How to verify sticky delta property on a stochastic volatility model





.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{
margin-bottom:0;
}
.everyonelovesstackoverflow{position:absolute;height:1px;width:1px;opacity:0;top:0;left:0;pointer-events:none;}








1












$begingroup$


Given a stochastic model for the evolution of St, with a given SDE for its volatility, how can you tell if the given model satisfy the sticky delta (or the sticky strike) property? Is it possible to prove analytically this property? Or the only way is to actually compute the prices?










share|improve this question









$endgroup$














  • $begingroup$
    You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
    $endgroup$
    – will
    May 27 at 9:40










  • $begingroup$
    I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
    $endgroup$
    – Giacomo Giannoni
    May 27 at 10:03


















1












$begingroup$


Given a stochastic model for the evolution of St, with a given SDE for its volatility, how can you tell if the given model satisfy the sticky delta (or the sticky strike) property? Is it possible to prove analytically this property? Or the only way is to actually compute the prices?










share|improve this question









$endgroup$














  • $begingroup$
    You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
    $endgroup$
    – will
    May 27 at 9:40










  • $begingroup$
    I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
    $endgroup$
    – Giacomo Giannoni
    May 27 at 10:03














1












1








1





$begingroup$


Given a stochastic model for the evolution of St, with a given SDE for its volatility, how can you tell if the given model satisfy the sticky delta (or the sticky strike) property? Is it possible to prove analytically this property? Or the only way is to actually compute the prices?










share|improve this question









$endgroup$




Given a stochastic model for the evolution of St, with a given SDE for its volatility, how can you tell if the given model satisfy the sticky delta (or the sticky strike) property? Is it possible to prove analytically this property? Or the only way is to actually compute the prices?







stochastic-volatility






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked May 27 at 9:24









Giacomo GiannoniGiacomo Giannoni

63 bronze badges




63 bronze badges















  • $begingroup$
    You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
    $endgroup$
    – will
    May 27 at 9:40










  • $begingroup$
    I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
    $endgroup$
    – Giacomo Giannoni
    May 27 at 10:03


















  • $begingroup$
    You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
    $endgroup$
    – will
    May 27 at 9:40










  • $begingroup$
    I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
    $endgroup$
    – Giacomo Giannoni
    May 27 at 10:03
















$begingroup$
You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
$endgroup$
– will
May 27 at 9:40




$begingroup$
You'll not be able to do it for $t_0$, as you'll need to bump your data, so you'll be forced to choose how you bump your smile (i.e. sticky delta or strike), i.e. you force the model to do what you want. You can however look at the conditional vol in the future, take a grid of points, say $S_t=90, S_t=91, ldots, S_t=110$ at $t=0.5$ and for each point measure the conditional distribution at $t=1$ (with stoch vol you need to sample the vol too, so you'll have a 2d grid). You'll get a corresponding smile for each, and now you can observe how the model changes the smile for different spots.
$endgroup$
– will
May 27 at 9:40












$begingroup$
I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
$endgroup$
– Giacomo Giannoni
May 27 at 10:03




$begingroup$
I asked explicitly for an analytic solution, I already know, given prices, how to do it, but that was not the question...
$endgroup$
– Giacomo Giannoni
May 27 at 10:03










1 Answer
1






active

oldest

votes


















3














$begingroup$

I agree with the comment made by will: for a given model, you can potentially compute a Delta according to any "stickiness rule" depending on which data you decide to bump vs. keep constant.



That being said, if you look at the following quantity
$$ Delta = left. frac{partial V}{partial S_0} rightvert_{Theta} $$
that we could call the in-model Delta as in "all parameters and state variables except the spot price are held constant" (e.g. $Theta = (v_0,theta,kappa,rho,xi)$ in Heston), then you can say that:




For a (log-) space homogeneous diffusion model, $Delta = left. frac{partial V}{partial S_0} rightvert_{Theta}$ will be a sticky-moneyness Delta.




A (log-) space homogeneous model is simply one where
$$ frac{dS_t}{S_t} = mu_t dt + sigma_t dW_t $$
where both the drift and diffusion coefficients cannot be direct functions of $S_t$ (e.g. no a local volatility model), such that after using Itô, you can directly integrate to obtain that $S_T/S_t$ does not depend on $S_t$ for any $T geq t$.



As a result of this last property, European vanilla prices end up being homogeneous functions of degree 1 in space i.e. for a spot price $S_0$, strike and expiry $(K,T)$
$$ C(xi S_0, xi K, T; Theta) = xi C(S_0, K, T; Theta), ,,forall xi > 0 $$
such that (Euler's theorem, or just deriving the above wrt $xi$ and setting $xi = 1$
$$ C = Delta S_0 + frac{partial C}{partial K} K tag{1} $$



Now, if you assume the model generates a volatility surface $Sigma(S_0;K,T,Theta)$ where $Sigma$ is the function defined through
$$ C(S_0,K,T;Theta) := C_{BS}(S_0, K, T; Sigma(S_0,K,T;Theta)) $$
then, starting from $(1)$, using the chain-rule and the fact that BS model is (log)-space homogeneous, you will get that
$$ frac{partial Sigma}{partial S_0}(S_0,K,T;Theta) = -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0,K,T;Theta) tag{2} $$
which is indeed the definition of the sticky-moneyness rule.



Indeed, sticky moneyness suggests that
$$ Sigma(S_0+delta S_0, K, T) = Sigma(S_0, K^*, T) $$
provided, as the name indicates, that $$frac{K^*}{S_0} = frac{K}{S_0+delta S_0} iff K^* = K(1 + delta S_0/S_0)^{-1}$$
Under such circumstances,
begin{align}
frac{partial Sigma}{partial S_0}(S_0, K, T) &= lim_{delta S_0 to 0} frac{Sigma(S_0+delta S_0, K, T) - Sigma(S_0, K, T)}{delta S_0} nonumber \
&= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 + delta S_0/S_0)^{-1}, Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
&= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 - delta S_0/S_0), Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
&= lim_{delta K to 0} frac{Sigmaleft(S_0, K-delta K, Tright) - Sigma(S_0, K, T)}{frac{S_0}{K}delta K} nonumber\
&= -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0, K, T)
end{align}






share|improve this answer









$endgroup$

















    Your Answer








    StackExchange.ready(function() {
    var channelOptions = {
    tags: "".split(" "),
    id: "204"
    };
    initTagRenderer("".split(" "), "".split(" "), channelOptions);

    StackExchange.using("externalEditor", function() {
    // Have to fire editor after snippets, if snippets enabled
    if (StackExchange.settings.snippets.snippetsEnabled) {
    StackExchange.using("snippets", function() {
    createEditor();
    });
    }
    else {
    createEditor();
    }
    });

    function createEditor() {
    StackExchange.prepareEditor({
    heartbeatType: 'answer',
    autoActivateHeartbeat: false,
    convertImagesToLinks: false,
    noModals: true,
    showLowRepImageUploadWarning: true,
    reputationToPostImages: null,
    bindNavPrevention: true,
    postfix: "",
    imageUploader: {
    brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
    contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/4.0/"u003ecc by-sa 4.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
    allowUrls: true
    },
    noCode: true, onDemand: true,
    discardSelector: ".discard-answer"
    ,immediatelyShowMarkdownHelp:true
    });


    }
    });















    draft saved

    draft discarded
















    StackExchange.ready(
    function () {
    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fquant.stackexchange.com%2fquestions%2f45816%2fhow-to-verify-sticky-delta-property-on-a-stochastic-volatility-model%23new-answer', 'question_page');
    }
    );

    Post as a guest















    Required, but never shown

























    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3














    $begingroup$

    I agree with the comment made by will: for a given model, you can potentially compute a Delta according to any "stickiness rule" depending on which data you decide to bump vs. keep constant.



    That being said, if you look at the following quantity
    $$ Delta = left. frac{partial V}{partial S_0} rightvert_{Theta} $$
    that we could call the in-model Delta as in "all parameters and state variables except the spot price are held constant" (e.g. $Theta = (v_0,theta,kappa,rho,xi)$ in Heston), then you can say that:




    For a (log-) space homogeneous diffusion model, $Delta = left. frac{partial V}{partial S_0} rightvert_{Theta}$ will be a sticky-moneyness Delta.




    A (log-) space homogeneous model is simply one where
    $$ frac{dS_t}{S_t} = mu_t dt + sigma_t dW_t $$
    where both the drift and diffusion coefficients cannot be direct functions of $S_t$ (e.g. no a local volatility model), such that after using Itô, you can directly integrate to obtain that $S_T/S_t$ does not depend on $S_t$ for any $T geq t$.



    As a result of this last property, European vanilla prices end up being homogeneous functions of degree 1 in space i.e. for a spot price $S_0$, strike and expiry $(K,T)$
    $$ C(xi S_0, xi K, T; Theta) = xi C(S_0, K, T; Theta), ,,forall xi > 0 $$
    such that (Euler's theorem, or just deriving the above wrt $xi$ and setting $xi = 1$
    $$ C = Delta S_0 + frac{partial C}{partial K} K tag{1} $$



    Now, if you assume the model generates a volatility surface $Sigma(S_0;K,T,Theta)$ where $Sigma$ is the function defined through
    $$ C(S_0,K,T;Theta) := C_{BS}(S_0, K, T; Sigma(S_0,K,T;Theta)) $$
    then, starting from $(1)$, using the chain-rule and the fact that BS model is (log)-space homogeneous, you will get that
    $$ frac{partial Sigma}{partial S_0}(S_0,K,T;Theta) = -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0,K,T;Theta) tag{2} $$
    which is indeed the definition of the sticky-moneyness rule.



    Indeed, sticky moneyness suggests that
    $$ Sigma(S_0+delta S_0, K, T) = Sigma(S_0, K^*, T) $$
    provided, as the name indicates, that $$frac{K^*}{S_0} = frac{K}{S_0+delta S_0} iff K^* = K(1 + delta S_0/S_0)^{-1}$$
    Under such circumstances,
    begin{align}
    frac{partial Sigma}{partial S_0}(S_0, K, T) &= lim_{delta S_0 to 0} frac{Sigma(S_0+delta S_0, K, T) - Sigma(S_0, K, T)}{delta S_0} nonumber \
    &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 + delta S_0/S_0)^{-1}, Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
    &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 - delta S_0/S_0), Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
    &= lim_{delta K to 0} frac{Sigmaleft(S_0, K-delta K, Tright) - Sigma(S_0, K, T)}{frac{S_0}{K}delta K} nonumber\
    &= -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0, K, T)
    end{align}






    share|improve this answer









    $endgroup$




















      3














      $begingroup$

      I agree with the comment made by will: for a given model, you can potentially compute a Delta according to any "stickiness rule" depending on which data you decide to bump vs. keep constant.



      That being said, if you look at the following quantity
      $$ Delta = left. frac{partial V}{partial S_0} rightvert_{Theta} $$
      that we could call the in-model Delta as in "all parameters and state variables except the spot price are held constant" (e.g. $Theta = (v_0,theta,kappa,rho,xi)$ in Heston), then you can say that:




      For a (log-) space homogeneous diffusion model, $Delta = left. frac{partial V}{partial S_0} rightvert_{Theta}$ will be a sticky-moneyness Delta.




      A (log-) space homogeneous model is simply one where
      $$ frac{dS_t}{S_t} = mu_t dt + sigma_t dW_t $$
      where both the drift and diffusion coefficients cannot be direct functions of $S_t$ (e.g. no a local volatility model), such that after using Itô, you can directly integrate to obtain that $S_T/S_t$ does not depend on $S_t$ for any $T geq t$.



      As a result of this last property, European vanilla prices end up being homogeneous functions of degree 1 in space i.e. for a spot price $S_0$, strike and expiry $(K,T)$
      $$ C(xi S_0, xi K, T; Theta) = xi C(S_0, K, T; Theta), ,,forall xi > 0 $$
      such that (Euler's theorem, or just deriving the above wrt $xi$ and setting $xi = 1$
      $$ C = Delta S_0 + frac{partial C}{partial K} K tag{1} $$



      Now, if you assume the model generates a volatility surface $Sigma(S_0;K,T,Theta)$ where $Sigma$ is the function defined through
      $$ C(S_0,K,T;Theta) := C_{BS}(S_0, K, T; Sigma(S_0,K,T;Theta)) $$
      then, starting from $(1)$, using the chain-rule and the fact that BS model is (log)-space homogeneous, you will get that
      $$ frac{partial Sigma}{partial S_0}(S_0,K,T;Theta) = -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0,K,T;Theta) tag{2} $$
      which is indeed the definition of the sticky-moneyness rule.



      Indeed, sticky moneyness suggests that
      $$ Sigma(S_0+delta S_0, K, T) = Sigma(S_0, K^*, T) $$
      provided, as the name indicates, that $$frac{K^*}{S_0} = frac{K}{S_0+delta S_0} iff K^* = K(1 + delta S_0/S_0)^{-1}$$
      Under such circumstances,
      begin{align}
      frac{partial Sigma}{partial S_0}(S_0, K, T) &= lim_{delta S_0 to 0} frac{Sigma(S_0+delta S_0, K, T) - Sigma(S_0, K, T)}{delta S_0} nonumber \
      &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 + delta S_0/S_0)^{-1}, Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
      &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 - delta S_0/S_0), Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
      &= lim_{delta K to 0} frac{Sigmaleft(S_0, K-delta K, Tright) - Sigma(S_0, K, T)}{frac{S_0}{K}delta K} nonumber\
      &= -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0, K, T)
      end{align}






      share|improve this answer









      $endgroup$


















        3














        3










        3







        $begingroup$

        I agree with the comment made by will: for a given model, you can potentially compute a Delta according to any "stickiness rule" depending on which data you decide to bump vs. keep constant.



        That being said, if you look at the following quantity
        $$ Delta = left. frac{partial V}{partial S_0} rightvert_{Theta} $$
        that we could call the in-model Delta as in "all parameters and state variables except the spot price are held constant" (e.g. $Theta = (v_0,theta,kappa,rho,xi)$ in Heston), then you can say that:




        For a (log-) space homogeneous diffusion model, $Delta = left. frac{partial V}{partial S_0} rightvert_{Theta}$ will be a sticky-moneyness Delta.




        A (log-) space homogeneous model is simply one where
        $$ frac{dS_t}{S_t} = mu_t dt + sigma_t dW_t $$
        where both the drift and diffusion coefficients cannot be direct functions of $S_t$ (e.g. no a local volatility model), such that after using Itô, you can directly integrate to obtain that $S_T/S_t$ does not depend on $S_t$ for any $T geq t$.



        As a result of this last property, European vanilla prices end up being homogeneous functions of degree 1 in space i.e. for a spot price $S_0$, strike and expiry $(K,T)$
        $$ C(xi S_0, xi K, T; Theta) = xi C(S_0, K, T; Theta), ,,forall xi > 0 $$
        such that (Euler's theorem, or just deriving the above wrt $xi$ and setting $xi = 1$
        $$ C = Delta S_0 + frac{partial C}{partial K} K tag{1} $$



        Now, if you assume the model generates a volatility surface $Sigma(S_0;K,T,Theta)$ where $Sigma$ is the function defined through
        $$ C(S_0,K,T;Theta) := C_{BS}(S_0, K, T; Sigma(S_0,K,T;Theta)) $$
        then, starting from $(1)$, using the chain-rule and the fact that BS model is (log)-space homogeneous, you will get that
        $$ frac{partial Sigma}{partial S_0}(S_0,K,T;Theta) = -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0,K,T;Theta) tag{2} $$
        which is indeed the definition of the sticky-moneyness rule.



        Indeed, sticky moneyness suggests that
        $$ Sigma(S_0+delta S_0, K, T) = Sigma(S_0, K^*, T) $$
        provided, as the name indicates, that $$frac{K^*}{S_0} = frac{K}{S_0+delta S_0} iff K^* = K(1 + delta S_0/S_0)^{-1}$$
        Under such circumstances,
        begin{align}
        frac{partial Sigma}{partial S_0}(S_0, K, T) &= lim_{delta S_0 to 0} frac{Sigma(S_0+delta S_0, K, T) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 + delta S_0/S_0)^{-1}, Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 - delta S_0/S_0), Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta K to 0} frac{Sigmaleft(S_0, K-delta K, Tright) - Sigma(S_0, K, T)}{frac{S_0}{K}delta K} nonumber\
        &= -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0, K, T)
        end{align}






        share|improve this answer









        $endgroup$



        I agree with the comment made by will: for a given model, you can potentially compute a Delta according to any "stickiness rule" depending on which data you decide to bump vs. keep constant.



        That being said, if you look at the following quantity
        $$ Delta = left. frac{partial V}{partial S_0} rightvert_{Theta} $$
        that we could call the in-model Delta as in "all parameters and state variables except the spot price are held constant" (e.g. $Theta = (v_0,theta,kappa,rho,xi)$ in Heston), then you can say that:




        For a (log-) space homogeneous diffusion model, $Delta = left. frac{partial V}{partial S_0} rightvert_{Theta}$ will be a sticky-moneyness Delta.




        A (log-) space homogeneous model is simply one where
        $$ frac{dS_t}{S_t} = mu_t dt + sigma_t dW_t $$
        where both the drift and diffusion coefficients cannot be direct functions of $S_t$ (e.g. no a local volatility model), such that after using Itô, you can directly integrate to obtain that $S_T/S_t$ does not depend on $S_t$ for any $T geq t$.



        As a result of this last property, European vanilla prices end up being homogeneous functions of degree 1 in space i.e. for a spot price $S_0$, strike and expiry $(K,T)$
        $$ C(xi S_0, xi K, T; Theta) = xi C(S_0, K, T; Theta), ,,forall xi > 0 $$
        such that (Euler's theorem, or just deriving the above wrt $xi$ and setting $xi = 1$
        $$ C = Delta S_0 + frac{partial C}{partial K} K tag{1} $$



        Now, if you assume the model generates a volatility surface $Sigma(S_0;K,T,Theta)$ where $Sigma$ is the function defined through
        $$ C(S_0,K,T;Theta) := C_{BS}(S_0, K, T; Sigma(S_0,K,T;Theta)) $$
        then, starting from $(1)$, using the chain-rule and the fact that BS model is (log)-space homogeneous, you will get that
        $$ frac{partial Sigma}{partial S_0}(S_0,K,T;Theta) = -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0,K,T;Theta) tag{2} $$
        which is indeed the definition of the sticky-moneyness rule.



        Indeed, sticky moneyness suggests that
        $$ Sigma(S_0+delta S_0, K, T) = Sigma(S_0, K^*, T) $$
        provided, as the name indicates, that $$frac{K^*}{S_0} = frac{K}{S_0+delta S_0} iff K^* = K(1 + delta S_0/S_0)^{-1}$$
        Under such circumstances,
        begin{align}
        frac{partial Sigma}{partial S_0}(S_0, K, T) &= lim_{delta S_0 to 0} frac{Sigma(S_0+delta S_0, K, T) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 + delta S_0/S_0)^{-1}, Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta S_0 to 0} frac{Sigmaleft(S_0, K(1 - delta S_0/S_0), Tright) - Sigma(S_0, K, T)}{delta S_0} nonumber \
        &= lim_{delta K to 0} frac{Sigmaleft(S_0, K-delta K, Tright) - Sigma(S_0, K, T)}{frac{S_0}{K}delta K} nonumber\
        &= -frac{K}{S_0} frac{partial Sigma}{partial K}(S_0, K, T)
        end{align}







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered May 27 at 10:28









        QuantupleQuantuple

        11.2k1 gold badge15 silver badges45 bronze badges




        11.2k1 gold badge15 silver badges45 bronze badges


































            draft saved

            draft discarded



















































            Thanks for contributing an answer to Quantitative Finance Stack Exchange!


            • Please be sure to answer the question. Provide details and share your research!

            But avoid



            • Asking for help, clarification, or responding to other answers.

            • Making statements based on opinion; back them up with references or personal experience.


            Use MathJax to format equations. MathJax reference.


            To learn more, see our tips on writing great answers.




            draft saved


            draft discarded














            StackExchange.ready(
            function () {
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fquant.stackexchange.com%2fquestions%2f45816%2fhow-to-verify-sticky-delta-property-on-a-stochastic-volatility-model%23new-answer', 'question_page');
            }
            );

            Post as a guest















            Required, but never shown





















































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown

































            Required, but never shown














            Required, but never shown












            Required, but never shown







            Required, but never shown







            Popular posts from this blog

            He _____ here since 1970 . Answer needed [closed]What does “since he was so high” mean?Meaning of “catch birds for”?How do I ensure “since” takes the meaning I want?“Who cares here” meaningWhat does “right round toward” mean?the time tense (had now been detected)What does the phrase “ring around the roses” mean here?Correct usage of “visited upon”Meaning of “foiled rail sabotage bid”It was the third time I had gone to Rome or It is the third time I had been to Rome

            Bunad

            Færeyskur hestur Heimild | Tengill | Tilvísanir | LeiðsagnarvalRossið - síða um færeyska hrossið á færeyskuGott ár hjá færeyska hestinum