Timeline for Mean Reverting Heston Model?
Current License: CC BY-SA 4.0
12 events
| when toggle format | what | by | license | comment | |
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| Nov 15, 2020 at 18:00 | history | tweeted | twitter.com/StackQuant/status/1328035036524896258 | ||
| Oct 11, 2020 at 20:29 | vote | accept | TheMathBoi | ||
| Oct 10, 2020 at 15:58 | history | edited | TheMathBoi | CC BY-SA 4.0 |
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| Oct 9, 2020 at 23:13 | history | became hot network question | |||
| Oct 9, 2020 at 19:17 | comment | added | Kevin | @TheMathBoi The factor $-\frac{1}{2}v_t$ is normally relates to Ito's Lemma when you take logs, so don't worry about it. Whether you have $\theta-\kappa X_t$ or $\kappa(\theta-X_t)$ doesn't matter either because you can just rescale $\theta$ | |
| Oct 9, 2020 at 19:10 | vote | accept | TheMathBoi | ||
| Oct 9, 2020 at 19:11 | |||||
| Oct 9, 2020 at 19:08 | answer | added | Kermittfrog | timeline score: 3 | |
| Oct 9, 2020 at 17:47 | history | edited | TheMathBoi | CC BY-SA 4.0 |
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| Oct 9, 2020 at 17:43 | comment | added | TheMathBoi | I saw this paper, actually. Or, well, an earlier version of it, anyway. I understand why the authors define $S_t = exp(X_t)$ but fail to see why they define $dX_t = [\theta(t) - \kappa X_t - \frac{v_t}{2}]dt + \sqrt{v_t}dW_t$. The second term makes enough sense to me -- it's merely its equivalent as in the Heston Model -- but why in the world are we subtracting variance from equilibrium mean at time t? And shouldn't we have $\kappa (\theta(t)-X_t)$ for the mean reversion? | |
| Oct 9, 2020 at 16:42 | comment | added | Kermittfrog | Hi, this (look may get you started. You may also start from ‚first principles‘ with Duffie/Pan/Singleton‘s Transform Methods paper . | |
| Oct 9, 2020 at 15:18 | review | First posts | |||
| Oct 10, 2020 at 14:43 | |||||
| Oct 9, 2020 at 15:12 | history | asked | TheMathBoi | CC BY-SA 4.0 |