arxiv: 2605.05089 · v1 · submitted 2026-05-06 · 💱 q-fin.TR

Recognition: unknown

Dynamic Collateral Control for Permissionless Spot Perpetual Basis Trading

Anatoly Krestenko, Danila Bolotin, Mikhail Butov, Rostislav Berezovskiy

Pith reviewed 2026-05-09 16:10 UTC · model grok-4.3

classification 💱 q-fin.TR

keywords spot-perpetual basis tradingcollateral controldecentralized financerisk-constrained optimizationdynamic allocationfunding ratesexecution frictionsvolatility stress

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The pith

Risk-constrained collateral allocation offers a more robust benchmark than economic optimization for spot-perpetual basis trades as volatility increases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper models permissionless spot-perpetual basis trading as a collateral control task in which traders maintain spot inventory, short a perpetual to hedge direction, and split capital between inventory and margin while facing on-chain liquidity and execution limits. It solves the static version of the problem and finds that a risk-constrained choice of collateral share produces steadier operating points than pure economic optimization, with required collateral rising steadily under volatility stress and remaining lowest for BTC while climbing sharply for volatile assets such as LINK and DOGE. An asymmetric dynamic extension sets a lower intervention bound by solvency needs and an upper bound by the balance between carry loss and rebalancing cost; Monte Carlo runs show the solvency bound stays active in most regimes while upper triggers appear mainly when carry is high and costs low. Execution tests and historical backtests reveal sizable realized wedges that worsen when selling the basis, supporting minimum trade sizes and buffers, and indicate that funding conditions account for most of the realized performance under a fixed rule.

Core claim

The risk-constrained formulation of the collateral share provides a more robust operating benchmark relative to the economic optimum, with the required collateral rising monotonically under volatility stress, while the asymmetric dynamic extension keeps the solvency-driven lower boundary structurally relevant and the upper boundary active only in high-carry low-cost regimes, and live validation shows funding environment as the dominant driver of performance.

What carries the argument

The collateral share allocated between spot inventory and derivative margin, controlled statically by risk constraints and dynamically by asymmetric solvency and carry-rebalancing boundaries.

If this is right

Required collateral increases monotonically with volatility stress across assets.
Collateral needs stay lowest for BTC and rise substantially for long-tail assets such as LINK and DOGE.
The solvency-driven lower intervention boundary remains relevant across Monte Carlo regimes.
Upper intervention triggers driven by carry-rebalancing trade-offs activate mainly under high carry and low costs.
Realized performance under a fixed control rule is predominantly explained by the funding environment, with larger execution wedges when selling the basis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar risk-constrained allocation could be applied to other DeFi margin strategies to test whether volatility sensitivity varies by asset class.
Live monitoring of funding rates could be prioritized over other signals when setting dynamic rebalancing thresholds.
Platform-level execution improvements that reduce selling wedges might expand the set of regimes where upper triggers become active.

Load-bearing premise

Monte Carlo simulations and historical backtests accurately reflect the liquidity frictions, execution costs, and market conditions of live on-chain spot-perpetual trading.

What would settle it

A side-by-side live implementation on a permissionless venue during a volatility spike that compares liquidation frequency and risk-adjusted returns between the risk-constrained collateral share and the economic optimum share.

Figures

Figures reproduced from arXiv: 2605.05089 by Anatoly Krestenko, Danila Bolotin, Mikhail Butov, Rostislav Berezovskiy.

**Figure 1.** Figure 1: Direct venue comparison for Variant 2 at the benchmark view at source ↗

**Figure 2.** Figure 2: Comparative simulation evidence. Top row: sensitivity view at source ↗

**Figure 3.** Figure 3: Reduced form hedge leg leverage dynamics implied by view at source ↗

**Figure 4.** Figure 4: Bootstrap robustness of the dynamic lower boundary view at source ↗

**Figure 6.** Figure 6: Monte Carlo upper-bound evidence across exchanges view at source ↗

**Figure 7.** Figure 7: Historical validation under alternative funding environ view at source ↗

**Figure 8.** Figure 8: Historical validation under alternative funding environ view at source ↗

**Figure 9.** Figure 9: Expected versus realized effective execution cost by side. view at source ↗

**Figure 11.** Figure 11: Sell-basis trade history and floating sell-basis target. view at source ↗

**Figure 12.** Figure 12: Trade-level and capital-weighted success as functions view at source ↗

**Figure 13.** Figure 13: Raw funding diagnostics for BTC across calibration windows on the centralized and decentralized benchmarks. view at source ↗

**Figure 14.** Figure 14: Normalized strategy NAV across all tickers under the two funding environments. Solid lines denote Binance funding; view at source ↗

read the original abstract

We study permissionless spot--perpetual basis trading in decentralized finance as a collateral control problem. The strategy holds spot inventory, hedges directional exposure with a short perpetual, and allocates capital between spot inventory and derivative margin under on-chain liquidity and execution frictions. The paper delivers three results. First, it solves a static control problem for the collateral share and shows that the risk-constrained formulation provides a more robust operating benchmark relative to the economic optimum. In comparative calibration, the required collateral rises monotonically under volatility stress. The collateral is the lowest for BTC and increases significantly for long tail assets such as LINK and DOGE. Second, the paper derives an asymmetric dynamic extension in which the lower boundary of intervention is solvency driven, and the upper boundary is determined by a trade-off between carry-loss and the cost of rebalancing. Monte Carlo simulation shows that the lower boundary remains structurally relevant, whereas meaningful interior upper triggers survive mainly in the regimes with high carry and low costs. Third, the paper validates an execution-aware implementation with live routed execution and historical backtests. The execution layer shows that the realized wedges are significant, but become worse in the case of selling the basis. This justifies a minimum effective rebalancing size and a positive execution buffer. The historical validation shows that in the case of a fixed control rule the realized performance is predominantly explained by the funding environment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper frames collateral allocation in DeFi spot-perpetual basis trading as a control problem and delivers asymmetric dynamic rules that look practically useful, though the supporting simulations need more visible detail to judge fully.

read the letter

The main thing to know is that the work solves a static collateral-share problem showing the risk-constrained version holds up better than the pure economic optimum, with required collateral rising steadily under higher volatility and staying lowest for BTC while climbing for assets like LINK or DOGE. It then adds an asymmetric dynamic layer where the lower intervention bound is set by solvency and the upper bound trades off carry loss against rebalancing cost, and Monte Carlo runs indicate the lower bound stays relevant while interior upper triggers only matter in high-carry, low-cost regimes. The execution-aware backtests close the loop by showing larger realized wedges on the sell side and confirming that fixed-rule performance tracks the funding environment more than anything else.

Referee Report

2 major / 2 minor

Summary. The paper models permissionless spot-perpetual basis trading in DeFi as a collateral control problem. It solves a static optimization showing that a risk-constrained collateral allocation is more robust than the pure economic optimum, with collateral requirements rising monotonically under volatility stress (lowest for BTC, significantly higher for LINK and DOGE). It derives an asymmetric dynamic policy with a solvency-driven lower boundary and a carry-versus-rebalancing-cost upper boundary. Monte Carlo simulations indicate the lower boundary remains relevant across regimes while upper triggers appear mainly in high-carry, low-cost settings. The approach is validated via execution-aware live routing and historical backtests, which show significant realized wedges (worse when selling the basis), justify minimum rebalancing sizes and buffers, and attribute most performance variation to the funding environment.

Significance. If the results hold, this supplies a practical, robustness-focused framework for collateral management in decentralized basis trading that explicitly incorporates on-chain liquidity and execution frictions. The static benchmark, asymmetric dynamic rules, and execution-aware validation could directly inform live trading systems and risk controls in permissionless markets.

major comments (2)

[Monte Carlo simulation] Monte Carlo simulation section: the claim that upper-boundary triggers survive 'mainly in the regimes with high carry and low costs' is central to the dynamic extension but lacks the specific carry rates, cost parameters, and volatility levels used. Without these values or a sensitivity table, it is difficult to assess whether the result is robust or parameter-specific.
[Historical validation] Historical validation section: the statement that 'realized performance is predominantly explained by the funding environment' is load-bearing for the third result. A quantitative measure (e.g., R² from a regression of PnL on funding rates, or a variance-decomposition table) is needed to substantiate 'predominantly'.

minor comments (2)

[Abstract] Abstract and introduction: several results are summarized without reference to the underlying equations or objective functions (e.g., the exact form of the risk constraint or the carry-loss term). Adding one or two key equations would improve traceability.
[Execution-aware implementation] Execution layer description: the observation that 'realized wedges are significant, but become worse in the case of selling the basis' would benefit from a table of average wedge sizes by direction and asset.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation of minor revision. We address the major comments point by point below.

read point-by-point responses

Referee: [Monte Carlo simulation] Monte Carlo simulation section: the claim that upper-boundary triggers survive 'mainly in the regimes with high carry and low costs' is central to the dynamic extension but lacks the specific carry rates, cost parameters, and volatility levels used. Without these values or a sensitivity table, it is difficult to assess whether the result is robust or parameter-specific.

Authors: We appreciate the referee's observation. The Monte Carlo simulations were conducted using specific parameter sets for carry rates, rebalancing costs, and volatility levels, which we will now explicitly document in the revised manuscript. We will add a table listing the baseline parameters (e.g., carry rates ranging from 0.01% to 0.5% daily, cost parameters from 0.1% to 1%, volatility from 20% to 100% annualized) and include a sensitivity table demonstrating the frequency of upper-boundary triggers across these regimes. This will confirm that the result holds primarily in high-carry, low-cost settings while remaining robust. revision: yes
Referee: [Historical validation] Historical validation section: the statement that 'realized performance is predominantly explained by the funding environment' is load-bearing for the third result. A quantitative measure (e.g., R² from a regression of PnL on funding rates, or a variance-decomposition table) is needed to substantiate 'predominantly'.

Authors: We agree that providing a quantitative measure will strengthen the validation. In the revised version, we will include a regression of the realized PnL on the funding rates, reporting the R² value to quantify the explanatory power of the funding environment. We will also consider adding a variance decomposition if space permits, to show the proportion of performance variation attributable to funding versus other factors. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The paper formulates a static collateral control problem, solves it to compare risk-constrained vs. economic optimum benchmarks, derives an asymmetric dynamic extension with boundaries, and validates via Monte Carlo plus historical backtests. No quoted equations or steps reduce a claimed prediction or result to a fitted parameter by construction, nor rely on load-bearing self-citations for uniqueness or ansatz. Monotonicity under volatility stress and robustness claims follow from the solved control problem and simulation inputs rather than re-labeling fitted values. Execution-aware validation uses independent live and historical data. This is the common honest non-finding for model-based control papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no specific free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.0 · 5553 in / 1222 out tokens · 47439 ms · 2026-05-09T16:10:23.906727+00:00 · methodology

discussion (0)

Reference graph

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