Leo Fernandes, Harkeerit Kalsi, Nicholas Vause, Matthew Downer, Sarah Ek and Sebastian Maxted
Hedge funds and different various funding funds (AIFs) usually take positions in monetary markets that considerably exceed their buyers’ capital by utilizing debt or derivatives. Nonetheless, such ‘leverage’ can pose dangers to monetary stability. Regulators looking for to scale back these dangers might think about making use of constraints to the fund entities or the actions through which they have interaction. On this submit, we use a easy portfolio selection mannequin to look at the consequences of the 2 approaches on fund investments. Beneath the entity-based strategy, we discover that fund managers substitute from lower-risk to higher-risk investments, whereas an activity-based strategy can keep away from this unintended reallocation by concentrating on particular investments.
Whereas conventional funding funds sometimes maintain lengthy positions in shares and bonds, AIFs might take lengthy and brief positions in a wider vary of belongings, together with illiquid belongings like actual property or unlisted securities. Specifically, hedge funds make use of a various array of methods that may be centered on a single asset class or span a number of lessons, with giant ‘multi-strategy’ funds concurrently pursuing a number of methods.
AIFs usually leverage their buyers’ capital by borrowing or utilizing derivatives to extend their monetary market exposures and amplify their funding returns. This requires funds to supply a portion of their capital as collateral – or ‘margin‘ – towards their borrowings and derivatives. Regardless of internationally agreed margin requirements placing an higher sure on leverage in lots of areas, funds are sometimes capable of improve their exposures to multiples of their buyers’ capital. Some funds, reminiscent of these pursuing relative worth methods, make use of vital leverage to take notably giant exposures to low-risk belongings, whereas others use much less leverage and take smaller positions in riskier or illiquid belongings. AIFs and their managers are topic to regulatory reporting and oversight beneath the UK’s Various Funding Fund Managers Directive, which requires them to reveal info on their leverage use to buyers however doesn’t sometimes impose direct, entity-level leverage limits.
Such leverage can pose dangers to monetary stability. For example, losses may unfold from any defaulting funds to their counterparties, as with the failure of Archegos Capital Administration. Losses may additionally unfold from any funds promoting belongings to be able to deleverage – maybe due to a sudden improve in margin necessities or decline of their danger urge for food – to holders of the identical belongings, resulting from downward stress on costs arising from the gross sales, notably for gross sales of extra concentrated or illiquid positions, as with liability-driven funding funds in Autumn 2022.
World regulators are contemplating methods to mitigate such dangers. These embrace entity-level leverage limits and activity-level measures, reminiscent of enhanced margin necessities or collateral haircuts. Entity-level leverage limits sometimes constrain fund-level ratios of whole exposures to web asset worth (NAV), whereas enhanced margin necessities and collateral haircuts would increase the decrease sure on the quantity of capital required to finance a specific asset or set of belongings.
Within the the rest of this submit, we apply a easy mannequin of portfolio option to AIFs to indicate how leverage limits and margin necessities would have an effect on their funding selections. An analogous challenge has been a lot studied in banking, each theoretically and empirically, as banks additionally face a leverage constraint (the leverage ratio) in addition to risk-weighted capital prices towards particular person belongings.
Portfolio selection mannequin
We mannequin funds with a given quantity of investor capital. Every fund chooses a amount of debt and the allocation of its portfolio between two methods, the place a ‘technique’ is an funding in a specific asset or set of belongings. We think about two kinds of fund: one invests in extremely correlated methods with related anticipated returns and volatilities, reminiscent of a business actual property (CRE) fund investing in workplace blocks in several cities; and the opposite invests in less-correlated methods with completely different anticipated returns and volatilities, reminiscent of a multi-strategy hedge fund working a long-short fairness technique and a relative worth technique. The funds have a typical danger aversion parameter, which governs the anticipated return they might hand over in alternate for decrease variance on the portfolio stage. Provided that risk-return trade-off, we will decide the optimum portfolios of the funds within the absence of each entity-level and activity-level constraints. Desk A summarises the inputs – which had been guided by analysis on returns and leverage – and outputs of this mannequin.
Desk A: Portfolio choices and decisions for 2 unconstrained funds
Observe: Returns are ‘unlevered’ returns, ie returns on every £1 invested in a method. In precept, they need to be extra returns over the risk-free rate of interest, however we summary from this challenge by assuming the latter is zero. The chance-aversion parameter is 4.
Within the absence of constraints, the CRE fund chooses 3.6x leverage (ie an assets-to-NAV ratio of three.6) and invests equally in each methods. In distinction, the multi-strategy hedge fund chooses considerably extra leverage and tilts its portfolio closely in direction of the low-risk relative worth technique.
We subsequent impose mounted leverage limits and margin necessities and examine how these two kinds of constraints have an effect on portfolio allocations between the methods. The outcomes are proven in Charts 1 and a pair of for the CRE fund and multi-strategy hedge fund respectively. In every chart, the highest row of charts exhibits optimum investments within the two methods and the underside row exhibits contributions of these investments to the portfolio variance. The left-hand column of charts exhibits how these variables change because the leverage restrict is diminished to half of the leverage chosen by the unconstrained funds. The charts within the different columns present the consequences of margin necessities being doubled – first for every particular person technique after which for each – from charges that don’t constrain the funds.
Chart 1: CRE fund portfolio allocations and their contributions to portfolio variance beneath entity-level leverage and activity-level margin constraints
Observe: Shifting from left to proper on the x-axes, the charts present how tighter leverage (first column) or margin (different columns) necessities have an effect on the fund’s portfolio allocation (prime row) and the variance of its portfolio return (backside row).
Chart 2: Multi-strategy hedge fund portfolio allocations and their contributions to portfolio variance beneath entity-level leverage and activity-level margin constraints
Observe: Shifting from left to proper on the x-axes, the charts present how tighter leverage (first column) or margin (different columns) necessities have an effect on the fund’s portfolio allocation (prime row) and the variance of its portfolio return (backside row).
Entity-level constraints
The mounted leverage restrict is efficient in constraining each the full belongings and portfolio danger of the CRE fund (Chart 1, first column). However this result’s pushed by the similarity of its funding choices. In distinction, the multi-strategy hedge fund responds to the leverage restrict by substituting a few of its giant positions within the low-risk (relative worth) technique for smaller ones within the high-risk (long-short fairness) technique (Chart 2, top-left chart). This substitution limits the decline within the fund’s general portfolio danger (Chart 2, bottom-left chart).
If extrapolated throughout a number of funds, such reallocation between methods may have two unintended penalties. First, the centered discount in capital allotted to the low-risk technique may undermine sure financial advantages arising from these investments. For instance, relative-value trades assist to maintain the costs of associated belongings consistent with each other, which promotes the environment friendly allocation of funding. Second, the focus of portfolios into the high-risk technique makes funds extra weak to idiosyncratic shocks to these investments, which may immediate more-severe spirals of deleveraging and asset value falls resulting from extra funds having bigger positions in widespread methods (ie ‘crowded trades’).
Conceivably, a dynamic entity-level constraint that took into consideration not solely the dimensions of investments but in addition their riskiness may comprise such reallocations. Nonetheless, value-at-risk (VaR) is the one such danger metric that’s at present utilized extensively throughout asset lessons and methods, however its estimation bias and different limitations have been properly documented.
Exercise-level constraints
Exercise-level constraints may give regulators the flexibleness to scale back danger from funding actions which can be tough to comprise with entity-level limits. For instance, whereas the entity-level leverage restrict had no constraining impact on the multi-strategy hedge fund’s publicity to the long-short fairness technique (Chart 2, first column), tightening a margin requirement would instantly cut back these investments (Chart 2, second column).
Constraining just one exercise, nonetheless, prompts funds to substitute into the opposite exercise (Charts 1 and a pair of, second and third columns). This danger is particularly pronounced for funds with related funding choices, as they might extra readily permit their portfolio danger to grow to be dominated by an funding substitute (examine bottom-middle charts in Charts 1 and a pair of) and, in so doing, retain a stage of portfolio danger that’s not a lot diminished.
To disincentive substitution into different actions, regulators may apply a number of activity-level constraints. In our mannequin, this reduces the riskiness of fund portfolios with out inducing shifts of their composition for each the CRE fund (Chart 1, remaining column) and the multi-strategy hedge fund (Chart 2, remaining column). That is achieved by setting margin necessities proportional to the riskiness of the methods, eg comparatively excessive values for the long-short fairness technique and comparatively low values for the relative worth technique. Furthermore, when margin necessities are universally utilized, the fund successfully turns into topic to an entity-level restrict that adjusts to the portfolio of methods that it employs at any time.
Because it occurs, risk-sensitive margin necessities are already widespread on account of current regulatory frameworks (reminiscent of these for cleared and non-cleared derivatives) and prudent counterparty danger administration. That mentioned, gaps in protection stay (eg bilateral authorities bond repo haircuts are sometimes zero) and in some instances their present calibration might fail to adequately cowl the monetary stability dangers (eg if hedge funds have crowded into explicit methods).
Conclusion
Our mannequin demonstrates that making use of mounted leverage limits on the entity stage might be efficient for less complicated funds with extremely related belongings or methods. However making use of them to extra complicated funds might have the unintended consequence of accelerating portfolio focus danger or crowding funds into sure methods, whereas lowering liquidity in others. Alternatively, activity-level measures can goal completely different belongings and methods with risk-sensitive constraints that don’t generate an unintended influence on capital allocation, reminiscent of a shift in direction of riskier belongings with much less leverage. In impact, mounted leverage limits set a median value for leverage throughout a portfolio of belongings, whereas margin necessities set marginal costs on particular person belongings, making the latter extra universally efficient in limiting the build-up of danger from leverage. These outcomes exhibit the advantages of insurance policies which deal with leverage dangers from more-complex funds in a risk-sensitive method, for instance, by enhancing activity-level measures.
Leo Fernandes, Harkeerit Kalsi and Nicholas Vause work within the Financial institution’s Market-Primarily based Finance Division; Matthew Downer is a technical specialist on the Monetary Conduct Authority; Sarah Ek is a senior affiliate on the Monetary Conduct Authority; and Sebastian Maxted is an affiliate on the Monetary Conduct Authority.
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