Implied Volatility vs Beta: Wetin Each One Tell You

Implied volatility vs beta na comparison between two numbers wey dey answer different questions about the same stock. Implied volatility na forward-looking estimate from option market of how far stock fit move altogether within a defined horizon, for either direction. Beta na backward-looking regression slope wey capture only the part of stock’s past movement wey index account for. Stock fit print high for one and low for the other, and both readings fit correct at the same time.

Wet beta dey actually measure

Beta na the slope of line wey dem fit through two return series: one for stock and one for index. If dem write am out, na beta = covariance(stock returns, index returns) / variance(index returns). Beta of 1.30 mean say within the period wey dem measure, stock move about 1.3 percent for every 1 percent move for the index, on average. Beta of 0.40 mean say e move roughly four-tenths as much.

Four choices dey inside every beta wey dem publish before anybody quote am: which index dem use compare the stock, how long the period last, how often dem sample returns, and whether dividends dey count inside the returns. Change any one of them and the number go change. Beta wey dem quote without period no get label. Two data providers fit disagree about the same stock and both still dey correct.

The statistic wey dey usually follow beta na R-squared: the share of the stock’s return variance wey the index explain, for scale from 0 to 1. Beta show the slope. R-squared show whether the line make sense to draw. Beta of 1.40 with R-squared of 0.65 describe stock wey mostly dey follow the index, but with stronger movement. The same 1.40 with R-squared of 0.10 describe stock wey dey move on its own, with small index influence on top. Na only the first one describe stock wey index hedge fit track closely.

Wetin implied volatility dey measure

Implied volatility na the volatility number wey go make option pricing model return the price wey option dey trade for now. Dem dey quote am as annualized standard deviation. IV of 30 percent mean say market dey price roughly 30 percent one standard deviation move over one year, and about 8.6 percent for the next 30 days, because volatility dey scale with the square root of time. Our guide to wetin implied volatility be explain the mechanics step by step, while implied volatility versus historical volatility cover how the forward number compare with wetin stock actually do before.

The main difference na wetin each number dey count. IV dey price every source of movement inside one figure: selloff wey affect the whole index, trial readout, guidance change, or takeover bid. Beta dey count only the part of movement wey move together with the index. Everything wey R-squared leave out no dey show for beta, but IV price am fully.

Implied volatility vs beta for ten household names

The panel below dey run regression for ten liquid names against SPY, the S&P 500 ETF, over the twelve months wey end August 21, 2026. E run the same regression two times: once with daily closing returns, and once with weekly returns from the same period.

WITH
    px AS
    (
        SELECT
            ticker,
            date,
            toFloat64(close) AS close_px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker IN ('SPY','AAPL','MSFT','NVDA','TSLA','COIN','KO','JNJ','XOM','GLD','TLT')
          AND date >= '2025-08-22'
          AND date <= '2026-08-21'
    ),
    daily_ret AS
    (
        SELECT
            ticker,
            date,
            close_px / lagInFrame(close_px) OVER (PARTITION BY ticker ORDER BY date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) - 1 AS ret
        FROM px
    ),
    weekly_px AS
    (
        SELECT
            ticker,
            toMonday(date)         AS week_start,
            argMax(close_px, date) AS week_close
        FROM px
        GROUP BY ticker, week_start
    ),
    weekly_ret AS
    (
        SELECT
            ticker,
            week_start,
            week_close / lagInFrame(week_close) OVER (PARTITION BY ticker ORDER BY week_start ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) - 1 AS ret
        FROM weekly_px
    ),
    daily_beta AS
    (
        SELECT
            s.ticker                                           AS symbol,
            round(covarSamp(s.ret, i.ret) / varSamp(i.ret), 2) AS beta_daily,
            round(pow(corr(s.ret, i.ret), 2), 2)               AS r_squared,
            count()                                            AS return_count
        FROM daily_ret AS s
        INNER JOIN
        (
            SELECT date, ret FROM daily_ret WHERE ticker = 'SPY' AND isFinite(ret)
        ) AS i ON i.date = s.date
        WHERE s.ticker != 'SPY' AND isFinite(s.ret)
        GROUP BY symbol
    ),
    weekly_beta AS
    (
        SELECT
            s.ticker                                           AS symbol,
            round(covarSamp(s.ret, i.ret) / varSamp(i.ret), 2) AS beta_weekly
        FROM weekly_ret AS s
        INNER JOIN
        (
            SELECT week_start, ret FROM weekly_ret WHERE ticker = 'SPY' AND isFinite(ret)
        ) AS i ON i.week_start = s.week_start
        WHERE s.ticker != 'SPY' AND isFinite(s.ret)
        GROUP BY symbol
    )
SELECT
    d.symbol       AS symbol,
    d.beta_daily   AS beta_daily,
    w.beta_weekly  AS beta_weekly,
    d.r_squared    AS r_squared,
    d.return_count AS return_count
FROM daily_beta AS d
INNER JOIN weekly_beta AS w ON w.symbol = d.symbol
ORDER BY beta_daily DESC

Each daily slope dey use 250 paired sessions. The highest daily beta for the group na COIN at 2.83, together with R-squared of 0.28. The lowest one na XOM at -0.51, and na the R-squared of 0.07 be the important figure there: for this period, the index explain only small part of the name’s movement, so the slope explain very little of wetin the position do.

Now compare the two beta columns. When you sample weekly instead of daily, for the same twelve months and against the same index, the slope fit change. COIN prints 2.5 for weekly returns, compared with 2.83 for daily returns. None of them wrong. Each one dey answer slightly different question.

Wet option market dey charge for those same names

Beta dey come from past prices. This panel dey come from wetin traders dey pay now: near-the-money contracts, wey get 20 to 45 days before expiry, averaged across the three weeks wey end on Aug 21, 2026. SPY join the list here as the index reference. E no dey beta panel for mechanical reason: when you regress am against itself, e beta na 1.00 and e R-squared na 1.00.

SELECT
    underlying_symbol                                                       AS symbol,
    round(avg(toFloat64(implied_volatility)) * 100, 1)                      AS iv_pct,
    round(avg(toFloat64(implied_volatility)) * 100 * sqrt(30.0 / 365.0), 1) AS expected_move_30d_pct,
    count()                                                                 AS contract_count,
    formatDateTime(max(date), '%b %e')                                      AS iv_through
FROM global_markets.options_greeks
WHERE underlying_symbol IN ('SPY','AAPL','MSFT','NVDA','TSLA','COIN','KO','JNJ','XOM','GLD','TLT')
  AND date >= '2026-08-03'
  AND date <= '2026-08-21'
  AND iv_converged = 1
  AND volume > 0
  AND days_to_expiry BETWEEN 20 AND 45
  AND underlying_close > 0
  AND abs(toFloat64(strike_price) / toFloat64(underlying_close) - 1) < 0.05
GROUP BY symbol
HAVING contract_count >= 25
ORDER BY iv_pct DESC

The group wey get the widest implied volatility na COIN at 65.9 percent. That price imply one standard deviation move of about 18.9 percent over 30 days. The narrowest na TLT at 11.7 percent. This panel rank the names by total expected movement. The beta panel, wey COIN dey lead, rank dem by the movement wey index fit explain. Two out of the eleven na ETFs. One hold gold and the other hold long-dated Treasuries. Neither one na equity index bet, so beta against equity index no be the correct instrument for either of dem.

The movement beta no fit see

Make we put the gap for clear view. Take the sessions wey SPY barely move, with absolute close-to-close return below 0.25 percent. Then measure wetin each of the ten names do on those exact days. If beta na the whole story, flat index for mean flat stock.

WITH
    px AS
    (
        SELECT
            ticker,
            date,
            toFloat64(close) AS close_px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker IN ('SPY','AAPL','MSFT','NVDA','TSLA','COIN','KO','JNJ','XOM','GLD','TLT')
          AND date >= '2025-08-22'
          AND date <= '2026-08-21'
    ),
    daily_ret AS
    (
        SELECT
            ticker,
            date,
            close_px / lagInFrame(close_px) OVER (PARTITION BY ticker ORDER BY date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) - 1 AS ret
        FROM px
    ),
    idx AS
    (
        SELECT date, ret AS index_ret
        FROM daily_ret
        WHERE ticker = 'SPY' AND isFinite(ret)
    )
SELECT
    s.ticker                                                     AS symbol,
    round(avgIf(abs(s.ret) * 100, abs(i.index_ret) < 0.0025), 2) AS move_on_flat_index_pct,
    round(avg(abs(s.ret) * 100), 2)                              AS move_full_sample_pct,
    countIf(abs(i.index_ret) < 0.0025)                           AS flat_index_count
FROM daily_ret AS s
INNER JOIN idx AS i ON i.date = s.date
WHERE s.ticker != 'SPY' AND isFinite(s.ret)
GROUP BY symbol
HAVING countIf(abs(i.index_ret) < 0.0025) > 0
ORDER BY move_on_flat_index_pct DESC

For the same twelve months, there be 69 of these near-flat index sessions. For those sessions, COIN move average of 3.09 percent for absolute terms, compared with 3.3 percent across the full sample. The quietest of the ten for those days, TLT, average 0.45 percent. Everything for that column na movement wey beta no assign to anything, and na the kind movement wey option seller dey quote price for.

Beta na estimate, and estimates dey move

Beta no be stock property like share count. Na statistic wey dem calculate over one window, and e dey change as the window dey move forward. The panel below dey recalculate NVDA beta against SPY for the beginning of every month. Each point dey use the twelve months of daily returns before am.

WITH
    px AS
    (
        SELECT
            ticker,
            date,
            toFloat64(close) AS close_px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker IN ('SPY','NVDA')
          AND date >= '2021-08-01'
          AND date <= '2026-08-21'
    ),
    daily_ret AS
    (
        SELECT
            ticker,
            date,
            close_px / lagInFrame(close_px) OVER (PARTITION BY ticker ORDER BY date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) - 1 AS ret
        FROM px
    ),
    paired AS
    (
        SELECT
            s.date AS d,
            s.ret  AS stock_ret,
            i.ret  AS index_ret
        FROM daily_ret AS s
        INNER JOIN
        (
            SELECT date, ret FROM daily_ret WHERE ticker = 'SPY' AND isFinite(ret)
        ) AS i ON i.date = s.date
        WHERE s.ticker = 'NVDA' AND isFinite(s.ret)
    ),
    anchors AS
    (
        SELECT DISTINCT toStartOfMonth(d) AS anchor
        FROM paired
        WHERE d >= '2022-09-01'
    )
SELECT
    formatDateTime(a.anchor, '%Y-%m')                                    AS month,
    formatDateTime(a.anchor, '%b %Y')                                    AS month_label,
    round(covarSamp(p.stock_ret, p.index_ret) / varSamp(p.index_ret), 2) AS rolling_beta_1y,
    round(pow(corr(p.stock_ret, p.index_ret), 2), 2)                     AS r_squared
FROM anchors AS a, paired AS p
WHERE p.d < a.anchor
  AND p.d >= subtractYears(a.anchor, 1)
GROUP BY a.anchor
ORDER BY a.anchor

The line get 48 monthly estimates. The first one, for the year wey end Sep 2022, show 2.32. The latest one, wey end Aug 2026, show 1.87 with R-squared of 0.44. Na the same stock, the same index and the same method, but different window. If hedge sizing dey rely on beta from old window, the sizing dey rely on number wey the data don already pass.

Implied volatility carry the same warning for another form. Na live price wey dey change every trading session. Normally, people dey read IV against its own history, no be alone. IV of 30 percent high? dey explain that comparison.

Which number fit which work

Beta dey answer index questions. How much S&P 500 exposure a book carry, and wetin be the size of index position wey fit offset am? Na beta weighting be that, and beta weighting a portfolio explain the arithmetic. Na also the number wey dey show when portfolio get different names wey all dey load on one market factor. concentration risk dey measure that situation from the other side.

Implied volatility dey answer single-name questions for one fixed period. Wetin market dey charge for movement in this stock over the next 30 days, and that price high or low when you compare am with the same stock history? Index no enter either question.

Neither number dey forecast direction. Beta dey describe co-movement wey don already happen. IV na price for movement size, and dem quote am without direction sign. Stock wey get beta of 1.8 and IV of 25 percent, plus stock wey get beta of 0.2 and IV of 80 percent, both dey normal. Dem carry different types of risk, and different instruments wey fit different jobs dey measure dem.

FAQ

Stock fit get high implied volatility and low beta?

Yes, e common. Beta dey count only movement wey dey move together with the index, while implied volatility dey price all movement, including single-company event wey index no know anything about. Stock wey dey wait for one binary announcement fit get very high IV while e regression slope against broad index dey near zero.

Beta na measure of risk?

Beta dey measure one kind of risk: how sensitive stock be to chosen index over chosen past window. E no talk about company-specific risk, and e no cover anything outside that window. Two stocks wey get identical betas fit still get completely different total volatility.

Wetin R-squared tell you wey beta no tell you?

R-squared na the share of stock return variance wey index dey explain, on scale from 0 to 1. Low R-squared mean say regression line no fit well. So beta wey come from the same calculation na slope through scattered points, instead of clear description of how the stock dey behave.

Implied volatility dey predict which direction stock go move?

No. IV na measure of size, e no get direction. IV of 40 percent dey price one standard deviation move of 40 percent over one year, either up or down, with no preference for any direction. Stock fit move much more or much less than the price wey IV imply.

Which window and return frequency dey enter beta calculation?

No be one standard standard. Common methods dey use anything from one year of daily returns to five years of monthly returns. The first panel above show say daily and weekly sampling for the same year fit produce different slopes. You fit compare beta only with another beta wey dem calculate the same way.

Every panel above come with the SQL wey produce am, so the window and filters dey one click below each chart. If you wan measure beta or implied volatility for any name wey you dey follow, ask for am in plain English on the Strasmore terminal.