Measuring Climate Change

Climate Change is a Relative Concept – Why We Should Pay Attention to the Actuaries Climate Index (ACI)

Measuring Climate Change

 

Headlines from a News Release on August 1, 2018 by US and Canadian Actuarial Organizations:

Fall 2017 Data Reveal Extreme Weather Frequency and Sea Level Changes Continue to Drive Composite Index Values Higher

Hurricane Harvey caused $125B of damage and has barely receded in our memories. Hurricane Florence has devastated the Carolinas coast line and the damage caused by flooding continues to unfold.  These painful reminders of the havoc we are experiencing because of hurricane-related activity provide an apt moment to reflect on the topic of climate change.

Frame of Reference

Modern infrastructure is built to operate in a narrow band of physical parameters.  Along the coastline and rivers, homes are built at a certain distance from the water based on historically observed levels of water.  House roofs and siding are built to withstand a certain range of wind speeds.  When the physical environment surrounding properties changes such that it is outside of the range of tolerance, damage and devastation occurs.  As we are observing for Hurricane Florence, large scale evacuations and costly rescues need to take place.  Hence, we are concerned with climate change and, much more importantly, concerned with the rate of climate change.

Actuaries Climate Index (ACI)

Based on the above, the ability to measure changes in climate objectively is critical to US and global population.  Although this appears to be a seemingly simple and important objective, it is fraught with controversy since it has such broad and dramatic ramifications.

To minimize controversy and maintain objectivity and without dwelling into the causes and the solutions to the issue, actuarial organization in the US and Canada have formulated the quarterly Actuaries Climate Index TM (ACI) as an objective measure of changes in extreme weather and changes in sea level relative to the base period of 1961 through 1990.  The Index is an educational tool designed to help inform actuaries, public policymakers, and the public on changes in these measures over recent decades.

The Actuarial Climate Index consists of six components:

  1. Changes in the frequency and duration of extreme high temperatures;
  2. Changes in frequency and duration of extreme low temperatures;
  3. Heavy precipitation;
  4. Drought;
  5. Strong wind; and,
  6. Changes in sea level.

These six components for the Actuaries Climate Index were selected since they are representative of the key impacts of climate on people and the economy.  The Index is available for 12 regions in the United States and Canada and examines how the six components, and the composite thereof, are changing over time by region and across the two countries.

The ACI is an index of climate risks not unlike the Consumer Price Index that monitors average price changes over time to a basket of standard goods and services.  Actuaries measure and manage many types of risks; changes in climate is one of them.  Increasing values in the index point to increased occurrences of extreme climate events.

The graph below shows the ACI value from the reference period of 1961 to 1990 to more recent decades.  The key metric is the five-year moving average shown by the black line. Starting with a reference period, from which the index was set at zero, the ACI has risen steadily.

With the Fall 2017 data, the five-year moving average of Actuaries Climate Index reached 1.19, a new record that surpasses the previous record of 1.14.  The seasonal Actuaries Climate Index value increased to 2.00, from a previous level of 1.59.

In simple terms, continued deviation of temperature and sea level extremes from historically expected patterns continue to push the indices upward.  This is challenging news to the entire man-made and natural ecosystems.

Significance of Relative Change

Relative change and the pace of change in climate is of significance to the entire ecosystem but of most importance to modern man for several reasons.

  • Within the past century, in the developed countries, we have been dramatically successful in engineering the physical environment to suit our needs. For example, we have constructed homes and offices so that we can live and work in a narrow temperature band of 65-75 degrees F.  We have built roads, railways, and air transport networks that allow us to travel thousands of miles away.  We have created electrical grids to bring us power exactly where we need it.

Although a marvel of modern engineering, our man-made environment is susceptible to damage from climate change.  More importantly, we have become dependent on this infrastructure and have reduced the capacity to adapt to changes in the physical environment.  Ironically, to make the physical environment suit our needs, we have magnified our susceptibility to climate change dramatically.  Any adaptation to climate change now involves maintaining the fragile man-made environment.

  • Although not obvious to us daily, we are very dependent on the rest of the ecosystem to maintain our comfortable modern lifestyles. We may be able to take care of additional rainfall through extra drainage, but it has a significant impact on the crops that are grown in our area.  Due to our dependence on agriculture and the specific types of crops we produce, we cannot just stop at taking care of extra rainfall for ourselves and ignore its impact the ecosystem we have created to suit our needs.
  • We now live in a globally interdependent world, economically and physically. Hence, the impact of relative climate change is never confined to one geographic area.

 Further Background on the ACI

To explore the index, select Regional GraphsComponent Graphs, or Maps, please visit the website of the American Academy of Actuaries and click on the icon for ACI.  You can use the EXPLORE menu to go directly to the screen showing aggregate index values for the US and Canada and look at continental USA or Canada, or if you prefer, at one of the 12 regions of interest to you.

The data used for the ACI is gathered from the following public data sources:

Drought, Temperature, & Precipitation

  • Global Historical Climatology Network (GHCN) Daily, from the NOAA Satellite and Information Service, is an integrated database of daily climate summaries from land surface stations across the globe. The grids each cover a surface area of 2.5 degrees longitude by 2.5 degrees latitude.
  • GHCNDEXis a dataset based on GHCN Daily.  It provides gridded, station-based indices of temperature-and precipitation-related climate extremes and was developed by the Climate Change Research Centre and the Australian Research Council’s Centre of Excellence for Climate System Science.
  • CLIMDEX(Datasets for Indices of Extreme Weather) is developed and maintained by researchers at the Climate Change Research Centre (CCRC), The University of New South Wales (UNSW), and funded by the Australian Research Council and the Australian Department of Climate Change and Energy Efficiency through Linkage project LP100200690 and in collaboration with the University of Melbourne, Climate Research Division (Environment Canada) and NOAA’s National Climatic Data Center (USA).  See also: Expert Team on Climate Change Detection and Indices at the World Meteorological Organization.

Sea Level

Wind

  • The National Centers for Environmental Prediction(NCEP) reanalysis data, done in conjunction with the National Center for Atmospheric Research (NCAR), is provided by the National Oceanic and Atmospheric Administration’s Oceanic and Atmospheric Research/Earth System Research Laboratory Physical Sciences Division in Boulder, Colorado, USA.

 

Please contact the authors for more information:

Chandu C. Patel, FCAS, MAAA  E-mail: chandu.patel@hugginsactuarial.com

Kim Piersol, FCAS, MAAA  Email: kim.piersol@hugginsactuarial.com

Ronald T. Kuehn FCAS, MAAA, CERA, CPCU, ARM, FCA  Email: rusty.kuehn@hugginsactuarial.com   

Analysis of Economic Scenarios Used by Huggins Actuarial Services, Inc. 2016 through 2018

OVERVIEW

When Huggins’ actuaries create an Economic Capital Model, they use a state-of-the-art modeling platform designed by actuaries and other insurance professionals that is widely known as an industry standard.  These models are used to predict profits, policyholder surplus, income and other financial metrics over a pre-determined timeframe. In order to reflect uncertainty in the future economic activity which affects balance sheet assets (stocks and bonds), Huggins loads Economic Scenarios into the Economic Capital Model. The Economic Scenarios used by Huggins are generated by a top flight firm that specializes in the analysis of industry trends. In the tables below, Huggins actuaries analyze how these economic scenarios for four classes of assets have been changing over the period 2016 to 2018:

  • Corporate A-Rated Bonds;
  • Blue Chip Stocks;
  • Master Limited Partnerships (“MLP Equities”); and
  • Treasury Bonds.

In the analysis for each category of asset, the actuaries consider yields for bonds and total returns for stocks and MLPs using the future forecasts given by one-year time-step (one year in the future), three-year time-step (three years in the future) and five-year time-step (five years in the future) scenarios. Each scenario consists of 10,000 random values generated by the Economic Scenario Generator. The yield and total return data are fit to a normal distribution and the following are derived:

  • Distribution Mean and
  • Distribution Standard Deviation.

 

SUMMARY OF RESULTS

Overall, the yields for bonds and total returns for blue chips stocks and MLP equities increase over time, both as time-step increases and going from the 2016 economic scenarios to the 2018 economic scenarios, reflecting both increasing bond yields and increasing inflation.

What do these increases in bond yields and total returns mean for an insurance company?  The impact depends on the asset mix of the current portfolio and the result is likely to be mixed.  Given that most property casualty companies invest heavily in bonds, the embedded bonds are likely to decrease in value as bonds are marked to market. The increase in bond yields for newly purchased bonds will offset this downward adjustment somewhat. The non-bond portion of the portfolio should have a larger contribution to the bottom line than in the recent past in both dividends and enhanced asset value. Overall, Huggins would expect to see increased investment returns, possibly returning to pre-recession levels.

ANALYSIS BY ASSET CLASS

For two-year “A” rated corporate bonds, the yields increase from 1.66% for one-year time-step to 3.50% for five-year time-step for the 2016 economic scenarios, to 3.32% for one-year time-step to 4.29% for five-year time-step for the 2018 economic scenarios. For five-year “A” rated corporate bonds, the yields increase from 2.13% for one-year time-step to 3.97% for five-year time-step for the 2016 economic scenarios, to 3.59% for one-year time-step to 4.71% for five-year time-step for the 2018 economic scenarios. For ten-year “A” rated corporate bonds, the yields increase from 2.78% for one-year time-step to 4.53% for five-year time-step for the 2016 economic scenarios, to 4.02% for one-year time-step to 5.21% for five-year time-step for the 2018 economic scenarios. See the three tables below.

 

Economic Scenarios – Two-Year “A” Rated Corporate Bonds

 10,000 Yield Scenarios

2016 Two Year

Corporate Bonds

2017 Two Year

Corporate Bonds

2018 Two Year

Corporate Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 1.66% 0.63% 2.45% 0.76% 3.32% 0.90%
3 Year 2.67% 1.07% 3.18% 1.24% 3.79% 1.40%
5 Year 3.50% 1.45% 3.84% 1.56% 4.29% 1.74%

 

Economic Scenarios – Five-Year “A” Rated Corporate Bonds

 10,000 Yield Scenarios

2016 Five Year

Corporate Bonds

2017 Five Year

Corporate Bonds

2018 Five Year

Corporate Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 2.13% 0.46% 2.81% 0.61% 3.59% 0.75%
3 Year 3.14% 0.92% 3.60% 1.09% 4.17% 1.26%
5 Year 3.97% 1.33% 4.28% 1.44% 4.71% 1.59%

 

Economic Scenarios – Ten-Year “A” Rated Corporate Bonds

 10,000 Yield Scenarios

2016 Ten Year

Corporate Bonds

2017 Ten Year

Corporate Bonds

2018 Ten Year

Corporate Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 2.78% 0.37% 3.33% 0.52% 4.02% 0.67%
3 Year 3.74% 0.83% 4.14% 1.00% 4.66% 1.16%
5 Year 4.53% 1.24% 4.81% 1.34% 5.21% 1.49%

 

For Blue Chip Stocks, the total returns increase from 5.04% for one-year time-step to 6.42% for five-year time-step for the 2016 economic scenarios, to 7.06% for one-year time-step to 8.02% for five-year time-step for the 2018 economic scenarios. See table below.

 

Economic Scenarios – Blue Chip Stocks

 10,000 Total Return Scenarios

2016 Blue

Chip Stocks

2017 Blue

Chip Stocks

2018 Blue

Chip Stocks

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 5.04% 16.94% 6.27% 16.50% 7.06% 16.85%
3 Year 5.60% 17.14% 6.91% 16.96% 7.49% 17.09%
5 Year 6.42% 17.12% 7.68% 17.53% 8.02% 17.72%

 

For MLP Equities, the total returns increase from 9.46% for one-year time-step to 10.26% for five-year time-step for the 2016 economic scenarios, to 9.75% for one-year time-step to 11.02% for five-year time-step for the 2018 economic scenarios. See table below.

 

Economic Scenarios – MLP Equities

 10,000 Total Return Scenarios

2016 MLP

Equities

2017 MLP

Equities

2018 MLP

Equities

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 9.46% 18.23% 9.44% 18.12% 9.75% 18.16%
3 Year 9.73% 18.64% 9.88% 18.63% 10.36% 18.76%
5 Year 10.26% 18.59% 10.63% 18.80% 11.02% 18.98%

 

For two-year Treasury bonds, the yields increase from 1.15% for one-year time-step to 2.89% for five-year time-step for the 2016 economic scenarios, to 2.69% for one-year time-step to 3.60% for five-year time-step for the 2018 economic scenarios. For five-year Treasury bonds, the yields increase from 1.50% for one-year time-step to 3.25% for five-year time-step for the 2016 economic scenarios, to 2.85% for one-year time-step to 3.92% for five-year time-step for the 2018 economic scenarios. For ten-year Treasury bonds, the yields increase from 1.94% for one-year time-step to 3.62% for five-year time-step for the 2016 economic scenarios, to 3.09% for one-year time-step to 4.23% for five-year time-step for the 2018 economic scenarios. See the three tables below.

 

 

Economic Scenarios – Two-Year Treasury Bonds

 10,000 Yield Scenarios

2016 Two Year

Treasury Bonds

2017 Two Year

Treasury Bonds

2018 Two Year

Treasury Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 1.15% 0.62% 1.93% 0.75% 2.69% 0.89%
3 Year 2.10% 1.05% 2.61% 1.22% 3.13% 1.38%
5 Year 2.89% 1.43% 3.22% 1.54% 3.60% 1.68%

 

Economic Scenarios – Five-Year Treasury Bonds

 10,000 Yield Scenarios

2016 Five Year

Treasury Bonds

2017 Five Year

Treasury Bonds

2018 Five Year

Treasury Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 1.50% 0.44% 2.17% 0.60% 2.85% 0.74%
3 Year 2.46% 0.91% 2.91% 1.08% 3.40% 1.25%
5 Year 3.25% 1.31% 3.56% 1.42% 3.92% 1.57%

 

Economic Scenarios – Ten-Year Treasury Bonds

 10,000 Yield Scenarios

2016 Ten Year

Treasury Bonds

2017 Ten Year

Treasury Bonds

2018 Ten Year

Treasury Bonds

 

Time-Step

 

Mean

Standard Deviation  

Mean

Standard Deviation  

Mean

Standard Deviation
1 Year 1.94% 0.35% 2.49% 0.51% 3.09% 0.65%
3 Year 2.86% 0.81% 3.25% 0.98% 3.70% 1.15%
5 Year 3.62% 1.22% 3.90% 1.33% 4.23% 1.47%

 

Authored by

GROVER M. EDIE

MBA, FCAS, MAAA, CPCU, ARP, CERA, ARM

and

FRANK SULLIVAN, PH.D.

California REG-2017-0001 Requirements and Impact

California REG-2017-0001 – Actuarial & Financial Implications Regarding Workers’ Compensation High-Deductible Policies

 

The New Requirements:

 REG-2017-0001 is an effort by the California Insurance Commissioner and Department of Insurance to protect California workers by decreasing the risk of insurer insolvency via new regulations concerning workers’ compensation high-deductible policies.

 

The proposed regulation will require the insurer to report uncollateralized Deductible Ultimate Receivables as a loss in its Financial Statements.  The assumption is that “loss” is defined in this case as an impact to the insurer’s income statement rather than specifically as underwriting loss and expense.  The American Academy of Actuaries Committee on Property and Liability Financial Reporting has two recommendations for how this “loss” would flow through the financial statement:

 

  • The Deductible Ultimate Receivables should be separated into the Deductible Recoverable and the Deductible Loss Reserve for reporting purposes
  • The “loss” should be recorded on the write-in line items on the financial statements. This maintains the underwriting loss and expenses in the financial statements on a net of deductible basis not dependent on collateralization

 

The two components recommended for reporting purposes include:

 

Deductible Recoverable

The Committee recommends that non-collateralized Deductible Recoverable be reported as a non-admitted asset.  This is assuming credit risk requirements are not satisfied. This follows the suggestions presented in the SSAP 65 section on high-deductible policies.  The Deductible Recoverable “shall be accrued and recorded as a reduction of paid losses simultaneously with the recording of the paid loss…”  SSAP 65 further defines that the accrual will be either partially or fully reflected as non-admitted to reflect the credit risk of potential non-payment.

 

Deductible Loss Reserves

The Committee recommends insurers report the uncollateralized Deductible Loss Reserves as a write-in liability on the balance sheet.  This is similar accounting treatment to the reinsurance credit risk of losses ceded to reinsurers.  The reported loss reserves are on a net basis while the Provision for Reinsurance Liability line item on the balance sheet is utilized to prevent surplus benefit from amounts ceded to certain reinsurers.  Keeping the credit risk from uncollateralized Deductible Loss Reserves separate from underwriting losses will result in greater transparency for the issue at hand.

 

These recommendations would result in the “loss” being recognized by the insurer.  There would be increased transparency in the financial statements because credit risk and underwriting risk are clearly separated.  It is consistent with SSAP 65, the Statement of Statutory Accounting Principles on high-deductible policies.

 

Impact of REG-2017-0001 and Next Steps

The implementation of this regulation could result in a potentially significant financial impact for certain insurers that would be difficult for them to offset.  These insurers may have more difficulty obtaining sufficient collateral for prior policies that may still have significant Deductible Loss Reserves.  On the other hand, insurers who write future high deductible polices  who in the past have not obtained the collateral requirements specified in the regulation may be able to adopt processes to rectify this situation.

 

Please contact the authors for more information:

James Chang, FCAS, MAAA Email: james.chang@hugginsactuarial.com Phone: 732-881-4663

Kim Piersol, FCAS, MAAA Email: kim.piersol@hugginsactuarial.com Phone: 610-892-1808

Ronald T. Kuehn FCAS, MAAA, CERA, CPCU, ARM, FCA Email: rusty.kuehn@hugginsactuarial.com Phone: 610-892-1823

 

 

 

Driverless Cars and Insurance

How Driverless Cars Affect the Auto Insurance Industry

by James Chang, FCAS, CPCU, ARe, MAAA and Kim E. Piersol, FCAS, MAAA

The American public has mixed feelings about giving up control of their cars’ steering wheels.  Despite the enthusiasm with which autonomous vehicles are being developed by auto manufacturers and technology companies, recent polls show that few drivers are interested in autonomous vehicle technology despite the potential safety and time-saving benefits.

 

Auto insurers are already thinking about how self-driving cars will affect them even though it might be a long time before we see them regularly on the road.  From the invention of the horseless carriage in 1890’s, car insurance has evolved from simple handwritten contracts to the high-tech global industry that it is today.

 

The transition to driverless cars will create the need for more personal coverage and to increase the need for more commercial insurance as car manufacturers will assume much of the risk for this new techology.  However, it is too soon to completely know what the insurance strategy will be now since it could be 25 to 30 years before we transition to driverless cars.

 

Currently, technology companies and auto manufacturers are capturing data, and that data can be used for actuarial models.  Insurers will also need to form partnerships with the winners in the autonomous vehicle space.  Insurers are having conversations with the autonomous vehicle providers to solve problems points and lot of benefits are to be gained through these partnerships.

 

We can speculate where the commercial liability responsibility lies for autonomous vehicle accidents.  The responsibility for liability becomes complicated when multiple parties are involved in an accident.  The fault can be divided between consumer, manufacturer, and software maker.  Manufacturers may be required to purchase insurance for their vehicles in the future.  There may be a battle between the manufacturer and insurance company to see who pays for the accident.  Other trends driverless cars can have on the insurance industry include dropping of insurance premium as accidents decline and more drivers could reduce or drop coverage.

 

There could be new lines of business created as a result from driverless cars.  Cybersecurity, such as protection against remote vehicle theft, ransomware, coverage for identity theft, and theft or misuse of personal data will emerge as well as product liability for software and sensors and public infrastructure insurance for cloud server systems that manage traffic and road network.

 

Industry experts conclude that insurers can take key steps in preparation of a driverless future.

 

  • Build expertise in big data and analytics; insurers must be able to harness the data generated by autonomous vehicles and systems that support them.
  • Develop the actuarial modeling framework by utilizing advanced actuarial modeling techniques as they adapt to the autonomous vehicle features.
  • Collaborate with automakers, software developers, governmental entities, manufacturers and the like to share data and expertise.

 

The business model will change as the auto insurance risks change followed by insurance policy changes.

“Stuff”

Too much clutter in your life?  Read the latest column from Huggins’ consulting actuary Grover Edie, MBA, FCAS, MAAA, CERA, CPCU, ARM, ARP in the latest edition of Actuarial Review.

 

 

CAMAR Presentation – Bermuda EBS Technical Provisions

The presentation given by consulting actuary Chandu C. Patel, FCAS, MAAA at the Fall 2017 CAMAR meeting is available for viewing.

 

Bermuda_Economic_Balance_Sheet_CAMAR_Fall_2017_Final_Huggins (003)

 

 

NAIC Draft Model Law On Cybersecurity by Chandu C. Patel, FCAS, MAAA, Rusty Kuehn, FCAS, MAAA, and Kim Piersol, FCAS, MAAA