A POSITIVE SOLUTION FOR PENSION BENEFICIARIES VIA A NOBEL PRIZE WINNING VaR VOLATILITY INVESTMENT STRATEGY

Brought to market by a blend of regulated and unregulated service providers, to enable a time efficient and commercially viable business opportunity for IFAs, SIPP Trustees, DFMs and Sub Custodians

The Pension Solution Collaboration (PSC)

Most existing pension arrangements are susceptible to negative performance and market shocks and do not match the investors risk profile. The PSC counterparts aim to provide tangible and measurable benefits to poorly served pension scheme beneficiaries.

PLEASE NOTE: These Materials are for information purposes only and do not constitute an offer or an invitation to acquire or dispose of any investment or investment advice in any jurisdiction.

What Is PSC?

A blend of regulated and unregulated services providers are brought together to enable a time efficient and commercially viable business opportunity for IFAs, SIPP Trustees, DFMs and Sub Custodians

Target Markets

1

Defined Contribution Scheme Members (DC)

2

Defined Benefit Scheme
Members (DB)

PSC Service Providers

Introduction

Market research strongly indicates that the majority of investors prefer to know the level of risk they are taking, and dislike their existing arrangements which are susceptible to negative performance and market shocks

Market research indicates strongly that pension beneficiaries are motivated to switch to access pre determined risk levels and the prospect of improved returns

Nobel Prize Winning Model: You Can’t Predict Returns, but You Can Predict Risks.

In financial services, many try to predict the future movements of asset prices. However, empirical studies have shown it is impossible to do so. Which is why timing the entry into and out of markets is hit and miss and often leads to losses. It is possible, however, to predict risk to a certain extent by monitoring volatility. This means that if the markets exhibit high volatility, i.e. if there is high risk this week, there is a greater than 50% chance that next week will again be volatile. On the other hand, a high return this week doesn't tell us anything reliable about next week's potential return.

Discretionary Fund Managers on the PSC panel apply a digitally managed Value at Risk (VaR) range of globally diversified portfolios predominantly consisting of Exchange Traded Funds (ETFs). The PSC VaR portfolios aim to improve the probability of meeting the pre determined risk preferences of pension investors and increase the probability of improved risk-return profiles.

The Secret of Investment Management

“The essence of investment management is the management of risks, not the management of returns.”

- Benjamin Graham, Warren Buffett’s mentor

VaR Risk Management

Risk is constrained via dynamic rebalancing of each model portfolio following a Value at Risk (“VaR”) volatility management strategy. Portfolios are highly diversified across multiple global asset classes

As volatility changes, the technology makes adjustments to each portfolio to keep it within the VaR constraint

The technology measures risk itself and adopts a systematic approach to asset class weights to ensure each portfolio truly reflects the investors downside risk exposure comfort level

Source: Figure 3, page 47, Yale Study

View Yale Study

Complementary Strategies

The Value at Risk (VaR) volatility managed personalised ETF strategy has been further advanced by the application of complementary strategies:

Optimization of conditional value-at-risk

RT Rockafellar, S Uryasev
Journal of risk, 2000
pacca.info

Portfolio optimization with conditional value-at-risk objective and constraints

P Krokhmal, J Palmquist, S Uryasev
Journal of risk, 2002
Citeseer

Value-at-risk in portfolio optimization: properties and computational approach

AA Gaivoronski, G Pflug
Journal of Risk, 2004
search.proquest.com

Conditional value-at-risk: Optimization algorithms and applications

S Uryasev
Intelligence for Financial Engineering, CIFEr…, 2000
ieeexplore.ieee.org

Conditional value-at-risk for general loss distributions

RT Rockafellar, S Uryasev
Journal of banking & finance, 2002
Elsevier

Conditional value at risk and related linear programming models for portfolio optimization

R Mansini, W Ogryczak, MG Speranza
Annals of operations research, 2007
Springer

A mixed integer linear programming formulation of the optimal mean/value-at-risk portfolio problem

S Benati, R Rizzi
European Journal of Operational Research, 2007
Elsevier

Robust optimization of conditional value at risk and

portfolio selection

AG Quaranta, A Zaffaroni
Journal of Banking & Finance, 2008
Elsevier

On the covariance matrices used in value at risk models

CO Alexander, CT Leigh

Value at risk models in finance

S Manganelli, RF Engle
2001
papers.ssrn.com

Value-at-risk vs. conditional value-at-risk in risk management and optimization

S Sarykalin, G Serraino
State-of-the-Art Decision, 2008
pubsonline.informs.org

Algorithms for optimization of value-at-risk

N Larsen, H Mausser, S Uryasev
Financial engineering, E-commerce …, 2002
Springer

Value-at-risk based portfolio optimization

A Puelz
Stochastic Optimization: Algorithms and Applications, 2001
Springer

Portfolio optimization under the value-at-risk constraint

TA Pirvu
Quantitative Finance, 2007 - Taylor & Francis

Portfolio optimization problems in different risk measures using genetic algorithm

TJ Chang, SC Yang, KJ Chang
Expert Systems with Applications, 2009
Elsevier

Portfolio optimization with linear and fixed transaction costs

MS Lobo, M Fazel, S Boyd
Annals of Operations Research, 2007
Springer

Conditional value-at-risk: optimization approach

S Uryasev, RT Rockafellar
Optimization: algorithms and applications, 2001
Springer

Regulatory evaluation of value-at-risk models

JA Lopez
1997
papers.ssrn.com

Portfolio optimization with conditional value-at-risk objective and constraints

J Palmquist, S Uryasev, P Krokhmal
1999
smartquant.com

Evaluating value-at-risk models with desk-level data

J Berkowitz, P Christoffersen
Management…, 2011
pubsonline.informs.org

Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization

C Lim, HD Sherali, S Uryasev
Computational Optimization…, 2010
Springer

Five VaR Portfolios

Cautious VaR -3%
Cautious/Moderate VaR -8%
Moderate VaR -13%
Moderate/Adventurous VaR -18%
Adventurous VaR -25%

One year VaR 5%, meaning a 95% probability that the portfolios will not decline by more than the VaR limit in any one year period

Better Returns in Appreciating Markets

Value at Risk (VaR) volatility managed strategies can actually perform better in appreciating markets

VaR volatility isn't just about risk management

Three examples follow, these examples track the period from 2007 to 2016 comparing a buy and hold portfolio of seven asset classes, stocks, government bonds, corporate bonds, property, commodities, gold, cash to a VaR volatility model

Portfolio Performance (historical index data)

Click on the portfolio performance graphs below for a larger view

Cautious (-3%)

Click to Enlarge

Moderate (-13%)

Click to Enlarge

Adventurous (-25%)

Click to Enlarge

Dynamic Rebalance (Heat maps)

These 3 examples show the dynamic allocation adjustments of the VaR Volatility performance records detailed in the previous charts.

Index Allocation - Cautious (-3%)

Click to Enlarge

Index Allocation - Moderate (-13%)

Click to Enlarge

Index Allocation - Adventurous (-25%)

Click to Enlarge

Fully Regulated Compliant Process

The process has been designed to enable a high volume of switch and transfer clients to gain access to pre determined risk constraint levels and improved pension investment performance via a compliant and streamlined work flow process

A blend of regulated and unregulated services providers are brought together to enable a time efficient and commercially viable business opportunity for IFAs, SIPP Trustees, DFMs and Sub Custodians

The Client Journey

Each PSC client will run through a linear onboarding process.
  1. Marketing company contact opted-in prospects
  2. Prospects are existing clients of the marketing company or have requested a pension health check
  3. IFA under contract with marketing company
  4. Prospect referred to an IFA
  5. Fact find and switching report created by IFA, outsourced to third party paraplanning firm
  6. Reports sent to prospect
  7. Prospect agrees to go ahead with IFA, client becomes a client of the IFA
  8. Outsourced document collectors collect documents from client
  9. Application processed at third party paraplanning firm for IFA
  10. Application signed off by IFA
  11. DFM sign off DFM application form and conduct their own suitability assessment of the client
  12. DFM can agree or disagree with the IFA advice
  13. IFA opens SIPP, outsourced to third party paraplanning firm
  14. SIPP opened, funds transferred to SIPP
  15. SIPP transfers money to sub-custodian
  16. On the next monthly dealing day, start trading
  17. DFM carries out dynamic rebalancing of CVaR
  18. Client receives monthly statement of account
  19. Annual IFA servicing of client risk profile

Panel Membership Opportunity

Panel membership of the PSC qualifies IFAs, SIPP Trustees, DFMs and Sub Custodians with a steady flow of new clients with no marketing investment

For DC switches, IFAs receive a pre prepared switch report and compliance summary which enables them to advise each client from a desk top review, meaning substantial fee increases with minimal time investment and low compliance risk

SIPP Trustees, DFMs and Sub Custodians on-board a steady flow of clients that have been fully advised by panel member IFAs following a fully justified switch or transfer

Contact Us

Please contact us at admin@thepensionsolution.co.uk with any questions