Assessment of the new investment limits for assets of Social Security Regimes for Public Servants established by Resolution CMN 3,922/2010.
| Autor | Damasceno, Alexandre Teixeira |
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Introduction
The Social Security Regimes for Public Servants (RPPSs) is a system established within the scope of each federative entity--union, states, Federal District, and municipalities--that ensures that civil servants at least have retirement and pension for death benefits. Each RPPS has its own management responsible for organizing the social security of public servants. These can be both active public servants as well as those already receiving benefits (retirees) and pensioners, whose benefits are paid by the federative entity. The objectives of the main RPPSs are to administer and manage pension funds and to manage and operationalize the institution itself, including granting, paying, and maintaining benefits for participants.
In Brazil there are currently 2,216 RPPSs, which together have approximately BRL 277.33 billion (Brasil, 2017), approximately 4.07% of Brazil's GDP, of funds invested in guarantee assets. The National Monetary Council (CMN), through Resolutions 4,604/2017 and 4,695/2018, amended Resolution 3,922/2010, defining new limits for the application of RPPS funds. The period leading up to these changes was marked by numerous governance and interference problems that involved state-owned pension funds and the RPPSs, which were widely reported by the media (i). In light of this problematic environment, the new regulation introduced more rigid and restrictive investment rules for RPPSs, which may imply a significant reduction in the profitability of admissible portfolios.
According to modified Resolution 3,922/2010, there are four levels of governance that RPPSs can adhere to, representing different degrees of complexity, ranging from Level I, the simplest, to Level IV, the most advanced. For an RPPS to obtain a certain level of governance, it has to adhere to the Pro-Management program of the Secretariat for Social Security and be approved. To achieve the highest levels of governance (III and IV) there must be a robust organizational structure with a greater number of technicians and a higher cost of maintaining the staff of the unit responsible for the investment management of the RPPS. For each of these levels, preceding governance conditions have to be implemented and proven. The higher the level of governance of a RPPS, the greater the investment limits for risky assets, as shown in Table 1.
These regulatory changes were made during a historical moment when real interest rates were falling. Currently, Brazilian federal government bonds offer real rates below the RPPS actuarial targets. Therefore, RPPSs will need to seek investments in risky assets that offer greater returns to guarantee the payment of the agreed retirement benefits. Among the assets allowed by Resolution 3,922/2010, investment funds stand out: private credit fixed income (FI RF CP), hedge funds (FIM), stock funds (FIA), private equity funds (FIP), real estate (FII), overseas investment funds (FIM IE), and infrastructure (FIP-IE). These can offer higher returns than the actuarial target rates, but they are riskier.
The central hypothesis of this article is that RPPSs will not obtain satisfactory economic results capable of generating portfolios that exceed their actuarial goals, given the current low interest rate scenario, despite the recent easing of RPPS investment limits (Table 1). Therefore, the objective is to verify if the new RPPS investment limits introduced by Res. CMN 3,922/2010 enable RPPSs to build diversified investment portfolios with returns capable of reaching and exceeding their actuarial goals. Additionally, we also aim to measure the probability of these portfolios reaching the actuarial goals. Thus, it will be possible to understand the trade-off between the increased cost of implementing governance policies vis-a-vis the economic and actuarial benefit provided by the increase in investment limits in risky asset classes.
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THEORETICALBACKGROUND AND EMPIRICAL LITERATURE
The assets and liabilities management in entities that manage pension plans (encompassing both public and private pension plans as well as the RPPS) is a problem of long-term intertemporal allocation choices. For this task, the deterministic models are limited, since the economic and actuarial projections needed to estimate both assets and liabilities are based on static parameters and are, of course, inadequate to deal with the uncertainty of variables such as interest rates, inflation, wage growth, price projections, mortality rates, and other economic and actuarial variables (Ribeiro & Sagastizabal, 2015; Valladao, 2008). Also, these models do not allow the use of assets with uncertain cash flow, such as variable income assets. In addition, the regulations covering pension funds impose a number of restrictions on asset allocation that deterministic models find it hard to cope with. For this reason, more complex stochastic ALM models with greater control over the variables began to be developed in the 1990s and were essential to addressing these challenges.
The development of ALM models is not recent. Leibowitz et al. (1992) conduct an important and comprehensive historical review of their evolution. The first ALM models applied to pension funds were the Dedication Models (DMs) that emerged in an economic environment with high interest rates. These models were deterministic and their main objective was to build a portfolio of fixed income securities with the lowest possible price, aiming to allocate a fixed income security with the same maturity date to each of the liability cash flows. The main advantages of these models are predictability of cash flows, reduction of reinvestment and market risks, passive (less costly) management, and, finally, more simplified asset allocation (100% in fixed income securities). The disadvantages are the difficulty in building the portfolio (finding securities with suitable maturities for the liabilities), the high mathematical complexity of the models, the need to accurately project the actuarial liabilities, and the fact that the economic efficiency of the strategy is limited to scenarios with high interest rates (Ryan, 2013).
The DM model is more appropriate in situations where market interest rates are higher than the rates for actuarial targets. This is not the current scenario in Brazil. Immunization Models (IMs) gradually replaced DMs (Ryan, 2013). This new generation of IM models aimed to build fixed income securities portfolios with the highest returns possible subject to the restriction that the optimal portfolio duration (ii) and convexity (iii) were equal to those of the liabilities flow.
In Brazil, with the Real Plan (1994), inflation stabilized, allowing actuarial projections to be more accurate and reliable. As a result, ALM models began to be adopted more often by institutional investors. Saad and Ribeiro (2004), for example, used two models from the IM family and one DM model. The IM models in their first version used portfolio optimization models with duration restriction and later also included the convexity restriction. To mitigate the limitations of the deterministic model in dealing with the variability of future scenarios, Saad and Ribeiro (2006) presented a variant of the DM, in which the authors included two penalty factors applied to the assets: first, volatility; and second, risk aversion.
Some ALM models deal with liabilities in a deterministic way (as input parameters) and stochastic modeling applies to asset returns and the yield curve. These models use the so-called "asset only method." Models also observed in the literature treat both asset and liability variables at random, establishing stochastic models to simulate the future behavior of returns, wage growth, inflation, interest rates, and mortality rates, with the objective of estimating assets and liabilities stochastically (Dempster, Germano, Medova, & Villaverde, 2003; Drijver & Haneveld, 2002; Hurtado, 2008; Valladao, 2008; Ziemba, 2003).
ALM models started to combine stochastic modeling with optimization techniques under uncertainty scenarios; that is, decision-making models applied to simulated scenarios that more accurately adjust the random behavior of assets and liabilities. These optimization techniques and methods are known as stochastic linear programming (SLP). They are now applied to asset and liability management. This type of model makes it possible to include complex rules and restrictions that are necessary for incorporating real situations that investors consider, thus providing more accurate ALM models (Geyer & Ziemba, 2008; Hosseinzadeh; & Consigli, 2017; Lauria & Consigli, 2017).
In order to make optimization models feasible and efficient, techniques for reducing the dimensionality of the data have been developed, such as building scenarios in multinomial trees. The latest-generation models have started to use techniques such as the decision support system (DSS) in conjunction with SLP, to deal with large numbers of variables, restrictions, and scenarios (Dutta et al., 2019; Rao et al., 2018). An interesting application of ALM models using SLP was developed by Andongwisye et al. (2018), in which a pay-as-you-go social security system was assessed for its long-term equilibrium.
Ferstl and Weissensteiner (2011) developed an ALM model using SLP to build optimal portfolios that minimize the CVaR (conditional value at risk) of the difference in mark-to-market value between assets and liabilities. The stochastic model used to simulate future stock returns and interest rates was the first order vector autoregression--VAR (1)--and the yield curve parameters were estimated using the Cox-Ingersoll-Ross (CIR) model. The liability flow was treated in a deterministic way, so that the stochastic treatment is restricted to the asset part.
Another study that uses CVaR as a quantitative risk constraint in ALM for pension funds is that of Toukourou and Dufresne (2018). They use two CVaR...
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