested that banks be required to retain a portion of any loan they originate but to do so would exhaust the bank's lending capacity during a bonanza of business. Standards for a bank's lending capacity are set by the Federal Reserve, as a multiple of their retained profits or reserves. Those capacity limits had been relaxed by the Basle accords, but only on condition, the banks restricted themselves to AAA-rated loans. This will turn out to be a critical point because it put unwarranted reliance on the opinion of the rating agencies, and in any event, led to "tranches".
|
|
Federal Reserve Building
|
Here's how things roughly went. Investment banks learned to buy up and combine great bunches of these mortgages into a bundle. The bundle was then sliced into tranches of lesser bundles, attempting to sort out the bundles by their credit rating. Elegant mathematical formulas were brought forward which did a fairly good job of sifting the potentially weak loans away from another bundle that was largely risk-free. Those better sub-bundles, thought to warrant a AAA rating, were then sold to institutions who were restricted to them by the Basle accords but paid a lower interest return than the mortgage pool they came from. That was already an uncomfortably low rate by historical standards, now made lower. However, in view of its superior quality with default risk removed, it could be bought with borrowed money, eventually creating an adequate but leveraged return after costs. The debt was thus piled on debt, and the process repeated with exaggeration on the next lower quality tranche, the AA paper. And so on down to the lowest grade, which was thought to contain all or almost all of the default risk in the whole mortgage pool. People who bought the lowest tranche were real risk takers, experts who knew what they were doing, receiving a premium interest return to do it. Because this process was thought to create a sophisticated assessment of the true risk in the bundle, it was thought it would justify lower rates for everybody, squeezing out the unnecessary cushion of comfort. It was a plausible idea, and if it worked, it would be a brilliant one. But it had a big unrecognized flaw. It assumed that essentially all of the defaults would occur at the bottom of the pile, or possibly at the next higher level. There would be no defaults in the AAA level until all of the lower tranches had been wiped out -- an almost inconceivable economic calamity.
Ingenuity was then carried to yet another level. Credit derivatives are a form of insurance against default, but there was a more traditional form already in existence. Several so-called monoline companies offer insurance against default, backed by the enormous strength of pooled resources of a number of the largest strongest financial institutions in the world. The rating agencies assess their strength as AAA, the highest quality. Now, it was reasoned, if a tranche of mortgages rated AA by the agencies were insured by an insurance company, itself rated AAA, then the effective risk to the investor was really only AAA, or negligible. Alchemy. The lead was turned into gold. Unfortunately, when the panic finally hit, monoline insurance stock which was considered rock-solid at $80 a share, was soon selling for $15. The flaw in all this was that the rating of the bond was based on the credit rating of the borrowers. No one had supposed that people who were quite able to pay their debts would walk away from them. When home prices fell only ten or so percent, many of them fell below the cost of the borrowed-up mortgage. Instead of feeling horror at defiling their credit reputation, many of these prosperous borrowers regarded foreclosure as simply a business decision. The protection of monoline default insurance was trivialized when one of the smartest investors in the world, Warren Buffett, announced he proposed to form a company to ensure municipal bonds, and only municipal bonds, against default. Since that might strip away what had become the only profitable portion of the monoline portfolio, the prospect of such crippled companies paying housing claims would be bleak. Pseudo AAA tranches were now clearly back to being AA, and even real AAA tranches were under a cloud. All of this was not anticipated.
There remain two other questionable developments in this colorful adventure: the role of the rating agencies, and off-the-books behavior by the regulated mortgage originators.
Everyone agrees there is a tangle about the rights and responsibilities which begin when childhood begins. We wish to avoid this issue as much as we can, but partitioning the costs of the average child requires stating some point or other, as its beginning.
Keeping the practicalities of paying for it in mind, we hope no one will object if we say childhood begins, the day you are born.
We next consider the healthcare costs of children, from birth until age 25, linked with the costs of the elderly, for a reason. One of the points made in this book as an arguable alternative to the present employer-based system is to keep it within your family, rather than tax other people as a class. However, although the system now claims to begin with the first full-time employment, a newborn provokes about $18,000 of medical expense including obstetrics before that, right from the beginning, before the child can even feed him or her self. Age 26 might be a reasonable place to begin self-support, not because of tax deduction, but since that's typically the age group with the lowest health costs. Even that starting age has its problems because the parents are not much more accustomed to managing finances than the child is. The central question remains the same. Who is to supply the $18,000?
The Progressive movement started the idea of "family plans" about a century ago, but Henry J. Kaiser is credited with noticing an employer's gift of the insurance would supply two tax deductions, the employer's and the employee's, during World War II. That "reduced" the cost of health insurance by at least 50% (for the employer and employee), but it made a married employee seem more expensive than unmarried ones, made healthcare seem a free cost to the recipients and therefore boosted its cost, introduced a religious note by discouraging multiple pregnancies, and was unfair to unemployed or self-employed persons who were excluded from getting the gift. It is impossible to determine how much this new twist distorted employment and medical prices, but by suspicion the unfairness was major. It surely prompted a response, and this is one. If a big business can get tax deductions for giving away healthcare, why can't everyone else?
So it is proposed -- hold your breath -- HSAs give the equivalent of $18,000 at the death of an older relative, to a newborn's HSA at birth. The average childbearing mother has 2.1 children, which works out to one grandchild per grandparent, and helps smooth out the cost of multiple children. Because births and deaths cannot be forced to coincide, some sort of fund has to be created to make all this come out fairly, but the result should equal a zero balance between two generations. And because everyone who is alive has somehow already paid his birth cost, there is less urgency to begin this feature at the onset of the program--it becomes a feature of the transition. And, going back to the pros and cons of including Medicare premiums in the compounding, the more surplus is generated, the shorter the transition period should become. Ultimately, of course, the cost of health insurance for the mother is reduced; but the main beneficiary of the transfer is whoever is now paying for the mother's health insurance. That would sometimes be the father, sometimes the employer, and sometimes the Affordable Care insurance.
A few children are cursed with horrendous medical bills, which quite often predict lifetime disabilities. For the most part, however, childhood medical costs are pretty small. It would seem to produce an < b>ideal configuration for insurance, leading to mostly small premiums, affording a lot of protection against a fearful risk which is nevertheless relatively uncommon. However, a newborn is unable to walk, talk or feed him or her self, beyond even mentioning his or her lack of savings. Parents are now expected to pay such bills, and when they are very large it is common for grandparents to help out. So it sort of fits the common situation to group the two dependent periods of life (childhood and old age) together, as a continuous loop skirting the income-producing period of life entirely. The underlying purpose is to shift overfunded money to an underfunded time, compensating the childhood cohort for the fact that compound interest appreciates very little during childhood, but very greatly toward the end of life. This configuration fairly shouts "risk pool" but requires legislative action because it is more a metaphor than legal reality. It serves to explain to people why we have struggled to close the loop for twenty or more years because what is true for children is definitely not true for Medicare, where the main costs congregate. To meet the disparity, we chose to employ patchwork solutions for a single generation, counting on the enhanced generosity of the public for disabled children to meet the major expense. This appearance contrasts sharply with the deceptively low average cost of ordinary childhood healthcare. The only danger is for this temporary expedient to become a career.
Please note the fiscal dilemma. Even if subsidies or gifts provided a $100 nest egg to start health savings account at birth, 2.5 doublings at 7% would only create a fund of $525 by age 25. That's not nearly enough to fund healthcare for individuals at risk of auto accidents and HIV while trying to pay for college, home mortgages or the like. By contrast, $100 a year for forty years might well pay for all of Medicare while retaining leverage of eight dollars out, for one dollar in. Adding $1400 a year for 20 more years would be much better, at 80 to one. For lucky people, $8127 might work, but its safety margin is too narrow for launching a lifetime medical system. The actual plan proposed is a complicated variant of this approach. As the reader will see, there will be ample funds available for a lump sum donation, once the system has closed the loop, because just 8.5 extra doublings from the beginning of lifetimes to the end of other lifetimes, without supplementation, should silence any remaining doubts, at 256 to one leverage.
Once it gets underway, the two-generation process is very simple, requiring only a few amendments to existing legislation. Extend the age limits of catastrophic high-deductible insurance down to the date of birth, and allow the premiums to compound up to the date of death or 104, the length of a perpetuity. After that, allow surplus Health Savings Accounts of the parents or grandparents to flow over to the HSAs of the child, and allow surplus funds of grandparents and designated others to be transferred (from the date of death of one, to the date of birth of the other) via the HSAs of both. Gifts of this sort might even become a popular item in obituaries, in lieu of flowers.
Springing such a radically different proposal on an unprepared public is potentially to provoke ribald rejection, so it's gradually introduced here as a challenge to provoke alternative proposals. At the moment, I don't see what they would be. We are combining the advantages of two systems, for the young and for the old, which separately they cannot achieve, except through the socially threatened but biologically inescapable, concept of "family".
Quick Analysis of Financial-Industry
Big-Data Analytic Needs
DRAFT George Fisher July 24, 2017
Abstract
Databricks intends to create a Finance Vertical position to support the Sales and SA teams when working with financial-industry organizations. This article attempts to describe the structure of the worldwide financial industry, who the major players are and what their needs might be in the context of Apache Spark and Databricks offerings.
Contents
1 Executive Summary 2
2 Introduction 2
3 Risk Mitigation 3
4 Opportunity Discovery 5
5 Finance-Industry Sponsored Kaggle Contests 6
6 Spark and Finance on YouTube 11
7 APPENDIX 15
1 Executive Summary
The opportunities in the finance sector lie on a wide spectrum: at one
end are the quant funds for whom large-scale analytics are the entire
business, at the other, are traditional depositories for many of whom a
daily batch cycle and a quarterly book closing have long sufficed.
Quite often both extremes exist in the same company.
For this entire spectrum the easy-to-use, streaming, multi-source,
big-data analytics offered by Databricks can offer advantages.
Perhaps with quick adoption by the quants and slower adoption by the others. Early adoption may involve a lot of discovery but a growing collection of proven use cases will ease later sales.
1
.
streaming will supplant batch
.
predictive analytics will replace BI
.
easy multi-sourcing can unite stove pipes
.
pooling can dramatically reduce operational complexity and cost
In addition, in the larger companies, the pressure to comply with data-
related regulations company-wide has become almost overwhelming and
nearly all are struggling with multitudes of incompatible systems that
A spark might unite.
2 Introduction
The finance industry is vast, far too large and diverse to make a
comprehensive enumeration of all the functions performed or of the firms that perform them. The Economist Intelligence Unit [14]
might be a good source, to begin with for such a survey.
The Appendix of this report contains lists of the major financial organizations grouped by function starting on page 15.
The questions of interest to Databricks are (1) which finance firms are
most likely to benefit from the manipulation and analysis of large
datasets and (2) what are the types of manipulation and analysis of
interest?
The two main concerns for the finance industry are:
.
Risk Mitigation
.
Opportunity Discovery
1 I wonder if the entirely cloud-based solution offered by Databricks
does not leave a lot on the table given the pervasiveness of
proprietary datacenters in this world. IBM mainframes, at that.
3 Risk Mitigation
[7]
Simply put, risk mitigation means don’t lose money, don’t go out of business and don’t go to jail.
Risk Categories
1. Business Risk Risks undertaken by the business
itself to maximize share- holder value and profits. For example: the
cost to launch a new product. Risk mitigation takes the form of
competent management controls.
2. Exogenous Risk Political upheaval, natural
disaster, economic disrup- tion. Insurance is the most-common risk
mitigation tool in these cases.
3. Financial Risk Financial risk arises from
volatility in equities, deriva- tives, currencies, interest rates etc.
In the case of financial firms these risks are also Business Risks
since finance is the business.
.
Market
Risk
Changes in prices, their magnitude, direction and volatility.
.
Credit Risk
The effect of counter-party default or the repercussions of providing
services to bad actors.
.
Liquidity
Risk
The inability to make timely payment. Margin calls often precipitate
this when illiquid securities cannot be sold or col- lateralized.
.
Operational Risk
Failures of judgment, integrity, controls, proce- dures or technology.
Cyber Security
An aspect of Operational Risk that gains clar- ity at senior levels
with every report of the losses incurred and chaos engendered by
widespread sophisticated hacking.
Financial-firm financial-risk mitigation is a field of study unto
itself. For example, there is a rigorous, multi-partFinancial Risk Manager (FRM) Certification [5] created by Global Association of Risk Professionals (GARP).
4. Regulatory Compliance While perhaps not a risk per
see this is a huge concern to financial firms, particularly since the
Financial Crisis of a decade ago and the rules promulgated as a
response.
For example, one of the main tenets of BCBS 239 [15] is that all
‘material risk data’ must be automatically aggregated and analyzed across the entire banking group on a near-real-time basis while facing severe economic stresses. Multitudes of incompatible systems are a huge barrier.
[11]
4 Opportunity Discovery
If Risk Mitigation is Operations, Opportunity Discovery is Research
& Devel- opment.
An inexhaustive list:
•
F
undamental
Analysis
The study of the financial characteristics of in- dividual firms,
seeking undiscovered value. Warren Buffett is the world’s most-famous
fundamental analyst.
•
Macro
The study of economy-wide signals. George Soros’ famous short of the UK
Pound is an example [12]. The ‘Big Short’ of 2007-2008 is another [22].
•
Relative
The study of relative movements of securities. Long/Short hedge funds
are an example.
•
T
ec
hnical
Analysis
The study of trendlines.
•
Quantitative
Analysis
The intersection of big data and machine learn- ing. Jim Simons’
Renaissance Capital [16] is the most successful example I know of but
there are many others; some are listed in the appendix be- ginning on
page 17. Some Kaggle contests focused on this, see Section
5.
•
Product Development
Swaps are an example of building a product to meet very specific
customer needs. Even more sophisticated products are possible with
analytical support using all available data.
•
Customer Enhancement
Using machine learning to reduce customer churn; using predictive
analytics for product-customer targeting; consis- tent customer support
across multiple access channels; etc. . . . using Ama- zonian
techniques in a banking environment to take on the characteristics of
the fintechs.
•
Cost Control
Route optimization for filling ATMs; redundant process identification;
risk reduction not just as a regulatory requirement, but as a cost
saver and a profit enhancer
•
Risk System Integration
The regulators are forcing the larger firms to create “living willsâ€
which has resulted in a much better understanding the the numerous
piece parts. The Basel risk data requirements are now forcing a
near-real-time integration of numerous disparate systems. This seems
like fertile ground for innovation both for compliance and to build
upon the results.
5 Finance-Industry Sponsored Kaggle Contests
Over the past several years a number of financial firms have sponsored
Kaggle contests. Someone at these firms thought that these subjects were worth paying for crowd-sourced analysis and was willing to go to the considerable trouble of setting up and monitoring a contest with thousands of participants lasting three months or more.
Two Sigma is a quant fund, listed in the appendix on pages 17 and 23.
The challenge was to predict daily price changes. (In this contest I
earned a Kaggle Silver Medal for coming in 37th out of 2,070
contestants. [9])
Opportunity
Di
sco
v
ery
Improve credit risk models by predicting the probability of default on
consumer credit.
Risk Mitigation
Improve the quality of information within transaction data.
Risk Mitigation
Predict which customers will leave an insurance company in the next 12
months.
Risk Mitigation
Given a dataset of 2D dashboard camera images, State Farm is
challenging Kag-
guess to classify each driver’s behavior. Are they driving attentively,
wearing their seatbelt, or taking a selfie with their friends in the
backseat?
Risk Mitigation
Santander (Spain-based bank) is challenging Kagglers to predict which
products their existing customers will use in the next month based on
their past behavior and that of similar customers.
Opportunity
Di
sco
v
ery
Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship.
Risk Mitigation
, Opportunity Disco very
Using terabytes of noisy, non-stationary data Winton Capital is looking
for data scientists who excel at finding the hidden signal in the
proverbial haystack, and who are excited by creating novel statistical
modeling and data mining techniques.
Opportunity
Di
sco
v
ery
Using a customers shopping history, can you predict what insurance policy they will end up choosing?
Opportunity
Di
sco
v
ery
Claims management may require different levels of the check before a claim can be approved and payment can be made. With the new practices and behaviors generated by the digital economy, this process needs adaptation thanks to data science to meet the new needs and expectations of customers. Kagglers are challenged to predict the category of a claim based on features available early in the process.
Risk Mitigation
, Opportunity Disco very
The life insurance application process is antiquated. Customers provide
extensive information to identify risk classification and
eligibility, including scheduling medical exams, a process that takes
an average of 30 days.
The result? People are turned off. That's why only 40% of U.S.
households own individual life insurance. Prudential wants to make it quicker and less labor intensive for new and existing customers to get a quote while maintaining privacy boundaries.
Opportunity
Di
sco
v
ery
Predict a transformed count of hazards or pre-existing damages using a
dataset of property information. This will enable Liberty Mutual to more accurately identify high-risk homes that require an additional examination to confirm their insurability.
Risk Mitigation
Fire losses account for a significant portion of total property losses.
High severity and low frequency, fire losses are inherently volatile,
which makes modeling them difficult. In this challenge, your task is to
predict the transformed ratio of loss to the total insured value. This will
enable more accurate identification of each policyholders risk exposure
and the ability to tailor the insurance coverage for
their specific operation.
Risk Mitigation
The Benchmark Bond Trade Price Challenge is a competition to predict the next price that a US corporate bond might trade at.
Opportunity
Di
sco
v
ery
Determine whether a loan will default and the loss incurred. We are
building a bridge between traditional banking, where we are looking at
reducing the consumption of economic capital, to an asset-management
perspective, where we optimize on the risk to the financial investor.
Risk Mitigation
Develop models to predict the stock market’s short-term response following large trades. Contestants are asked to derive empirically
models to predict the behavior of bid and ask prices following such
“liquidity shocksâ€.
Modeling market resiliency will improve trading strategy evaluation
methods by increasing the realism of backtesting simulations, which
currently, assume zero market resiliency.
Risk Mitigation
, Opportunity Disco very
Bodily Injury Liability Insurance covers other peoples bodily injury or death for which the insured is responsible. The goal of this
competition is to predict Bodily Injury Liability Insurance claim
payments based on the characteristics of the insureds vehicle.
Risk Mitigation
Allstate is currently developing automated methods of predicting the cost, and hence severity, of claims. Kagglers are invited to create an algorithm which accurately predicts claims severity.
Risk Mitigation
6 Spark and Finance on YouTube
•
Apache Spark on IBM z Systems Demo for Finance
https://www.youtube.com/watch?v=yw0dQFMyxFQ
References to IMS, CICS, and VSAM make me think this is Spark on an IBM
mainframe. Considering the fact that IBM mainframes are still quite widely used, this might be worth understanding.
Opportunity
Discovery
, Risk Mitigation
•
Using Spark to Analyze Activity and Performance in High Speed
T
rading En
vironmen
ts
https://www.youtube.com/watch?v=zdz9Cj1-hjA
Corvil: Irish data monitoring and analytics for financial data using
Spark. Non-intrusive low-latency electronic trading monitoring,
regulatory compliance through the use of streaming telemetry.
Risk Mitigation
•
Spark in Finance Quantitative Investing
https://www.youtube.com/watch?v=WPc-DoSeCpU&t=7s
Reading historical and live tick data, determine a trend and propose
trades.
Opportunity
Disc
o
v
ery
•
Financial Modeling Using Apache Spark
https://www.youtube.com/watch?v=jCXOa6doXEs
Blackrock mortgage analysis of mortgage data. Using Spark, Scala, and D3
to visualize a large loan-level mortgage dataset, extract distributions and cluster boundaries. Also, use K-Means to reveal similar borrower groups and corresponding discriminant attributes.
Opportunity
Disc
o
v
ery
•
Estimating Financial Risk with Spark
https://www.youtube.com/watch?v=0OM68k3np0E
VaR with Monte Carlo using market risk factors explained by Cloudera
Risk Mitigation
7 APPENDIX
Global Financial Services Companies by Revenue
[20]
|
Berkshire Hathaway
|
Conglomerate
|
210.8
|
United States
|
|
AXA
|
Insurance
|
147.5
|
France
|
|
Allianz
|
Insurance
|
140.3
|
Germany
|
|
ICBC
|
Banking
|
134.8
|
China
|
|
Fannie Mae
|
Investment Services
|
131.9
|
United States
|
|
ING
|
Banking
|
130.0
|
Netherlands
|
|
BNP Paribas
|
Banking
|
126.2
|
France
|
|
Generali Group
|
Insurance
|
116.7
|
Italy
|
|
China Construction Bank
|
Banking
|
113.1
|
China
|
|
Banco Santander
|
Banking
|
108.8
|
Spain
|
|
JP Morgan Chase
|
Banking
|
108.2
|
United States
|
|
Socit Gnrale
|
Banking
|
107.8
|
France
|
|
HSBC
|
Banking
|
104.9
|
United Kingdom
|
|
Agricultural Bank of China
|
Banking
|
103.0
|
China
|
|
Bank of America
|
Banking
|
100.1
|
United States
|
|
Bank of China
|
Banking
|
98.1
|
China
|
|
Wells Fargo
|
Banking
|
91.2
|
United States
|
|
Citigroup
|
Banking
|
90.7
|
United States
|
|
Prudential
|
Insurance
|
90.2
|
United Kingdom
|
|
Munich Re
|
Insurance
|
88.0
|
Germany
|
|
Prudential Financial
|
Insurance
|
84.8
|
United States
|
|
Freddie Mac
|
Investment Services
|
80.6
|
United States
|
|
Banco Bradesco
|
Banking
|
78.3
|
Brazil
|
|
Lloyds Banking Group
|
Banking
|
75.6
|
United Kingdom
|
|
Ita Unibanco Holding
|
Banking
|
70.5
|
Brazil
|
|
Zurich Insurance Group
|
Insurance
|
70.4
|
Switzerland
|
|
Aviva
|
Insurance
|
69.0
|
United Kingdom
|
|
Banco do Brasil
|
Banking
|
69.0
|
Brazil
|
|
MetLife
|
Insurance
|
68.2
|
United States
|
|
American International Group
|
Insurance
|
65.7
|
United States
|
|
China Life Insurance
|
Insurance
|
63.2
|
China
|
|
Mitsubishi UFJ Financial Group
|
Banking
|
59.0
|
Japan
|
|
Legal & General Group
|
Insurance
|
56.9
|
United Kingdom
|
|
Dai-ichi Life
|
Insurance
|
56.5
|
Japan
|
|
Barclays
|
Banking
|
55.7
|
United Kingdom
|
|
Aegon
|
Insurance
|
55.2
|
Netherlands
|
|
Deutsche Bank
|
Banking
|
55.0
|
Germany
|
|
UniCredit
|
Banking
|
54.2
|
Italy
|
|
CNP Assurances
|
Insurance
|
53.2
|
France
|
|
BBVA
|
Banking
|
52.1
|
Spain
|
|
Credit Agricole
|
Banking
|
51.2
|
France
|
|
Ping An Insurance Group
|
Insurance
|
51.1
|
China
|
|
National Australia
|
Banking
|
49.2
|
Australia
|
|
Commonwealth Bank
|
Banking
|
47.8
|
Australia
|
|
Intesa Sanpaolo
|
Banking
|
47.7
|
Italy
|
|
UBS
|
Investment Services
|
47.7
|
Switzerland
|
|
Sumitomo Mitsui Financial Group
|
Banking
|
47.3
|
Japan
|
|
Westpac Banking Group
|
Banking
|
43.9
|
Australia
|
|
Bank of Communications
|
Banking
|
43.5
|
China
|
|
Credit Suisse Group
|
Investment Services
|
42.5
|
Switzerland
|
|
MS&AD Insurance Group
|
Insurance
|
42.2
|
Japan
|
|
Royal Bank of Scotland
|
Banking
|
42.1
|
United Kingdom
|
|
Goldman Sachs
|
Investment Services
|
41.7
|
United States
|
|
People’s Insurance Company
|
Insurance
|
41.3
|
China
|
|
Tokio Marine Holdings
|
Insurance
|
39.4
|
Japan
|
|
Royal Bank of Canada
|
Banking
|
38.3
|
Canada
|
|
ANZ
|
Banking
|
37.5
|
Australia
|
|
Manulife Financial
|
Insurance
|
37.3
|
Canada
|
|
Sberbank
|
Banking
|
36.1
|
Russia
|
|
State Bank of India
|
Banking
|
35.1
|
India
|
|
Talanx
|
Insurance
|
34.9
|
Germany
|
|
Power Corporation of Canada
|
Insurance
|
34.2
|
Canada
|
|
Swiss Re
|
Insurance
|
33.6
|
Switzerland
|
|
American Express
|
Financial Services
|
33.4
|
United States
|
|
Allstate
|
Insurance
|
33.3
|
United States
|
|
Mizuho Financial Group
|
Banking
|
32.8
|
Japan
|
|
Old Mutual
|
Investment Services
|
32.2
|
United Kingdom
|
|
Morgan Stanley
|
Investment Services
|
32.0
|
United States
|
|
Standard Life
|
Insurance
|
31.2
|
United Kingdom
|
|
Sompo Holdings
|
Insurance
|
30.9
|
Japan
|
|
TD Bank Group
|
Banking
|
30.6
|
Canada
|
|
China
|
Banking
|
28.4
|
China
|
|
China
|
Banking
|
27.9
|
China
|
|
Bank of Nova Scotia
|
Banking
|
27.6
|
Canada
|
|
Onex
|
Investment Services
|
27.4
|
Canada
|
|
China
|
Insurance
|
27.3
|
China
|
|
Mapfre
|
Insurance
|
27.1
|
Spain
|
|
Standard Chartered
|
Banking
|
26.9
|
United Kingdom
|
|
Dexia
|
Banking
|
26.6
|
Belgium
|
|
Hartford Financial Services
|
Insurance
|
26.4
|
United States
|
|
Travelers Cos
|
Insurance
|
25.7
|
United States
|
|
Commerzbank
|
Banking
|
25.5
|
Germany
|
|
Aflac
|
Insurance
|
25.4
|
United States
|
|
Shanghai Pudong Development
|
Banking
|
25.4
|
China
|
Major
Stock Exchanges
[21]
|
New York Stock Exchange
|
United States
|
New York
|
|
NASDAQ
|
United States
|
New York
|
|
London Stock Exchange Group
|
United Kingdom
|
London
|
|
Japan Exchange Group
|
Japan
|
Tokyo
|
|
Shanghai Stock Exchange
|
China
|
Shanghai
|
|
Hong Kong Stock Exchange
|
Hong Kong
|
Hong Kong
|
|
Euronext
|
European Union
|
Amsterdam, Brussels, Lisbon, London, Paris
|
|
Shenzhen Stock Exchange
|
China
|
Shenzhen
|
|
Toronto Stock Exchange
|
Canada
|
Toronto
|
|
Deutsche Brse
|
Germany
|
Frankfurt
|
|
Bombay Stock Exchange
|
India
|
Mumbai
|
|
National Stock Exchange of India
|
India
|
Mumbai
|
|
SIX Swiss Exchange
|
Switzerland
|
Zurich
|
|
Australian Securities Exchange
|
Australia
|
Sydney
|
|
Korea Exchange
|
South Korea
|
Seoul
|
|
OMX Nordic Exchange
|
Sweden
|
Stockholm
|
|
JSE Limited
|
South Africa
|
Johannesburg
|
|
BME Spanish Exchanges
|
Spain
|
Madrid
|
|
Taiwan Stock Exchange
|
Taiwan
|
Taipei
|
|
BM&F Bovespa
|
Brazil
|
So Paulo
|
Quant
F
unds
[13]
•
D. E. Shaw (New York, NY)
•
Renaissance Technologies (East Setauket, NY)
•
Morgan Stanley PDT (New York, NY)
•
Point72 Asset Management (SAC Capital)
•
AQR Capital
•
Two Sigma Investments (New York, NY)
•
Citadel (Chicago, IL)
•
Jane Street Capital (New York and London)
•
RG Niederhoffer
•
Jump Trading
•
KCG Holdings
•
Bridgewater Associates
•
Hudson River Trading
•
Man Group AHL
•
Highbridge
•
Millennium/WorldQuant
•
Winton
•
Bluecrest
•
Ellington Capital
•
Tower Research Capital
•
Parametrica Global Master Ltd
•
Camox Ltd
•
Voloridge Trading
•
Senvest Partners Ltd
•
BlackRock European Hedge
Credit
Card Issuers
[1]
1. Visa - 323M Cardholders
2. MasterCard - 191M Cardholders
3. Chase - 93M Cardholders
4. American Express - 58M Cardholders
5. Discover - 57M Cardholders
6. Citibank - 48M Cardholders
7. Capital One - 45M Cardholders
8. Bank of America - 32M Cardholders
9. Wells Fargo - 24M Cardholders
10. US Bank - 18.5M Cardholders
11. USAA - 10M Cardholders
12. Credit One - 6M Cardholders
13. Barclaycard US 418K Cardholders
14. First PREMIER Bank (subprime)
15. PNC
Mortgage Risk
[10]
Prior to the financial collapse of 2007-2008 mortgage, securitization was the hot thing. Many institutions and individuals got burned and a
residual fear of securitization remains.
The result is that for jumbo and subprime mortgages, the originators are now holding many more of the loans. This reduces the systematic
risk but an unanticipated consequence is that Fannie Mae and Freddie Mac [3] are
now holding 50% of $11 trillion outstanding in the middle market.
Therefore the US government has undertaken a huge amount of default and
interest-rate risk.
Insurance Companies by Premium Income
[8]
Property/Casualty Insurance
|
State Farm Mutual Automobile Insurance
|
62,189,311
|
|
Berkshire Hathaway Inc.
|
33,300,439
|
|
Liberty Mutual
|
32,217,215
|
|
Allstate Corp.
|
30,875,771
|
|
Progressive Corp.
|
23,951,690
|
|
Travelers Companies Inc.
|
23,918,048
|
|
Chubb Ltd.
|
20,786,847
|
|
Nationwide Mutual Group
|
19,756,093
|
|
Farmers Insurance Group of Companies
|
19,677,601
|
|
USAA Insurance Group
|
18,273,675
|
Life Insurance/Annuities
|
MetLife Inc.
|
95,110,802
|
|
Prudential Financial Inc.
|
45,902,327
|
|
New York Life Insurance Group
|
30,922,462
|
|
Principal Financial Group Inc.
|
28,186,098
|
|
Massachusetts Mutual Life Insurance Co.
|
23,458,883
|
|
American International Group
|
22,463,202
|
|
Jackson National Life Group
|
22,132,278
|
|
AXA
|
21,920,627
|
|
AEGON
|
21,068,180
|
|
Lincoln National Corp.
|
19,441,555
|
|
Homeowners Insurance
State Farm Mutual Automobile Insurance
|
17,516,715
|
|
Allstate Corp.
|
7,926,984
|
|
Liberty Mutual
|
5,993,803
|
|
Farmers Insurance Group of Companies
|
5,284,511
|
|
USAA Insurance Group
|
5,000,407
|
|
Travelers Companies Inc.
|
3,305,427
|
|
Nationwide Mutual Group
|
3,249,456
|
|
American Family Insurance Group
|
2,609,366
|
|
Chubb Ltd. (4)
|
2,485,193
|
|
Erie Insurance Group
|
1,471,544
|
Private
P
assenger Auto Insurance
|
State Farm Mutual Automobile Insurance
|
39,194,660
|
|
Berkshire Hathaway Inc.
|
25,531,762
|
|
Allstate Corp.
|
20,813,858
|
|
Progressive Corp.
|
19,634,834
|
|
USAA Insurance Group
|
11,691,051
|
|
Liberty Mutual
|
10,774,426
|
|
Farmers Insurance Group of Companies
|
10,304,622
|
|
Nationwide Mutual Group
|
7,640,558
|
|
American Family Insurance Group
|
4,005,549
|
|
Travelers Companies Inc.
|
3,896,786
|
|
Commercial Auto Insurance
|
|
|
Progressive Corp.
|
2,625,929
|
|
Travelers Companies Inc.
|
2,124,182
|
|
Nationwide Mutual Group
|
1,735,614
|
|
Zurich Insurance Group
|
1,624,621
|
|
Liberty Mutual
|
1,604,461
|
|
Old Republic International Corp.
|
1,123,042
|
|
Berkshire Hathaway Inc.
|
951,775
|
|
American International Group (AIG)
|
867,567
|
|
Auto-Owners Insurance Co.
|
739,495
|
|
Chubb Ltd.
|
695,210
|
|
Commercial Lines Insurance
|
|
|
|
Chubb Ltd.
|
16,528,891
|
|
Travelers Companies Inc.
|
16,463,566
|
|
Liberty Mutual
|
15,056,251
|
|
American International Group (AIG)
|
13,144,961
|
|
Zurich Insurance Group
|
12,554,597
|
|
CNA Financial Corp.
|
9,763,122
|
|
Nationwide Mutual Group
|
8,335,275
|
|
Hartford Financial Services
|
7,679,737
|
|
Berkshire Hathaway Inc.
|
7,650,236
|
|
Tokio Marine Group
|
6,256,196
|
|
W
orkers’ Compensation Insurance
|
|
Travelers Companies Inc.
|
4,467,425
|
|
Hartford Financial Services
|
3,324,361
|
|
AmTrust Financial Services
|
2,972,901
|
|
Zurich Insurance Group
|
2,851,695
|
|
Liberty Mutual
|
2,481,479
|
|
Berkshire Hathaway Inc.
|
2,479,354
|
|
State Insurance Fund Workers’ Comp (NY)
|
2,437,325
|
|
Chubb Ltd.
|
2,368,918
|
|
American International Group
|
2,345,247
|
|
State Compensation Insurance Fund (CA)
|
1,638,849
|
Global Asset Management Firms by Revenue
[18]
|
BlackRock
|
United States
|
4,890
|
|
The Vanguard Group
|
United States
|
3,149
|
|
UBS
|
Switzerland
|
2,716
|
|
State Street Global Advisors
|
United States
|
2,460
|
|
Fidelity Investments
|
United States
|
2,025
|
|
Allianz
|
Germany
|
1,949
|
|
J.P. Morgan Asset Management
|
United States
|
1,760
|
|
BNY Mellon Investment Management
|
United States
|
1,740
|
|
PIMCO
|
United States
|
1,590
|
|
Credit Agricole Group
|
France
|
1,527
|
Global Investment Banks by Revenue
[2]
|
JPMorgan
|
3,361
|
|
Goldman Sachs
|
2,858
|
|
Bank of America Merrill Lynch
|
2,684
|
|
Morgan Stanley
|
2,501
|
|
Citi
|
2,378
|
|
Barclays
|
1,884
|
|
Credit Suisse
|
1,760
|
|
Deutsche Bank
|
1,387
|
|
RBC Capital Markets
|
994
|
|
UBS
|
904
|
|
Wells Fargo Securities
|
871
|
|
HSBC
|
793
|
|
Jefferies LLC
|
750
|
|
BNP Paribas
|
619
|
|
Lazard
|
565
|
|
BMO Capital Markets
|
448
|
|
Nomura
|
445
|
|
Mizuho
|
435
|
|
Sumitomo Mitsui Financial Group
|
413
|
|
Evercore Partners Inc
|
407
|
Hedge Funds By Assets Under Management
[6]
|
OrgCRD
|
PrimaryBusinessName
|
May2017AUM
|
|
110814
|
NOMURA ASSET MANAGEMENT CO., LTD.
|
367.6
|
|
105129
|
BRIDGEWATER ASSOCIATES, LP
|
239.3
|
|
158117
|
MILLENNIUM MANAGEMENT LLC
|
207.6
|
|
158319
|
SAMSUNG ASSET MANAGEMENT COMPANY, LTD.
|
182.2
|
|
148826
|
CITADEL ADVISORS LLC
|
152.7
|
|
143161
|
APOLLO CAPITAL MANAGEMENT, L.P.
|
125
|
|
140074
|
PICTET ASSET MANANGEMENT SA.
|
122.8
|
|
110997
|
NIKKO ASSET MANAGEMENT CO LTD
|
120.6
|
|
282598
|
VANGUARD ASSET MANAGEMENT, LIMITED
|
120.2
|
|
111128
|
THE CARLYLE GROUP
|
101.9
|
|
106661
|
RENAISSANCE TECHNOLOGIES LLC
|
97
|
|
144533
|
KOHLBERG KRAVIS ROBERTS
|
90
|
|
168122
|
ANNALY MANAGEMENT COMPANY
|
87.9
|
|
152719
|
ALPHADYNE ASSET MANAGEMENT PTE. LTD.
|
84.6
|
|
133720
|
PINE RIVER CAPITAL MANAGEMENT L.P.
|
82.8
|
|
159732
|
TPG GLOBAL ADVISORS, LLC
|
79.5
|
|
138111
|
BALYASNY ASSET MANAGEMENT L.P.
|
75.1
|
|
144603
|
EASTSPRING INVESTMENTS (SINGAPORE) LIMITED
|
74.5
|
|
155587
|
FIELD STREET CAPITAL MANAGEMENT, LLC
|
63.3
|
|
107580
|
BLACKSTONE ALTERNATIVE ASSET MANAGEMENT LP
|
62.3
|
|
148823
|
BLUECREST CAPITAL MANAGEMENT LIMITED
|
62.2
|
|
142979
|
BLACKSTONE REAL ESTATE ADVISORS L.P.
|
60.1
|
|
160795
|
APG ASSET MANAGEMENT US, INC
|
59.3
|
|
130074
|
ARES MANAGEMENT LLC
|
58.4
|
|
136979
|
BLACKSTONE MANAGEMENT PARTNERS L.L.C.
|
57.4
|
|
161600
|
AGNC MANAGEMENT, LLC
|
56.9
|
|
129612
|
FORTRESS INVESTMENT GROUP
|
56.9
|
|
156601
|
ELLIOTT MANAGEMENT CORPORATION
|
56
|
|
160309
|
ELEMENT CAPITAL MANAGEMENT LLC
|
55.9
|
|
139345
|
MACQUARIE FUNDS MANAGEMENT
|
54.7
|
|
160188
|
MOORE CAPITAL MANAGEMENT, LP
|
53.8
|
|
107913
|
OZ MANAGEMENT LP
|
51.7
|
|
159738
|
TPG CAPITAL ADVISORS, LLC
|
51.6
|
|
137137
|
TWO SIGMA INVESTMENTS, LP
|
49.3
|
|
152254
|
TWO SIGMA ADVISERS, LP
|
48.7
|
|
110338
|
MACKENZIE INVESTMENTS
|
48.6
|
|
156078
|
HUDSON AMERICAS L.P.
|
48.4
|
|
160000
|
LONE STAR NORTH AMERICA ACQUISITIONS, LLC
|
48.1
|
|
152175
|
CERBERUS CAPITAL MANAGEMENT, L.P.
|
48
|
|
173355
|
CANDRIAM LUXEMBOURG S.C.A.
|
47.1
|
|
156934
|
3G CAPITAL PARTNERS LP
|
46.3
|
|
143158
|
APOLLO MANAGEMENT, L.P.
|
46.2
|
|
157589
|
CAPULA INVESTMENT US LP
|
45.8
|
|
156945
|
WARBURG PINCUS LLC
|
45.7
|
|
132272
|
VIKING GLOBAL INVESTORS LP
|
43.4
|
|
160679
|
ADAGE CAPITAL MANAGEMENT, L.P.
|
42
|
|
146629
|
KKR CREDIT ADVISORS (US) LLC
|
41.5
|
|
159215
|
ALPINVEST PARTNERS B.V.
|
41.2
|
|
108679
|
D. E. SHAW
|
37
|
Largest private equity firms by PE capital raised
[17]
|
The Carlyle Group
|
Washington D.C.
|
$30,650.33
|
|
Kohlberg Kravis Roberts
|
New York City
|
$27,182.33
|
|
The Blackstone Group
|
New York City
|
$24,639.84
|
|
Apollo Global Management
|
New York City
|
$22,298.02
|
|
TPG
|
Fort Worth/San Francisco
|
$18,782.59
|
|
CVC Capital Partners
|
Luxembourg
|
$18,082.35
|
|
General Atlantic
|
New York City
|
$16,600.00
|
|
Ares Management
|
Los Angeles
|
$14,113.58
|
|
Clayton Dubilier & Rice
|
New York City
|
$13,505.00
|
|
Advent International
|
Boston
|
$13,228.09
|
|
EnCap Investments
|
Houston
|
$12,400.20
|
|
Goldman Sachs Principal Investment Area
|
New York City
|
$12,343.32
|
|
Warburg Pincus
|
New York City
|
$11,213.00
|
|
Silver Lake
|
Menlo Park
|
$10,986.40
|
|
Riverstone Holdings
|
New York City
|
$10,384.26
|
|
Oaktree Capital Management
|
Los Angeles
|
$10,147.28
|
|
Onex
|
Toronto
|
$10,097.21
|
|
Ardian (formerly AXA Private Equity)
|
Paris
|
$9,805.25
|
|
Lone Star Funds
|
Dallas
|
$9,731.81
|
In
v
estmen
t
Banking Private Equity Groups
[19]
ABN AMRO AAC Capital Partners Barclays Capital Equistone Partners
Europe BNP Paribas PAI Partners
CIBC World Markets Trimaran Capital Partners
Citigroup Court Square; CVC; Welsh, Carson, Anderson &
StoweBruckmann, Rosser, S Deutsche Bank MidOcean Partners
Globus Capital Holdings Globus Capital Banca
Goldman Sachs Goldman Sachs Capital Partners JPMorgan Chase CCMP
Capital; One Equity Partners Lazard Lazard Alternative Investments
Merrill Lynch Merrill Lynch Global Private Equity
Morgan Stanley Metalmark Capital; Morgan Stanley Capital Partners New
York
National Westminster Bank Bridgepoint Capital
Nomura Group Terra Firma Capital Partners
UBS UBS Capital; Affinity Equity Partners; Capvis; Lightyear Capital
Wells Fargo Pamlico Capital
William Blair & Company William Blair Capital Partners
F
ederal Reserve System
The St. Louis Fed is well known among economics geeks as a fantastic
source of data, analysis and commentary. [4] In fact, all the Fed banks
are avid consumers of data, analysis and risk-management metrics.
[14] The Economist of London. The Economist Intelligence Unit. https://
www.eiu.com/home.aspx
.
[15] Wikipedia. BCBS 239.
https://en.m.wikipedia.org/wiki/BCBS_239
. [16] Wikipedia. James Harris Simons.
https://en.wikipedia.org/wiki/
James_Harris_Simons
.
[17] Wikipedia. Largest private equity firms by PE capital raised.
https:
//en.wikipedia.org/wiki/List_of_private_equity_firms
.
[18] Wikipedia. List of asset management firms.
https://en.wikipedia.org/
wiki/List_of_asset_management_firms
.
[19] Wikipedia. List of investment banking private equity groups.
https://
en.wikipedia.org/wiki/List_of_private_equity_firms
.
[20] Wikipedia. List of largest financial services companies by revenue.
https://en.wikipedia.org/wiki/List_of_largest_financial_
services_companies_by_revenue
.
[21] Wikipedia. Major Stock Exchanges.
https://en.wikipedia.org/wiki/
List_of_stock_exchanges
.
[22] Wikipedia. The Big Short.
https://en.wikipedia.org/wiki/The_Big_
Short
.
REFERENCE COMMITTEE A
When the idea of Last-Year insurance was presented to the AMA in December 1987, someone got to the microphone before I could. The AMA system is to publish meeting agendas in an advanced handbook. The subject had therefore been announced with a few spare sentences leading up to a proposal that the Association should look into the matter.
Whether the proposal was really unclear or whether a comedian just jumped at an opening, the subject was introduced with a mocking story. There was a little town outside Philadelphia, it seems, which used to have an ordinance about its fire hydrants. All hydrants were required to be inspected, one week before each fire. To follow that jibe with a description of insurance technicalities isn't the easiest position to in, but somehow the reference committee subsequently found the generosity to endorse the study.
Last year of life insurance is life insurance, paid after the death of the subscriber. The death benefit is paid to a health insurance company, reimbursement medical expenses incurred during the final year of the subscriber's life. The ultimate effect and the intention is to reduce the premiums of health insurance.
Since there can be no free lunch, it is clear this proposal will not reduce the cost of medicare care. The overall total cost of health insurance, therefore, is not changed by changing the form of premium collection. Indeed another layer of administration is required. What difference can it make whether you pay part of your premium to company A or company B? There are five answers.
Pre-Funding. As emphasized in the first section of this book, there is a great need to change our national system of health insurance from a pay-as-you-go system to a prefunded one. Such a radical shift in philosophy could be quite disruptive, so transitional steps are needed. each age group has a different point of view about pay-as-you-go. Young subscribers since their premiums are higher than their risks. Older subscribers feel thirty years of paying premiums creates a moral obligation for health insurance to carry them through their time of heaviest expenses. Consequently, established dominant health insurers have legitimate anxiety about new companies skimming off their healthy subscribers, leaving them with the sick ones and thus triggering an insupportable upward spiral of premiums and dropouts. The problem is to prevent this disaster for the private sector without precipitating it by changes which frighten away healthy subscribers. The problem is to fix the engine with the motor running.
Therefore, the initial reaction that last year insurance constitutes fragmentation is unfair; the segmentation is intentional, aimed at providing a gradual shift toward pre-funded health insurance in one area where it may be achievable. Ina segmented system, reducing the premium for a reduced unfunded component of health insurance means fewer remains at stake when you try to reduce the unfunded problem still further. Subscribers and insurers have more temptation but less latitude for gaming a system with fatal illness largely removed. When a greater proportion of claims represent randomized unpredictable acute illness or accidental injuries, the troublesome non-random risks are easier to see. The main difficulty is obstetrics, where family planning makes the insurance mechanism highly unstable; further ideas relating to obstetrics need to be developed and would be easier to develop if isolated underwriting of fatal illness proves a success.
Catastrophic Health Coverage. When Secretary of HHS Otis Bowen opened up the subject of catastrophic health insurance, he was probably as jolted as other physicians to watch the way this popular idea was instantly redefined. Once it became clear that catastrophic health coverage was a legislative slam-dunk, attempts were made to include domiciliary care of the aged, chronic illness of all sorts, mental retardation, and many other things which were expensive hence a catastrophe if you had to pay for them. Any hope Medicare could be restructured to pay for expensive illness first, paying for minor illness only if money was left over, went up in the smoke of special interest lobbying and revived hope among liberals of extending Medicare into a national health scheme.
This appalling example of what is out there on the other side of the gates, should at least remind serious students of health financing to use highly technical definitions when they make a proposal. There is, of course, plenty of room to argue that terminal care life insurance should cover expenses two years before death, or conversely that it should only cover two months. You can change the calendar definition of the coverage almost at will, and yet still intelligibly call it last-year insurance. The intent is clearly to cover the characteristically high costs of dying under medical supervision, as contrasted with saving lives with medical miracles, or nursing chronic invalids. if such coverage should pay for sunglasses, facelifts, or porcelain teeth, it would clearly be unintentional. Terminal care of fatal illness.
With the mechanism largely impervious to deliberate redefinition, and largely immune to manipulation for profit, isolation of the ethical issues of terminal care becomes a possibility. The cost of the problem gets held up for regular consideration, as premiums for the coverage get revised. Public attitudes about whether an extreme medical function is desirable would surely be reflected in the choices actually made between different coverage options. At different ages, one might feel a desperation to have every possible chance of survival, yet might later wish to be left to die in peace. Lawyers may argue about the legitimacy of living wills, but few would dispute that someone who spent his last-year insurance on something else, had made an important statement about his wishes. Deathbed discussions are almost invariably couched in slogans. The same relative, on the same day, may say "Let him die in peace," and then "Where there is life, there is hopes." Such expressions are usually made for the effect they have on the listeners and do not greatly illuminate underlying public attitudes about a serious subject. Observation of how much of their money they are collectively willing to spend is often a better guide to what people truly want that is the expression of opinion by their representatives. On one occasion, I happened to watch a large conf=gressional committee listening attentively to testimony on health insurance when unexpectedly the subject of euthanasia was introduced. Within two minutes, a majority of the congressmen had fled the room.
Pre-Existing Conditions. People change jobs with fair frequency, voluntarily and involuntarily. The tendency of young entrants into the job market is to take part-time or small-time employment in order to gain experience, but then if possible to work their way into permanent employment with a major employer. This progression is seen by them as moving into a better job, one "where the benefits are good."
This system has a sort of hidden equity to it since generous pay and generous benefits are definitely linked with the profitability of the firm. Unions have tended to be strong and aggressive in prosperous companies, while conversely companies in the rust belt losing out to foreign imports have found the industrial unions much more tolerant of givebacks. Fortune 500 companies definitely get a better quality of worker, because they pay up. With many exceptions, the tendency is to work for small struggling companies when you are young, and big prosperous ones when you get good at your work. This unofficial system provides health insurance directly to the working population, while the youngsters just entering the job market mostly don't have health problems. If such a young uninsured person does get suddenly sick, the larger companies may still pick up much of the cost involuntarily, courtesy of the cost-shifting mysteries within hospital accounting systems. Much against their will, the large prosperous companies do partially reinsure the system against risks being run within the pool of young people from whom their future employees will be drawn.
Obviously, such a system is unstable. One of its worst features is that those who develop extremely serious illness before they get into the employer health insurance mainstream, are probably permanently excluded from it. There is no way available to them or their parents to guarantee future insurability for health insurance. As long as health insurance remains so firmly linked to employment in a large firm, it is hard to imagine any solution except through modification of the life insurance mechanism. Even so, if large numbers of people are to be encouraged to protect their insurability for health insurance, some way must be found for them to get their investment back, once the huge majority of them eventually do acquire employer-paid health insurance. We will return to this issue in the next chapter.
If the average person lives to be 80, and that's almost true, only forty years of that time are spent in the workforce where employer-based group health insurance is the norm. Since this period of time includes the coverage of dependents children and has potential carry-over to retiree health benefits, it is critical for the individual worker and his family to lock up his health insurance protection. The most frightening aspect of sickness among active workers is the possibility they may not be able to get health insurance when they lose their jobs. To be sick and out of a job is to have a "pre-existing condition." Since the pre-existing condition is the one most likely to cause a problem, it is small consolation to be covered for everything else. To have a wife with leukemia or a child with cerebral palsy is a very strong reason not to switch jobs if there is any question of health insurance coverage. While the person who knows the condition exists may have some bargaining power or individual coverage options before he leaves the job. But to develop a serious health condition during a period of unemployment is a truly ominous situation. Insurance contracts do not include exclusions of coverage of pre-existing conditions as legal boilerplate, they really mean to exclude the risk to themselves. In fairness to them, it must be noted they cannot possibly allow people to get sick and apply for insurance. The situation needs some mechanisms for insuring against loss of health insurability, and last-year-of-life insurance might at least serve to reduce the range of potential uninsurability.
Portability. Our system of linking health insurance to the place of employment has the disastrous obverse that if you lose your job, you lose your health insurance. This particular issue periodically gets more attention when a recession in the economy leads to waves of layoffs. Employers of more than??? are required to maintain health insurance for ??? weeks after a layoff. Employees are entitled to continue their employer's group health plan at their own expense for ??? weeks more. However, such arrangements are complicated and unwelcome; it is not clear they are very popular with families who have suffered the bewilderment of losing their income. Last-year-of-life insurance would be as portable at your own expense, while funded life insurance is both portable and permanent as long as the cash values can carry the premium. Perpetual insurance is still better; the cash values have built to the point where the interest they generate is sufficient to pay the premium further contribution.
True, present income tax laws permit only term life insurance to be considered a business expense for an employer. In 1988 the Congress is undoubtedly in no mood for social legislation which increases the national budget deficit, such as by creating a tax shelter for cash-value life insurance. But laws can be changed when Congress wants to change them, and the experience with the catastrophic health insurance shows the public can sometimes whiplash congressional opinion very rapidly. A severe recession would immediately restore Keynesian ideas about budget deficits to fashion. The best present response to legislative defeatism on this subject is to examine the net effect on the deficit of replacing a portion of health insurance premiums with last-year life insurance premiums, transferring tax-deductibility from one to the other. If the two financial effects wash out, permitting last-year health premiums to be treated as business deductions should worry few practical politicians.
Experience-Rated Unfairness: The AIDS Epidemic.If a company had a policy of paying all medical bills of its employees, the cost to the company would vary with the amount of sickness there happened to be. Since self-insurance of this type represents at least half of all health insurance in America, health insurance companies must offer a comparable cost if they are to have any hope of selling insurance. Rather than establish a single premium rate for the community, the usual practice is to offer "experience rating", sometimes also called "merit rating." In an experience-rated group, the premium is adjusted up or down to reflect the cost of the claims actually submitted. From the point of view of the subscribing employer, the cost is the same as it would be to pay the claims directly, and the administrative profit of the insurance company may well be less than the cost of processing the claims in the employer's personnel department. Adjust this cost somewhat to recognize the interest earned or lost on the premiums and claims, and you pretty much have a formula for the dominant American health insurance system. The cost of fatal illnesses, the last year-of-life costs, are thus buried in a system which emphasizes the yearly costs of employers while making little analysis of the individuals who are included in the coverage.
From time to time, reformers have tried to force health insurance companies to charge a uniform community rate to all subscribers, but are immediately confronted with a rush by low-cost employers to drop out of insurance and adopt a self-insuring approach. As long as health insurance is unfunded and carries no future guarantees, it is not easy to convince lucky people they should pay more than they have to, just to lower the premiums of those who have bad luck. An earlier section of this book dealt with the pernicious effect on intergenerational risk-sharing which is exacted by the tax code in return for treating premium costs as business expenses. Many people see the wisdom of paying a higher premium when they are young and healthy so they will not be stranded when they are middle-aged and sick. A fair number of people are willing to pay more for their health insurance if remain healthy than if they happen to get sick. But almost no one wants to pay more for his health insurance when he is well while relying on the unenforceable voluntary generosity of future generations for support if he gets sick himself. Everyone distrusts the possibility that future generations might go self-insured and leave the present generation hanging out to dry.
Experience-rated health insurance, therefore, is an evil for which there are few obvious remedies. Since employment groups delimit final boundaries, experience-rating is inherent in basing health insurance on the employer. Last-year-of-life insurance contains the potential for the major cost risk of fatal illness to escape voluntarily from that employer-based partition. There is no way to know how much-hidden age, sex, race, or other discrimination there is in job recruitment, and certainly no. way to know how much the potential health costs are weighted in the equation. NOr is there any way to know how much American Business are unsuccessful with foreign competition because of these immeasurable issues is dramatically illustrated by the current epidemic of a contagious venereal virus, HIV...
AIDS is invariably fatal, its complications are expensive to manage, and it is relatively easy to surmise who is likely to catch it. This combination of features creates strong incentives for insurance companies to exclude the condition from coverage, or exclude high-risk groups from the subscriber base. Since the average cost of treating a single case is???, several HMOs have been driven out of business by having a run of cases of AIDS. From an insurance viewpoint, the most treacherous feature of AIDS is that the distribution of cases is not random throughout the population. If even a financially strong insurer is careless or altruistic about accepting high-risk groups, it's premium structure may rapidly become overpriced by comparison with competitors who somehow did not have so many cases. To be perfectly frank, homosexuals are overrepresented in the entertainment, fashion, and advertising industries, as well as the art world in general. It is almost impossible to imagine such industries maintaining an employment-based health insurance system in the future except if they somehow exclude paying for the risk of AIDS. If the epidemic spreads, and particularly if legislatures seek to prevent the exclusion of certain industries, then cities like San Francisco may simply not have any health HMOs or states like New York may not have any health insurance. Whether the exclusion is applied to people with positive blood tests, or to unmarried males, or to the entertainment industry, to cities or to whole states, insurers will find a way to protect their own solvency. If not, the whole country will be without health insurance until a cure is found.
Consider now the advantages of last-year-of-life health insurance for coping with this problem. Since AIDS is invariably fatal, it has the grisly advantage that no one is going to recover from the condition, only to contract a second expensive fatal illness later. Everybody else who doesn't get AIDS is also going to have a last year of life, and for the majority, it will be an expensive year. Medicare finds that ??% of its claims over the last 60 days of someone's life. Because the AIDS victims are young they have fewer years for compound interest to reduce premium costs, but having said that it remains true the population-wide risk of fatality at a young age is very small. Community premiums could double or triple without discouraging potential subscribers who have the cost of terminal cancer in mind. Actuarial costs of last-year insurance for the whole population can be calculated much more accurately than any individual can guess his own risk. Risk-avoidance strategies might somehow evolve, but with so little annual mortality in employer groups, yearly experience-rating could not be their mechanism.
Originally the "lower counties" of Pennsylvania, and thus one of three Quaker colonies founded by William Penn, Delaware has developed its own set of traditions and history.
Start in Philadelphia, take two days to tour around Delaware Bay. Down the New Jersey side to Cape May, ferry over to Lewes, tour up to Dover and New Castle, visit Winterthur, Longwood Gardens, Brandywine Battlefield and art museum, then back to Philadelphia. Try it!
Millions of eye patients have been asked to read the passage from Franklin's autobiography, "I walked up Market Street, etc." which is commonly printed on eye-test cards. Here's your chance to do it.
In 1751, the Pennsylvania Hospital at 8th and Spruce was 'way out in the country. Now it is in the center of a city, but the area still remains dominated by medical institutions.
Grievances provoking the American Revolutionary War left many Philadelphians unprovoked. Loyalists often fled to Canada, especially Kingston, Ontario. Decades later the flow of dissidents reversed, Canadian anti-royalists taking refuge south of the border.
