Segmented Arbitrage: Siriwardane, Sunderam & Wallen (2025)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 48 pages. To replicate or extend it, obtain the full source via the DOI (paywalled).

TL;DR

Using daily data on 32 no-arbitrage spreads across equity, fixed income, and foreign exchange markets over January 2010 to February 2020, the paper documents that the average pairwise correlation of arbitrage spreads is only 0.22, far below what integrated-intermediary models predict. The paper argues this reflects two types of segmentation: (i) funding segmentation, where certain trades (equity spot-futures, equity options box, CIP) rely on unsecured funding while others rely on secured (repo) funding, so that shocks to unsecured funding markets raise unsecured spreads but not secured ones; and (ii) balance-sheet segmentation, where intermediaries specialize in certain trades, so idiosyncratic balance-sheet shocks (JPMorgan London Whale 2012, Deutsche Bank CDS exit 2014) move specific arbitrage spreads but not others. A sign-restricted SVAR shows the high-dimensional factor structure of spreads is driven largely by weakly correlated supply shocks on the arbitrageur side.

Core results

Magnitudes and significance are as reported; */** = 10%/5%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Average pairwise correlation of arbitrage spreads is 22%, far below the single-factor benchmark	Table II Panel A, p. 2560	Mean ρ = 0.22, SD = 0.30; 75th pctile = 0.42; 90% of pairs reject H₀: ρ > 0.67 (p = 0.00); N = 496 pairs
R2	Low correlations persist even within same-tenor trades, ruling out measurement error and noise-trader risk	Table II Panels B–D, pp. 2560–2561; Figure 3, p. 2562	Short-tenor mean ρ = 0.19; after 1-month moving average, 10 PCs needed to explain 90% of variation; overnight IOER–GCF pair ρ = 23%
R3	Supply shocks from sign-restricted SVAR are weakly correlated (avg 16% pairwise), not demand shocks	Figure 4, p. 2567	Average ρ of supply shocks across all futures-based trades = 16%; average ρ of demand shocks also 16%; supply shocks within equity SF cluster higher at 62%; 1% upper bound on quarterly supply-shock correlations = 37%
R4	Unsecured arbitrages load strongly on TED spread; secured arbitrages do not	Table III, p. 2569	Unsecured β(TED) = 0.49** (t = 4.58); secured β(TED) = 0.07 (t = 1.33, insignificant); unsecured spreads approximately 7× more sensitive to TED than secured
R5	2016 MMF reform raised unsecured arbitrage spreads by ~12 bps; secured spreads unaffected	Table IV col. (1), p. 2573; Figure 5, p. 2571	β = 11.77** (t = 2.47); dynamic estimates show initial spike of 18.03** at reform month, elevated for 3+ months; pass-through ≈ 0.59, matching OLS TED coefficient
R6	Fidelity IPrime MMF outflows specifically move equity spot-futures spreads but not other unsecured or secured spreads	Table V, p. 2575	OLS: Fidelity flows coef = −0.55** (t = −3.86) for equity SF; IV estimate = −1.09** (t = −2.25); CIP/Box coef = −0.14* (t = −1.84, significant at 10% only); secured coef = 0.01 (insignificant)
R7	JPMorgan London Whale balance-sheet shock raised equity spot-futures spreads relative to other unsecured spreads	Figure 7C, p. 2582	Equity SF spreads significantly higher vs. other unsecured arbitrages following March 1, 2012 and June 13, 2012; widening persisted for several months; JPM CP rates unchanged, ruling out funding channel
R8	Deutsche Bank’s 2014 CDS market exit raised CDS-bond arbitrage spreads relative to other secured spreads	Figure 8B, p. 2584	Effect significant at 5%; relative widening persisted over 5 months; other secured and unsecured arbitrage spreads unaffected
R9	Fixed-income hedge fund losses predict future increases in secured (not unsecured) arbitrage spreads	Table VII, p. 2586	Secured β(FI Arb HF Return_{t-1}) = −0.66** (t = −3.04); unsecured β = 0.00 (t = 0.01); driven by Treasury-swap and CDS-bond sub-strategies

Overall (paper’s conclusion). Riskless arbitrage is segmented. Both funding segmentation (unsecured vs. secured funding markets) and balance-sheet segmentation (intermediary specialization) drive low correlations across arbitrage spreads. The evidence implies that intermediary asset pricing models most naturally describe individual market segments rather than capital markets as a whole.

Datasets used

Dataset	Role in paper	Wiki page
Bloomberg (spot rates, FX forwards, OIS rates, futures prices, Treasury yields, inflation swaps, CDS via Markit)	Construction of all 32 arbitrage spreads; FX CIP, equity spot-futures, Treasury spot-futures, Treasury-swap, TIPS-Treasury series	Bloomberg (licensed)
van Binsbergen, Diamond & Grotteria (2019) box rates extended by authors using CBOE SPX options data	Equity options (box arbitrage) spreads at 6-, 12-, 18-month tenors	No page yet
Markit (cash bond and CDS pricing)	CDS-bond basis construction for IG and HY bonds	Markit bond pricing / Markit CDS (licensed)
CFTC Commitments of Traders (Traders in Financial Futures, weekly)	Quantities data on positions by dealer, hedge fund, and asset-manager type for futures-based trades	CFTC COT
Crane data / SEC Form N-MFP	MMF total net assets and holdings, for MMF reform analysis	Crane Data (licensed)
Preqin Pro Hedge Fund Database	Hedge fund returns data; fixed-income arbitrage strategy flag	Preqin (licensed)
Federal Reserve yield curve models (Gurkaynak, Sack, Wright 2007/2010)	Zero-coupon constant-maturity Treasury and TIPS yields for TIPS-Treasury arbitrage	No page yet
CRSP / Compustat (via WRDS)	Supporting equity holdings data (Y-9C bank trading book filings cited; CRSP implied for stock characteristics)	WRDS / CRSP / Compustat (licensed)
Coalition Greenwich / S&P (qualitative)	JPMorgan equity derivatives market share since 2015 (cited contextual fact)	No page yet

The CIP spread construction and FX arbitrage measurement follow the methodology of Du, Tepper, and Verdelhan (2018).

Sample: 32 arbitrage spreads, daily, January 1, 2010 to February 29, 2020 (post-GFC, pre-Covid). CDS-bond and Treasury-swap series start Sep 2011 for some maturities. CFTC quantity data weekly from July 2010.

Theory / model

The paper develops a stylized model (Section I, pp. 2548-2553) in which a unit measure of competitive, atomistic arbitrageurs (intermediaries) trade N riskless arbitrage trades. The arbitrageur’s objective is (eq. 1, p. 2549):

$$\max_{q_{n,t},\, V_{k,t}} \sum_{n=1}^{N} q_{n,t} \left( s_{n,t} - \sum_l w_{n,l} f_{l,t} \right) - \frac{1}{2} \sum_{k=1}^{K} c_{k,t} V_{k,t}^2$$

where $s_{n,t}$ is the arbitrage spread on trade $n$ at time $t$, $q_{n,t}$ is the quantity supplied, $w_{n,l}$ is the fraction of trade $n$ funded from source $l$ (with cost $f_{l,t}$ in excess of zero), $V_{k,t}$ is the aggregate scale of activities under balance-sheet constraint $k$, and $c_{k,t}$ is the marginal cost of meeting constraint $k$. Market clearing requires $q_{n,t} = a_{n,t}$ (inelastic outside demand).

Canonical benchmarks. Under balance-sheet and funding integration with a single balance-sheet constraint ($c_{k,t} = 0$ for $k > 1$, $f_{l,t} = 0$ for all $l$), the equilibrium spread is (eq. 2, p. 2550):

$$s_{n,t} = v_{n,1} c_{1,t} V_{1,t} = v_{n,1} c_{1,t} \left( \sum_n a_{n,t} v_{n,1} \right)$$

All spreads move with the single factor $c_{1,t} V_{1,t}$ and are perfectly correlated. This is the prediction the data contradict: the canonical single-constraint intermediary model of He and Krishnamurthy (2013) implies perfect spread correlation, whereas the mean pairwise correlation is 0.22. Under a single frictional funding factor with balance-sheet integration ($c_{k,t} = 0$, $v_{n,k} = 0$, $f_{n,1} > 0$, $f_{n,l} = 0$ for $l > 1$), spreads are $s_{n,t} = w_{n,1} f_{1,t}$ — again a one-factor structure. Under integration with many constraints ($L = 1$, $K > 0$) spreads have a K+1 factor structure (eq. 3, p. 2550). The margin-based asset pricing of Garleanu and Pedersen (2011), in which integrated funding implies a one- or two-factor spread structure, is contradicted by the high-dimensional factor structure documented in the data:

$$s_{n,t} = w_{n,1} f_{1,t} + \sum_{k=1}^{K} v_{n,k} c_{k,t} V_{k,t}$$

Funding segmentation. When trades $n = 1,\ldots,N_1$ can use only source $l = 1$ and trades $n = N_1+1,\ldots,N$ can use only source $l = 2$, the equilibrium is (eq. 4, p. 2551):

$$s_{n,t} = \begin{cases} w_{n,1} f_{1,t} & \text{if } n \leq N_1 \\ w_{n,2} f_{2,t} & \text{if } N_1 < n \end{cases}$$

Cross-group correlation equals only $\rho(f_{1,t}, f_{2,t})$ (eq. 5, p. 2551):

$$\rho(s_{n_1,t},\, s_{n_2,t}) = \begin{cases} 1 & \text{if } n_1, n_2 \leq N_1 \text{ or } N_1 < n_1, n_2 \\ \rho(f_{1,t}, f_{2,t}) & \text{if } n_1 \leq N_1,\; n_2 > N_1 \end{cases}$$

Balance-sheet segmentation. When arbitrageurs in group I specialize in trades $n = 1,\ldots,N_1$ and group $\tilde{I}$ in trades $n = N_1+1,\ldots,N$, with different marginal balance-sheet costs, equilibrium spreads are (eq. 6, p. 2552). The premise that intermediaries specialize draws on evidence of intermediary specialization in credit derivatives in Siriwardane (2019):

$$s_{n,t} = \begin{cases} \epsilon_{n,i} + v_{n,1} c_{1,t}^{I} V_{1,t}^{I} & \text{if } n \leq N_1 \\ \epsilon_{n,j} + v_{n,1} c_{1,t}^{\tilde{I}} V_{1,t}^{\tilde{I}} & \text{if } N_1 < n \end{cases}$$

The correlation between spreads of the two groups depends on (i) correlation of balance-sheet shocks across groups, (ii) correlation of demand shocks, and (iii) cross terms (eq. 7, p. 2552):

$$\rho(s_{1,t}, s_{2,t}) = \rho(c_{1,t}^{I},\, c_{1,t}^{\tilde{I}}) \times \rho(a_{1,t}, a_{2,t}) + \rho(c_{1,t}^{I},\, a_{2,t}) \times \rho(c_{1,t}^{\tilde{I}},\, a_{1,t})$$

The model is not estimated structurally; it organizes the empirical tests by providing testable signatures: funding segmentation implies covariance between certain spreads and certain funding rates; balance-sheet segmentation implies covariance between certain spreads and specific intermediary balance-sheet costs.

Method

The paper applies four methods, each addressing a different identification challenge, building on sign-restricted-svar, panel-regression, differences-in-differences, and instrumental-variables.

Sign-restricted SVAR (supply vs. demand decomposition, Section II.C). For each futures-based trade $i$, let $Y_t = [s_t \;\; q_t]'$ be the vector of the spread and quantity (gross open interest). The structural VAR is (eq. 8, p. 2565):

$$B Y_t = A_0 + A_1 Y_{t-1} + \epsilon_t, \qquad \epsilon_t = [\epsilon_{s,t} \;\; \epsilon_{d,t}]'$$

The reduced form is $Y_t = \Phi_0 + \Phi_1 Y_{t-1} + u_t$ where $\Phi_0 = B^{-1} A_0$, $\Phi_1 = B^{-1} A_1$, and the residual covariance $\Sigma_u$ depends on $B$. Sign restrictions on the impact matrix identify the structural shocks (eq. 9, p. 2565):

$$\begin{bmatrix} u_{s,t} \\ u_{q,t} \end{bmatrix} = \underbrace{\begin{bmatrix} - & + \\ + & + \end{bmatrix}}_{B^{-1}} \begin{bmatrix} \epsilon_{s,t} \\ \epsilon_{d,t} \end{bmatrix}$$

A supply shock ($\epsilon_s$) lowers spreads and raises quantities; a demand shock ($\epsilon_d$) raises both. The sign-restricted SVAR used to separate supply from demand shocks builds on Uhlig (2005). Estimation follows Arias, Rubio-Ramirez, and Waggoner (2018): Bayesian Normal-Wishart prior, 1,000 draws from the posterior using Cholesky decomposition of $\Sigma_u$. The model is estimated separately for each trade; the correlations of supply and demand shocks across trades are computed from the median-draw shock series.

Panel OLS: funding sensitivity (Section III.B, Table III). The baseline funding regression relates monthly changes in arbitrage-implied riskless rates to Treasury yield changes and TED spread changes (eq. 10, p. 2569):

$$\Delta r_{i,j,t} = \alpha_{i,j} + \beta_1 \Delta y_{j,t} + \beta_2 \Delta \text{TED}_t + \epsilon_{i,j,t}$$

• $r_{i,j,t}$ is the implied riskless rate for trade $i$ in strategy $j$
• $y_{j,t}$ is the maturity-matched Treasury yield
• $\text{TED}_t$ is the maturity-matched LIBOR minus Treasury spread (proxy for unsecured funding costs)
• Standard errors are clustered by strategy-month.

Empirical specifications

Spec 1: MMF reform DiD (R5, eq. 11, p. 2572). Baseline differences-in- differences estimating the 2016 MMF reform impact on unsecured vs. secured spreads, using daily data, trade and time fixed effects, clustered by trade and date. The 2016 MMF reform event study design builds on Anderson, Du, and Schlusche (2019):

$$s_{i,t} = \alpha_i + \alpha_t + \beta \cdot \mathbf{1}[i \in \text{Unsecured}] \cdot \mathbf{1}[t \geq \text{October2016}] + \epsilon_{i,t}$$

• $s_{i,t}$ is the absolute value of the arbitrage spread for trade $i$ on date $t$
• $\mathbf{1}[i \in \text{Unsecured}]$ equals 1 for CIP, box, and equity spot-futures trades
• $\mathbf{1}[t \geq \text{October2016}]$ equals 1 on or after the reform month
• Fixed effects: trade ($\alpha_i$) and time ($\alpha_t$); SE clustered by trade and date

Column (1) of Table IV (p. 2573) reports $\beta = 11.77^{**}$ (t = 2.47). A dynamic version replaces the single post-reform indicator with monthly leads and lags to trace the time profile of adjustment.

Spec 2: IV for equity repo funding (R6, Table V, p. 2575). The baseline augments eq. (10) with flows into Fidelity IPrime MMFs. The IV instrument is passive flows (eq. on p. 2575):

$$Z_t = F_t \times L_{t-3}^{I}$$

• $F_t$ is total flow into all Fidelity MMFs
• $L_{t-3}^{I}$ is the lagged share of Fidelity MMF assets that are IPrime
• Standard errors are clustered by strategy-month

The IV estimate (column 4, Table V) of the equity spot-futures spread on Fidelity flows is $\beta = -1.09^{**}$ (t = -2.25); the CIP/box and secured spread coefficients are indistinguishable from zero.

Spec 3: London Whale dynamic DiD (R7, eq. 12, p. 2583). In a weekly panel of unsecured arbitrage spreads, the event study estimates relative widening of equity spot-futures vs. other unsecured spreads around the March 1 and June 13, 2012 event dates:

$$s_{i,t} = \alpha_i + \alpha_t + \sum_{j=-4}^{24} \beta_j \cdot \mathbf{1}[i \in \text{Equity SF}] \cdot \mathbf{1}[t = j] + \epsilon_{i,t}$$

• $j$ indexes weeks since the first event date
• $\alpha_i$, $\alpha_t$ are trade and time fixed effects

Panel C of Figure 7 (p. 2582) shows equity spot-futures spreads were significantly elevated relative to other unsecured spreads for several months following each event date.

Spec 4: Hedge fund balance-sheet forecasting regression (R9, eq. 13, p. 2585). Monthly changes in spread levels on lagged hedge fund returns:

$$\Delta s_{i,t} = \alpha + \beta\, r_{t-1}^{H} + \epsilon_{i,t}$$

• $r_{t-1}^{H}$ is the lagged monthly return of Barclay’s fixed-income arbitrage hedge fund index (standardized to mean zero, unit variance)

Table VII (p. 2586) shows $\beta = -0.66^{**}$ (t = -3.04) for secured spreads and $\beta = 0.00$ (t = 0.01) for unsecured spreads; the effect is concentrated in Treasury-swap and CDS-bond sub-strategies.

When to read the full paper

Obtain the article via DOI 10.1111/jofi.13469 if you are: evaluating the cross-market structure of limits to arbitrage; testing intermediary asset pricing models across market segments; studying the 2016 MMF reform’s effects on derivatives markets; replicating the SVAR decomposition or the event studies; or checking specific coefficient estimates in the Internet Appendix. The locators above point to the exact tables and figures. For “what did this paper find,” the table above is the intended default.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(5), October 2025, pp. 2543–2590. DOI: 10.1111/jofi.13469. © 2025 the American Finance Association. Published by Wiley under the Wiley VOR terms; paywalled.

This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. Extraction is extract-only: core results and locators reproduced for research commentary; no verbatim reproduction of substantial portions. Contact the publisher for reuse rights.

Siriwardane, Emil N., Adi Sunderam, and Jonathan Wallen. “Segmented Arbitrage.” The Journal of Finance 80, no. 5 (October 2025): 2543–2590. DOI: 10.1111/jofi.13469.