Discount Factors and Monetary Policy: Vandeweyer, Yang & Yannelis (2026)

What this is. The paper’s core results, the conceptual model that motivates the A/H ratio design, and the estimating equations: enough to understand what the paper found and how the discount factor channel is identified, without reading all 18 pages. To replicate or extend, read the original at https://doi.org/10.1016/j.jfineco.2025.104190.

TL;DR

The paper uses dual-listed stocks (A-shares in Mainland China, H-shares in Hong Kong) as a laboratory to isolate the discount factor channel of monetary policy transmission to stock prices. Since both share types represent claims on the same firm cash flows, the ratio of A-share to H-share prices (the A/H ratio) cancels out cash flow news and reflects only differences in investors’ discount rates across the two segmented markets. US Federal Open Market Committee (FOMC) monetary policy surprises, as measured by Kuttner (2001) fed-funds futures, significantly shift the A/H ratio, with a 100 basis-point surprise causing roughly a 30 basis-point change within five trading days. The effect is concentrated in cycle-amplifying surprises (surprise rate cuts during easing cycles and surprise rate hikes during tightening cycles), while contradictory surprises are insignificant. Cross- sectionally, value stocks, small stocks, and high-beta stocks respond more strongly, consistent with standard asset pricing theory in which discount rate revisions have disproportionately large effects on risky, short-duration cash flows.

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators refer to the source PDF.

#	Result	Locator	Magnitude
R1	A 100 bp Fed surprise shifts the A/H ratio by ~30 bp within five trading days, isolating the discount factor channel	Table 3, cols 1-5, p. 8	Surprise x Post = 0.293*** (SE 0.0297) with company FE and post-announcement indicator (col 3); stable across specifications: range 0.276 to 0.306, all significant at 1%
R2	The effect is asymmetric: only cycle-amplifying surprises matter. Surprise rate cuts during easing cycles and surprise rate hikes during tightening cycles move the A/H ratio significantly; contradictory surprises do not	Table 4, Panels A-B, p. 8	Amplifying-hike: 0.390*** (0.112); amplifying-cut: 0.355*** (0.0400); contradictory-hike: -0.0964 (0.107); contradictory-cut: 0.0467 (0.179)
R3	US monetary policy passes through ~1-for-1 to Hong Kong interbank rates (HIBOR) but leaves Mainland China interbank rates (SHIBOR) unaffected, validating the market segmentation assumption	Figure 3, p. 6	1 pp Fed funds surprise generates ~1 pp increase in 3-month HIBOR by day 1; LIBOR (USD) also rises; SHIBOR coefficient near zero and statistically insignificant
R4	Value firms (low PE ratio) show roughly twice the discount-factor sensitivity of growth firms	Table 6, Panel A, p. 11	Below-median PE: Surprise x Post = 0.346*** (0.0651); above-median PE: 0.162*** (0.0485); triple interaction Surprise x Post x Charact = -0.161* (0.0908)
R5	Small firms (low market capitalization) are more sensitive to discount-factor revisions than large firms	Table 6, Panel B, p. 11	Below-median MC: Surprise x Post = 0.367*** (0.0575); above-median MC: 0.237*** (0.0346); triple interaction = -0.0889*** (0.0210)
R6	High-CAPM-beta stocks react more strongly than low-beta stocks, consistent with discount rate revisions hitting riskier cash flows disproportionately	Table 6, Panel C, p. 11	Below-median beta: Surprise x Post = 0.220*** (0.0638); above-median beta: 0.381*** (0.0730); triple interaction = 0.362** (0.146)

Overall (paper’s conclusion). Monetary policy announcements cause investors to revise their discount factors and impact stock prices. The discount factor channel is substantial and survives controls for cash-flow news: professional analysts’ EPS forecasts for the same firms do not diverge across the two regions following FOMC announcements (ruling out the information channel of Nakamura and Steinsson (2018)), and non-exporting firms (with no US revenue channel) react identically to exporters. The asymmetry toward cycle-amplifying surprises suggests that higher-frequency event-study strategies that do not control for business-cycle context may understate the effects of monetary policy on asset prices.

Theory / model

The conceptual framework follows Section 2.2 (p. 4). Consider an economy with two segmented regions, A (Mainland China) and H (Hong Kong), populated by different investors but trading integrated firms whose shares represent claims on the same future cash flows. The monetary policy stance in each region is captured by $m_A$ and $m_H$, interpreted as the central bank’s reaction function across all policy instruments.

Following Cochrane (2005), the price of stock $i$ in region $J \in \{A, H\}$ equals the expected discounted cash flow under the region’s risk-adjusted discount factor, and following Cieslak and Pang (2021) the discount factor decomposes into a risk-free rate and a risk premium component (Eq. 1, p. 4):

$$P_J^i = \frac{1}{R_J^i(m_J)} E\!\left[x^i(m_A, m_H)\right], \tag{1}$$

where $R_J^i(m_J)$ is the risk-adjusted discount factor applied by region-$J$ investors to stock $i$, and $E[x^i(m_A, m_H)]$ is the common cash-flow expectation (identical across regions under integrated trade). The discount factor decomposes into a risk-free component and a firm-specific risk premium:

$$R_J^i = r^f(m_J) + r^{p,i}(m_J), \qquad J \in \{A, H\}.$$

Taking the ratio of A-share price to H-share price (Eq. 2, p. 4):

$$\frac{P_A^i}{P_H^i} = \frac{R_H^i(m_H)}{R_A^i(m_A)}. \tag{2}$$

The common cash-flow term $E[x^i]$ cancels exactly. The A/H ratio therefore depends only on the ratio of discount factors across the two regions, not on firms’ cash flows. A change in the A/H ratio around an FOMC announcement reflects changes in relative discount factors attributable to monetary policy, not information about earnings or dividends.

The identification logic rests on two institutional facts. First, the Hong Kong dollar is pegged to the USD through the Linked Exchange Rate System (LERS), so the Hong Kong overnight interbank rate (HIBOR) closely tracks the US federal funds rate (Section 2.1, p. 3). Second, Mainland China maintains strict capital controls, so investors there are largely insulated from US monetary policy and have an independent monetary policy stance via the People’s Bank of China. A US monetary policy surprise therefore affects $m_H$ but not $m_A$, generating an exogenous shift in $R_H^i$ that moves the A/H ratio without contaminating the Mainland discount factor $R_A^i$. Figure 3 (p. 6) confirms this empirically: a 1 pp Fed funds surprise shifts 3-month HIBOR by approximately 1 pp but leaves SHIBOR (the Mainland equivalent) near zero.

Method

The paper applies two estimating frameworks from the macro-finance event-study tradition, building on difference-in-differences and event-study designs, with panel-regression for inference.

Main DiD specification (Eq. 3, p. 4). The primary specification regresses the A/H ratio on the interaction of the Kuttner (2001) monetary policy surprise with a post-announcement indicator. The approach extends Bernanke and Kuttner (2005), who found a roughly 1% stock-index response to a 100 bp surprise, by isolating the discount factor channel via the A/H design:

$$(P_A/P_H)_{ist} = \alpha_i + \eta_s + \lambda_t + \beta\,\text{Surprise}_s \times \text{Post}_t + \epsilon_{ist}, \tag{3}$$

where $(P_A/P_H)_{ist}$ is the A/H share-price ratio for stock $i$ around announcement $s$ at event time $t$; $\alpha_i$ are company fixed effects absorbing time-invariant characteristics (size, location, sector); $\eta_s$ are announcement fixed effects absorbing macroeconomic conditions on each FOMC date; $\lambda_t$ are event-time fixed effects; $\text{Surprise}_s$ is the surprise component of the change in the target rate computed from federal-funds futures following Kuttner (2001); and $\text{Post}_t$ equals one for event times $t \geq 0$. The coefficient $\beta$ measures the change in the A/H ratio per percentage-point surprise. Standard errors are clustered at the company level (Section 2.3, p. 4). The sample covers FOMC announcements from June 2000 to September 2024, excluding ZLB periods (December 2008 to December 2015) and COVID (March 2020 to March 2022).

HIBOR passthrough specification (Eq. 4, p. 5). To validate the transmission assumption, the paper estimates event-by-event passthrough of the Fed surprise to Hong Kong interbank rates:

$$\text{HIBOR}_{st} = \eta_s + \lambda_t + \sum_{\tau=-4}^{5} \beta_\tau^{HB}\,\text{Surprise}_s \times \mathbf{1}(\text{Time Since Announcement}_{st} = \tau) + \epsilon_{st}. \tag{4}$$

Dynamic A/H event study (Eq. 5, p. 5). For graphical assessment of pre-trends and post-announcement dynamics, the main specification is extended to a full coefficient path:

$$(P_A/P_H)_{ist} = \alpha_i + \eta_s + \sum_{\tau=-4}^{5} \beta_\tau\,\text{Surprise}_s \times \mathbf{1}(\text{Time Since Announcement}_{st} = \tau) + \epsilon_{ist}. \tag{5}$$

Figure 4 (p. 8) shows no pre-trend ($\beta_\tau \approx 0$ for $\tau < 0$), with the effect peaking around day 3 and showing some mean-reversion by day 5.

Empirical specifications

Baseline results (R1, Table 3, p. 8). Equation (3) is estimated on 93,414 company-announcement observations (143 stocks, 129 announcements). Column (3) adds company FE and a post-announcement indicator for event-time trend; Column (4) adds company FE and full event-time FE; Column (5) adds separately trending company-specific time controls. Across all five specifications the Surprise x Post coefficient ranges from 0.276 to 0.306, all significant at the 1% level.

Asymmetry test (R2, Table 4, p. 8). Announcements are split by interest rate cycle phase (hiking vs. cutting vs. flat, defined by Figure 2) and by surprise direction (increase vs. decrease vs. near-zero). This yields a 3 x 3 grid. The key finding is that Panels A and B show significant coefficients only for amplifying configurations: surprise increases during hike periods (0.390***) and surprise decreases during cut periods (0.355***). All other cells are statistically insignificant, including surprise hikes during cutting cycles and surprise cuts during hiking cycles (contradictory surprises). Column (3) in each panel (near-zero surprises) is also insignificant.

Cross-sectional triple-difference (R4-R6, Eq. 6, p. 10). To examine heterogeneity in discount-factor sensitivity, the paper estimates a triple-difference specification adding a stock characteristic $\text{Charact}_{ist}$ (lagged one year) to equation (3). Following Fama and French (1992), the characteristics tested include price-to-earnings ratio and market capitalization, which predict cross-sectional stock returns and are interpreted as proxies for risk-factor exposure:

$$\begin{aligned} (P_A/P_H)_{ist} &= \alpha_i + \eta_s + \lambda_t + \beta_1 \text{Surprise}_s \times \text{Post}_t + \beta_2 \text{Charact}_{ist} \\ &\quad + \beta_3 \text{Surprise}_s \times \text{Charact}_{ist} + \beta_4 \text{Post}_t \times \text{Charact}_{ist} \\ &\quad + \beta_5 \text{Surprise}_s \times \text{Post}_t \times \text{Charact}_{ist} + \epsilon_{ist}. \end{aligned} \tag{6}$$

The coefficient $\beta_5$ captures differential discount-factor sensitivity by firm characteristic. Characteristics tested: price-to-earnings ratio (PE, lagged 1 year); log market capitalization (MC); CAPM beta from 5-year rolling monthly regressions on the Shanghai Composite Index; and the lagged A/H ratio. Table 6 (p. 11) reports results for splits below and above the cross-sectional median of each characteristic.

Robustness (Table 7, p. 11). Results are stable when including ZLB announcements (Column 1, coefficient 0.130***), excluding near-zero surprises (Column 2, 0.291***), excluding holiday event times (Column 3, 0.286***), and combining both holiday and near-zero exclusions (Column 4, 0.295***). Appendix D applies the stacked DiD approach of Baker et al. (2022), finding larger point estimates (Table D.1, all specifications approximately 0.445-0.447***) consistent with the main results.

Datasets used

Dataset	Role in paper	Wiki page
Wind Information (China)	Daily closing prices for A-shares and H-shares of 143 dual-listed firms; primary source for the A/H ratio	no page yet
HKMA (Hong Kong Monetary Authority)	Historical 3-month HIBOR rates on each trading day; core channel-validation data	no page yet
FRED (Federal Reserve Economic Data)	Fed target rate before December 2008; target-rate series for computing Kuttner surprises	FRED
Datastream (Refinitiv)	1-month fed-funds futures; 3-month HIBOR futures; LIBOR historical series (USD)	no page yet
Bloomberg	Historical USD-denominated LIBOR series used for placebo test (Figure 3, bottom-left panel)	no page yet
SHIBOR (Shanghai Interbank Offered Rate)	Mainland China interbank rate; placebo test confirming capital controls prevent Fed passthrough	no page yet
I/B/E/S via WRDS	EPS forecasts for dual-listed firms from Hong Kong and Mainland China brokers; used in robustness (Appendix B)	WRDS
FactSet GeoRev	Firm-level US export revenue share; used to rule out the cash-flow channel in robustness (Appendix A)	no page yet
China Foreign Exchange Trade System (CFETS)	HKD-CNY and USD-CNY 6-month forward exchange rates; used to rule out exchange-rate channel (Appendix C)	no page yet

Sample: June 2000 to September 2024 (daily), 143 dual-listed firms, 129 FOMC announcements (excluding ZLB and COVID periods), window of 5 trading days before and after each announcement. Stock prices converted to CNY using daily HKD-CNY exchange rates from Wind.

When to read the full paper

Use the original if you are: studying the transmission mechanism of monetary policy to stock prices; working on macro-finance models that need to distinguish the cash-flow and discount-rate channels; examining the role of the “Fed Put” and belief updating about the Fed’s reaction function; or designing event studies around FOMC announcements. The appendices (pp. 13-18) contain robustness tests on US export share (Appendix A), EPS forecast divergence (Appendix B), exchange rates (Appendix C), stacked DiD (Appendix D), and alternative clustering (Appendix E). The replication package (pseudo-data) is available via the Mendeley Data link on the article page.

Attribution and rights

Source: peer-reviewed, Journal of Financial Economics 175 (2026) 104190. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. The article is paywalled (copyright 2025 Elsevier B.V.); no CC licence was found in the Crossref metadata. Reproduction is extract-only.

Vandeweyer, Quentin, Minghao Yang, and Constantine Yannelis. “Discount factors and monetary policy: Evidence from dual-listed stocks.” Journal of Financial Economics 175 (2026): 104190. DOI: 10.1016/j.jfineco.2025.104190. Copyright 2025 Elsevier B.V.