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Schedule
TC3 Computational long-term finance
[ORGANIZED SESSION]
Session organizer: Giorgio Consigli, University of Bergamo, Italy

Chair: Kourosh Marjani Rasmussen, Technical University of Denmark, Denmark
Room 3.1.11

 

TC3.1
Computer-aided personal financial planning with medium and long-term goals

Giorgio Consigli (giorgio.consigli@unibg.it), Vittorio Moriggia (vittorio.moriggia@unibg.it)
We present an asset-liability management (ALM) model for individuals facing a long term allocation problem over a diversified investment universe including mutual and pension funds as well as unit linked contracts with corporate, fixed income or equity exposure. Inflation-adjusted living costs and investment or consumption targets over the planning horizon lead to the definition of a comprehensive decision support tool, whose key building blocks are discussed with reference to a case-study. A structured stochastic model for long-term scenario generation is also briefly analyzed.

Keywords: individual asset-liability management, financial and insurance products, multistage stochastic programming

 

TC3.2
Life-cycle wealth management for individuals

Kourosh Marjani Rasmussen (kmra@dtu.dk), Margrét Sesselja Otterstedt (mso@schantz.com)
Optimal financial planning for household considers accumulation and depletion of wealth over the life span of a family. In the accumulation period the major concern is allocation of available savings into a retirement environment, free asset environment or repayment of debt given that the returns on savings and interest rates on loans as well as household income are uncertain. In the depletion period the planning includes the order in which different saving accounts are depleted in the face of longevity risk. Over both periods it is all important that tax rules and calculation of welfare benefits are taken into account explicitly. Modelling the complete problem under uncertainty for most jurisdictions appears to be a daunting computational task. Besides, the use of such models is not likely to be embraced immediately by the practical financial advisor. This is why we suggest an optimization-simulation framework to be gradually built in order to capture the most important features relevant to a given jurisdiction, in our case, for the Danish market. We have developed a deterministic optimization model containing all legal and practical details of the Danish market. The purpose of this model is to deliver base-case results which can be tested across a number of scenarios for market risk, income risk and longevity risk. This is used as a benchmark for comparisons with results of a stochastic model which is a simplified version of the deterministic model as far as the legal and practical constraints are concerned but an enhancement as far as risks are concerned. The stochastic model must be detailed enough to generate robust results in comparison to the results of the detailed deterministic model. We conclude that such optimization-simulation framework is necessary to determine the right trade-off in between model realism and a sufficient degree of explicit risk-modelling.

Keywords: Financial planning for household, Computational long-term finance, Practical financial advisory tool

 

TC3.3
Goal Driven Investing: Targeting Individual Liabilities Using Stochastic Programming

Jung Hun Kim (jung.kim@pioneerinvestments.com), Matteo Germano (Matteo.Germano@pioneerinvestments.com), Francesco Sandrini (Francesco.Sandrini@pioneerinvestments.com), Viviana Gisimundo (viviana.gisimundo@pioneerinvestments.com)
The past years have been witness to the continuing and accelerating shift of Defined Benefit (DB) pension plans towards a Defined Contribution (DC) format. Ongoing regulatory and accounting reforms will only serve to reinforce this trend and consequently, DC will be the dominant pension plan system in the majority of countries worldwide. This transition from DB to DC, most significantly, shifts the investment risk from corporations to plan beneficiaries. Households are now finding their pension plans exposed to dynamic market developments and to a wide array of risk factors ranging from interest rate movements and global/local political events, and consequently, to increasing funding gaps and insolvency. In light of such risk exposures, the pension plan participants foresee an increased need for flexible and highly tailored Strategic Asset Allocation (SAA) to meet their specific retirement needs. Traditional asset allocation strategies often rely on static, single-period mean optimization techniques, shown empirically to be inadequate for the dynamics of short and medium term evolution of asset prices and risk-factor correlations. Additionally, individual plan participants are bound to have different objectives, constraints and preferences. The introduction of target-date and life-cycle funds addresses some of these issues by automatic portfolio adjustment of asset allocation and risk throughout the time horizon. However, even such strategies ultimately prove overly simplistic and leave much room for improvement. An investment strategy coherent with individual needs is necessary in addressing the increased risk exposures and meeting the client’s expectations. This research explores improvements in the solvency and suitability of the proposed solutions with respect to static and target-date portfolio strategy. We introduce a dynamic and tailor-made approach taking advantage of the stochastic programming . Given recent capital market trends and observed insights in the evolution of the pension plan universe, such an approach is useful to maximize a portfolio’s solvency likelihood while controlling for downside risk. In this problem context, the objective function is linked to the coverage percentage of the pension gap (defined as the difference between the amount needed to live comfortably and the amount received by the pension). Client specific characteristics such as salary model are modelled together with other classical portfolio allocation inputs. In such a framework, the salary evolution will affect both portfolio wealth (as contributions) and liability/ target (defined as percentage of last salary). The analysis opens the discussion for understanding potential implications and developments in the real world. An individual ALM study for various employees’ cohorts could drive to more suitable investment strategies solutions to offer. Combined with a sound financial advice and investment education, such solutions could represent a realistic and informed pension plan portfolio strategies.

Keywords: , ,