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 fifteen main findings 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, consistent with reverse causality because high-volatility stocks may 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 find 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 a decline in MISP and informed trading intensity. SYY_SCORE rises significantly for underpriced stocks, while its estimates for overpriced stocks are insignificant.

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)
R7	Penny Pilot inclusion increases options trading, supporting the instrument’s relevance	Table 4, Panel A col. (1) and Panel B col. (1), p. 7	TREAT x POST = +0.492*** (t = 11.74) for Ln(OPTVOLM); +0.042*** (t = 7.29) for O/S
R8	IV results hold when options trading is measured relative to stock volume	Table 4, Panel B cols. (2)-(4), p. 7	Instrumented O/S coefficients: TVOL -5.028*** (t = -3.48), IVOL -4.058*** (t = -3.32), MAXMIN -19.870*** (t = -3.26); 1-SD O/S increase reduces volatility 31-35% of outcome SD
R9	Placebo treatment dates before actual inclusion show no pre-treatment volatility effects	Table 7, cols. (3)-(11), p. 11	TREAT x PseudoPOST on TVOL = -0.011 (t = -0.12), +0.014 (t = 0.17), +0.014 (t = 0.19) across cols. (3)-(5); all volatility coefficients are insignificant
R10	Dynamic event-time estimates support parallel trends and show volatility falling after inclusion	Fig. 3, p. 10	Pre-addition TREAT x event-time coefficients are not statistically different from zero; post-addition coefficients become significantly negative
R11	The volatility decline remains after controlling for stock trading volume and investor attention	Table 8, Panels A-B, cols. (1)-(9), p. 12	TVOL TREAT x POST ranges from -0.172 to -0.132 in Panel A and -0.161 to -0.147 in Panel B; all estimates are significant at 5%
R12	Additional liquidity proxies confirm reduced concentrated trading and extreme daily losses	Table 9, cols. (5)-(6) and (9)-(10), p. 14	TREAT x POST = -16.040*** (t = -4.05) for MAXTURN - MEDTURN; -0.389** (t = -2.50) for
R13	Mispricing correction is significant for underpriced stocks but not overpriced stocks	Table 10, cols. (3)-(6), p. 15	Underpriced SYY_SCORE: +2.000** (t = 2.08), +1.496* (t = 1.99); overpriced SYY_SCORE: -1.834 (t = -1.64), -0.607 (t = -0.46)
R14	Options inclusion lowers all remaining informed-trading intensity measures	Table 11, cols. (3)-(10), p. 15	TREAT x POST: ITI_Impatient -0.021*** (t = -5.52), ITI_Patient -0.016*** (t = -4.49), ITI_Insider -0.006** (t = -2.20), ITI_Short -0.008*** (t = -4.32)
R15	The volatility reduction persists across alternative matching and sample-window checks	Tables 12-14, pp. 16-19	Nine alternative matching methods and sample periods restricted to 2006-2011 or excluding observations through June 2009 preserve the baseline pattern; baseline matched TVOL effect is -0.135** (t = -2.24) for +/-3 months and -0.181*** (t = -2.84) for +/-6 months

Overall (paper’s conclusion). The positive OLS correlation between options volume and stock price volatility is consistent with reverse causality, since volatile stocks may attract options trading. Once endogeneity is addressed via the Penny Pilot Program, the estimated 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, a 2006-2011 sample, a sample excluding observations through June 2009, 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

The paper has no formal structural model. The tested hypotheses and channels are described above. Its identification combines an instrumental-variable design using staggered Penny Pilot inclusion and a propensity-score-matched difference-in-differences design. The Penny Pilot Program lowered options tick sizes, increasing options trading costs less; treatment timing was staggered across securities.

Pooled OLS specification (Table 3, p. 5). The paper regresses the monthly volatility proxy on contemporaneous options activity and lagged firm controls:

$$\text{VolatilityProxy}_{i,t} = \alpha + \beta \text{OptionsTrading}_{i,t} + \gamma^{\prime} \text{Controls}_{i,t-1} + \varepsilon_{i,t} \tag{1}$$

VolatilityProxy is TVOL, IVOL, or MAXMIN. OptionsTrading is Ln(OPTVOLM) or O/S. The sample contains 380,064 firm-months from 2006-2021. Regressions include firm and month fixed effects, with heteroskedasticity-robust standard errors clustered by firm.

Instrumental variables (Table 4, p. 7). The first stage uses the interaction of program inclusion and post-inclusion months to instrument for options volume:

$$\text{OptionsTrading}_{i,t} = \alpha + \beta \left(\text{TREAT}_{i} \times \text{POST}_{i,t}\right) + \gamma^{\prime} \text{Controls}_{i,t-1} + \varepsilon_{i,t} \tag{2}$$

The second stage is:

$$\text{VolatilityProxy}_{i,t} = \delta + \zeta \widehat{\text{OptionsTrading}}_{i,t} + \lambda^{\prime} \text{Controls}_{i,t-1} + \nu_{i,t} \tag{3}$$

Both stages use the 380,064 firm-month sample from 2006-2021, firm and month fixed effects, and robust standard errors clustered by firm. Table 4 reports Ln(OPTVOLM) and O/S as alternative endogenous activity measures, three volatility proxies, and variants controlling for lagged volatility.

Matched difference-in-differences (Table 6, p. 9). A logit propensity score matches 264 pilot firms one-to-one to never-treated controls without replacement, using a 0.2 caliper and pre-inclusion TVOL, STKRET, SIZE, STKVOL, IOR, MOM, BM, and ROA. The baseline estimating specification is:

$$\text{Outcome}_{i,t} = \alpha + \beta \left(\text{TREAT}_{i} \times \text{POST}_{i,t}\right) + \gamma^{\prime} \text{Controls}_{i,t-1} + \mu_i + \tau_t + \varepsilon_{i,t} \tag{4}$$

Outcome is options activity or a volatility proxy. The sample is monthly observations in a 25-month event window, up to 13,020 firm-months from 264 matched pairs. All regressions include firm and month fixed effects and firm-clustered robust standard errors.

Placebo, attention controls, and event time. Table 7 replaces POST with a pseudo-post indicator dated six months before inclusion in the pre-treatment sample. Table 8 adds contemporaneous or one-month-lagged stock-volume changes, Google Search Volume Index, or Bloomberg News Heat Score. Both tables use firm and month fixed effects and firm-clustered robust standard errors; the Table 8 matched samples range from 8,333 to 12,667 observations (p. 12). Figure 3 estimates event-time interactions over k = -11,…,12 months:

$$\text{TVOL}_{i,t} = \alpha + \sum_{k=-11}^{12} \beta_{1k} \text{TREAT}_{i,t} \times D_{i,t,k} + \beta_2 \text{TREAT}_{i,t} + \sum_{k=-11}^{12} \beta_{3k}D_{i,t,k} + \gamma^{\prime} \text{Controls}_{i,t-1} + \varepsilon_{i,t} \tag{5}$$

The pre-addition coefficients are statistically indistinguishable from zero, while post-addition coefficients are significantly negative (Fig. 3, p. 10).

Mechanism specifications (Tables 9-11, pp. 14-15). The liquidity and mispricing tests use the same matched DiD design in equation (4), changing the dependent variable to the Table 9 liquidity-shock measures, Table 10 MISP or SYY_SCORE in underpriced and overpriced samples, or Table 11 informed-trading intensity measures. These regressions include firm and month fixed effects and robust standard errors clustered by firm. Table 9 uses up to 13,020 matched firm-month observations; Table 10 and Table 11 use their respective outcome-specific matched samples. The Table 10 MISP sample consists of stocks with previous-month SYY_SCORE below 30 or above 70.

Empirical specifications

All specifications below use the paper’s reported regressors and fixed effects; firm-clustered robust standard errors are used unless stated otherwise.

Placebo DiD (Table 7, p. 11). The authors restrict to the pre-treatment window and move the treatment date six months earlier:

$$\text{Outcome}_{i,t} = \alpha + \beta \left(\text{TREAT}_{i} \times \text{PseudoPOST}_{i,t}\right) + \gamma^{\prime} \text{Controls}_{i,t-1} + \mu_i + \tau_t + \varepsilon_{i,t} \tag{6}$$

Outcomes are options volume or TVOL, IVOL, and MAXMIN. The sample is the matched firms’ 12-month pre-treatment window, with 6,161 or 6,319 firm-months depending on outcome availability. Firm and month fixed effects are included; standard errors are robust and clustered by firm. The reported treatment coefficients for the volatility outcomes are statistically insignificant.

Attention and liquidity controls (Table 8, p. 12). The baseline matched DiD specification adds contemporaneous or lagged changes in stock volume and attention measures:

$$\text{Volatility}_{i,t} = \alpha + \beta \left(\text{TREAT}_{i} \times \text{POST}_{i,t}\right) + \delta \text{Proxy}_{i,t-k} + \gamma^{\prime} \text{Controls}_{i,t-1} + \mu_i + \tau_t + \varepsilon_{i,t} \tag{7}$$

Proxy is monthly change in stock volume, Google Search Volume Index, or Bloomberg News Heat Score; k is zero in Panel A and one in Panel B. TVOL, IVOL, and MAXMIN are the outcomes. Matched monthly samples range from 8,333 to 12,667 observations because Bloomberg data begin in 2010. Firm and month fixed effects and firm-clustered robust standard errors are used.

Liquidity and mispricing tests (Tables 9-11, pp. 14-15). These use equation (4) with the same matched DiD controls, fixed effects, and firm-clustered robust standard errors. Table 9 outcomes are log maximum-to-median daily volume, the maximum-volume deviation, maximum-to-median turnover difference, MAXRET, and |MINRET|; sample sizes are 12,667 or 13,020. Table 10 outcomes are MISP in the pooled mispriced sample and SYY_SCORE separately among stocks with prior-month scores below 30 or above 70; sample sizes range from 1,006 to 2,175. Table 11 outcomes are the five ITI measures for the matched firms over the 12 months before and after inclusion. Robustness Tables 12-14 repeat these matched specifications across nine matching methods, different sample periods, and +/-3- and +/-6-month event windows (pp. 16-19).

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 updated by gpt-6-luna on 2026-10-04; it has been machine-verified against the PDF but 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.