Options Trading and Price Stability: Kim (2026)

What this is. A distilled summary of Da-Hea Kim, “Does options trading stabilize stock prices? Evidence from a natural experiment,” Journal of Banking and Finance 185 (2026) 107612. It covers the six headline results with exact source locators, the identification strategy (Penny Pilot Program as a quasi-natural experiment), and the regression specifications. Read the original at doi.org/10.1016/j.jbankfin.2025.107612 to replicate or extend.

TL;DR

Kim (2026) revisits whether options trading stabilizes or destabilizes underlying stock prices. Pooled OLS regressions find a positive association between options volume and stock price volatility, but this reflects reverse causality: high-volatility stocks attract more options activity. Using the SEC’s Penny Pilot Program (PPP), which reduced options tick sizes for roughly 500 underlying securities in a staggered fashion from 2007 to 2020, as an exogenous shock to options trading volume, instrumental variable regressions show that a one-standard-deviation increase in options volume reduces total volatility by 1.21 percentage points (46% of the mean). Difference-in-differences (DiD) regressions using a propensity-score-matched control sample confirm that pilot-stock total volatility falls by 0.21 percentage points (7.5% of pre-treatment average) relative to never-included controls. Two mechanisms drive the result: (1) the options market absorbs liquidity shocks to the underlying stock, reducing excessive trade concentration and extreme daily returns; and (2) options trading corrects mispricing by anchoring prices to intrinsic values through enhanced price discovery, as evidenced by declines in the Stambaugh, Yu, and Yuan (2015) mispricing score and in the Bogousslavsky and Muravyev (2024) informed trading intensity measures.

Core results

Significance stars: \* 10%, \*\* 5%, \*\*\* 1%. Locators cite the source PDF.

#	Result	Locator	Magnitude
R1	Pooled OLS finds a positive (endogenous) association between options volume and total stock volatility	Table 3, col. (1), p. 5	Ln(OPTVOLM) on TVOL = +0.223*** (t = 60.55); 1-SD increase associated with +34% SD change in TVOL
R2	IV (PPP as instrument) reverses the sign: increased options trading causally reduces total volatility	Table 4 Panel A, col. (2), p. 7	Instrumented Ln(OPTVOLM) on TVOL = -0.435*** (t = -4.15); 1-SD increase in options volume -> -1.21 pp TVOL = 46% of mean = 67% of SD
R3	DiD (matched sample): pilot firms’ total volatility declines relative to controls after program inclusion	Table 6, col. (3), p. 9	TREAT x POST on TVOL = -0.205*** (t = -2.73); 7.5% of pre-treatment mean; robust to additional controls and lagged TVOL
R4	DiD: idiosyncratic volatility and extreme return range also decline for pilot firms	Table 6, cols. (7) and (9), p. 9	TREAT x POST on IVOL = -0.134** (t = -2.28); TREAT x POST on MAXMIN = -0.871*** (t = -2.65)
R5	Liquidity buffer mechanism: options inclusion reduces excessive stock trade concentration and extreme daily price moves	Table 9, cols. (1) and (7), p. 14	TREAT x POST on Ln(MAXVOLM/MEDVOLM) = -0.038*** (t = -2.95); TREAT x POST on MAXRET = -0.427* (t = -2.26); 5-11% of pre-treatment mean
R6	Mispricing correction mechanism: options trading reduces stock mispricing and informed trading intensity	Tables 10-11, cols. (1), p. 15	TREAT x POST on MISP = -3.180*** (t = -3.87; 11-13% of pre-treatment); TREAT x POST on ITI_13D = -0.020*** (t = -4.71)

Overall (paper’s conclusion). The positive OLS correlation between options volume and stock price volatility reflects reverse causality. Once endogeneity is addressed via the Penny Pilot Program, the causal effect reverses: expanded options trading reduces stock price volatility. The stabilizing effect operates through both a liquidity buffer channel and a mispricing correction channel, and it holds across nine alternative matching methods, alternative sample periods (including the pre-2011 concentrated-addition period and the post-2009 cohort), and shorter event windows of plus or minus three or six months. The findings support the beneficial role of options markets in enhancing underlying equity price stability.

Theory / model

The paper proposes no formal structural model. It tests two competing hypotheses drawn from the prior theoretical literature and identifies two empirical channels for its main result.

Stabilization hypothesis. Options markets facilitate information transmission, provide hedging opportunities, and offer an alternative avenue for liquidity demand, all of which may reduce volatility in the underlying stock (Grossman (1988); Figlewski and Webb (1993); Cao (1999)). On this view, options absorb excess supply or demand that would otherwise move stock prices, and their informational role anchors prices to fundamental values.

Destabilization hypothesis. Options attract noise traders and speculators, undermine stock market liquidity by diverting it toward derivatives, and enable new sources of volatility through leveraged positions (Stein (1987); Gorton and Pennacchi (1993)).

Prior empirical work gives mixed results; the central identification challenge is endogeneity (higher-volatility stocks endogenously attract more options trading). Cao et al. (2024) use the same PPP to show that expanded options trading improves price informativeness, but leave open whether the net effect on stock return volatility is positive or negative. This paper addresses that question directly.

Two operative channels:

1. Liquidity buffer. By providing hedging alternatives and an additional venue for investor liquidity demand, the options market can absorb excess supply or demand in the underlying stock, mitigating extreme price movements. Empirically tested via concentrated-trading metrics: Ln(MAXVOLM/MEDVOLM) (max-to-median daily volume within a month), the maximum daily turnover deviation MAXTURN - MEDTURN, and extreme return measures MAXRET and |MINRET| (Table 9).

2. Mispricing correction. Options trading enhances price discovery by incorporating diverse information into asset prices and enabling arbitrage between options and the underlying. As prices adjust more quickly to fundamental values, the scope for profitable informed trading diminishes and mispricing is reduced. Empirically tested via the Stambaugh, Yu, and Yuan (2015) composite mispricing score SYY_SCORE (based on 11 anomaly variables) and the Bogousslavsky and Muravyev (2024) informed trading intensity measures (Table 10, Table 11).

Identification logic. From January 2007 to June 2020 the SEC’s Penny Pilot Program reduced minimum options tick sizes for selected securities (from nickel-and-dime to penny-and-nickel increments), lowering options trading costs and increasing volume. The selection of securities was designed to represent diverse trading characteristics rather than target volatility levels, supporting the exclusion restriction that inclusion affects stock volatility only through the channel of options trading volume. The program’s staggered, phased implementation (securities added at eight distinct dates over 13 years) helps disentangle the effect of options trading from contemporaneous trends. Hao and Li (2022) and Anagnostopoulou et al. (2023) use the same program in related settings.

Method

Two complementary strategies identify the causal effect of options trading on stock price volatility.

Strategy 1: Instrumental variable (IV). The PPP inclusion status (TREAT, time-invariant) interacted with post-inclusion months (POST) instruments for options trading volume. The first-stage regression (Table 4, Panel A, col. (1)) is:

$$\text{OptionsTrading}_{it} = \alpha + \beta \bigl(\text{TREAT}_i \times \text{POST}_{it}\bigr) + \gamma \, \text{Controls}_{i,t-1} + \varepsilon_{it} \tag{1}$$

where TREAT equals 1 if firm $i$‘s options are included in the PPP (0 otherwise) and POST equals 1 for months after inclusion. The first-stage coefficient $\hat{\beta}$ on TREAT $\times$ POST is 0.492 (t = 11.74), confirming a strong instrument. The second-stage regression (Table 4, Panel A, cols. (2)-(4)) is:

$$\text{VolatilityProxy}_{it} = \delta + \zeta \, \widehat{\text{OptionsTrading}}_{it} + \lambda \, \text{Controls}_{i,t-1} + \varepsilon_{it} \tag{2}$$

Two interchangeable options activity measures are used: the natural logarithm of total monthly options contracts traded, Ln(OPTVOLM), and the option-to-stock volume ratio O/S = OPTVOLM / (STKVOL x 100). Both panels of Table 4 yield consistently negative second-stage coefficients. All regressions include firm and month fixed effects; standard errors are clustered by firm.

Strategy 2: Difference-in-differences (DiD) with propensity-score-matched controls. Treated firms (pilot options included in the PPP) are matched 1:1 to never-treated controls using nearest-neighbor logit propensity-score matching without replacement (caliper 0.2), yielding 264 matched pairs. Matching covariates are TVOL, STKRET, SIZE, STKVOL, IOR, MOM, BM, and ROA measured in the month before inclusion. The baseline DiD regression (Table 6) uses a 25-month event window (12 months before and after inclusion):

$$\text{Volatility}_{it} = \alpha + \beta \bigl(\text{TREAT}_i \times \text{POST}_{it}\bigr) + \gamma \, \text{Controls}_{i,t-1} + \varepsilon_{it} \tag{3}$$

The coefficient $\beta$ on TREAT $\times$ POST identifies the within-matched-pair change in volatility attributable to program inclusion. Placebo tests (Table 7) assign pseudo-treatment dates six months before actual inclusion and confirm that TREAT $\times$ PseudoPOST coefficients are statistically indistinguishable from zero for all volatility outcomes, supporting the parallel-trends assumption. The dynamic DiD specification (Fig. 3) replaces POST with event-window dummies $D_{ik}$ ($k = -11, \ldots, 12$):

$$\text{TVOL}_{it} = \alpha + \sum_{k=-11}^{12} \beta_{1k} \,\text{TREAT}_i \times D_{ik} + \beta_2 \,\text{TREAT}_i + \sum_{k=-11}^{12} \beta_{3k} D_{ik} + \gamma \,\text{Controls}_{i,t-1} + \varepsilon_{it} \tag{4}$$

Pre-addition $\beta_{1k}$ estimates are flat and statistically insignificant; post-addition estimates turn significantly negative, tracing the onset of the stabilization effect.

Empirical specifications

Pooled OLS baseline (Table 3). Sample: 380,064 firm-month observations, 2006-2021. Dependent variables are TVOL (cols. 1-4), IVOL (cols. 5-8), MAXMIN (cols. 9-12). Options activity is Ln(OPTVOLM) or O/S. Controls (lagged one month): STKRET, Ln(SIZE), Ln(STKVOL), IOR, MOM, BM, ROA. Columns (2), (4), (6), (8), (10), (12) also include the lagged dependent variable. Firm and month fixed effects; standard errors clustered by firm. Result R1 comes from col. (1).

IV specifications (Table 4). Same sample and controls. Panel A uses Ln(OPTVOLM) as the endogenous variable, Panel B uses O/S. Second-stage dependent variables: TVOL (col. 2 or 6), IVOL (col. 3 or 7), MAXMIN (col. 4 or 8). Columns (5)-(8) in each panel add the lagged volatility as an additional control. Result R2 comes from Panel A, col. (2). The standard deviation of Ln(OPTVOLM) is 2.78 (footnote 12), so the 1-SD partial effect is $-0.435 \times 2.78 = -1.21$ pp TVOL.

DiD baseline (Table 6). Sample: up to 13,020 firm-month observations from 264 matched pairs; 25-month event window (12,667 where additional controls require options trading data). Cols. (1)-(2) use options trading as the dependent variable (confirming the first-stage effect of PPP inclusion). Cols. (3)-(5) use TVOL with progressively richer controls (col. 5 adds lagged TVOL). Cols. (6)-(8) use IVOL; cols. (9)-(11) use MAXMIN. Results R3 and R4 come from cols. (3) and (7)/(9), respectively.

Liquidity buffer channel (Table 9). Same matched sample and DiD specification. Dependent variables: Ln(MAXVOLM/MEDVOLM) (log max-to-median daily volume ratio, cols. 1-2), (MAXVOLM - MEDVOLM)/MEDVOLM (percentage deviation, cols. 3-4), MAXTURN - MEDTURN (daily turnover range, cols. 5-6), MAXRET (maximum daily return, cols. 7-8), |MINRET| (absolute minimum daily return, cols. 9-10). Result R5 comes from cols. (1) and (7).

Mispricing correction channel (Table 10). Sample of mispriced stocks (SYY_SCORE > 70 or < 30). Dependent variables: MISP = |SYY_SCORE - 50| (cols. 1-2), SYY_SCORE for underpriced stocks (cols. 3-4), SYY_SCORE for overpriced stocks (cols. 5-6). Additional robustness in Table 11 uses five Bogousslavsky and Muravyev (2024) ITI measures (ITI_13D, ITI_Impatient, ITI_Patient, ITI_Insider, ITI_Short) as dependent variables. Result R6 comes from Table 10, col. (1) and Table 11, col. (1).

Robustness (Tables 12-14). Nine alternative matching methods (Mahalanobis distance and eight covariate-cell-based procedures following Cao et al. (2024)), alternative sample periods (2006-2011 only and excluding the Global Financial Crisis through June 2009), and shorter event windows of plus or minus three and six months around the inclusion month. Results are consistent in sign and significance across all specifications.

Datasets used

Dataset	Role in paper	Wiki page
Ivy OptionMetrics	Options trading volume, open interest, implied volatility, bid-ask quotes for all exchange-listed options (2006-2021)	OptionMetrics
CRSP	Stock returns, share prices, trading volume, shares outstanding for underlying stocks	WRDS / CRSP (licensed)
Compustat	General accounting data: book-to-market ratio, return on assets	WRDS / Compustat (licensed)
Thomson Reuters 13F	Institutional ownership ratio (IOR) for matching and controls	WRDS / 13F (licensed)
Stambaugh, Yu, and Yuan (2015) SYY_SCORE	Composite mispricing score (percentile rank on 11 anomalies); downloaded from R. F. Stambaugh’s website	No page yet
Bogousslavsky, Fos, and Muravyev (2024) ITI	Informed Trading Intensity measures (ITI_13D, ITI_Impatient, ITI_Patient, ITI_Insider, ITI_Short); downloaded from D. Muravyev’s website	No page yet
CBOE SEC filings (hand-collected)	Penny Pilot Program inclusion schedule: 460 securities, 8 addition events, 2007-2020	No page yet

Sample: January 2006 to December 2021 (192 months). The main matched-sample analysis covers a 25-month window around each inclusion event; the IV analysis uses the full 380,219 firm-month panel. Options and stock data are merged at the underlying-security level; accounting data are lagged one year.

When to read the full paper

Read the source at doi.org/10.1016/j.jbankfin.2025.107612 if you are:

• assessing whether options markets stabilize or destabilize underlying equity prices (the causal IV and DiD evidence is in Tables 4 and 6);
• studying the liquidity buffer or mispricing correction channels of derivatives markets (Tables 9-11);
• using the Penny Pilot Program as a quasi-natural experiment for options trading (compare with Cao et al. (2024) for price informativeness; Hao and Li (2022) for earnings management implications);
• evaluating the robustness methodology for staggered DiD with many alternative matching procedures (Table 12) or shorter event windows (Table 14);
• benchmarking results against the recent retail and zero-day-to-expiration options literature.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 185 (2026) 107612. DOI: 10.1016/j.jbankfin.2025.107612. Published by Elsevier B.V. All rights reserved. This distillation was extracted by an LLM (claude-sonnet-4-6) on 2026-06-25 and is not human-verified or independently reproduced. The article is paywalled; only textual extracts are permitted here.

Kim, Da-Hea. “Does options trading stabilize stock prices? Evidence from a natural experiment.” Journal of Banking and Finance 185 (2026): 107612. DOI: 10.1016/j.jbankfin.2025.107612. Copyright 2025 Elsevier B.V. All rights reserved.