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.

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.

 

 

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