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Friends, I had an antipsychotic injection (Fluanxol Depot) this morning. I get this injection after every 45 days as doctor had increased its duration from 30 days to 45 days a bit more than two months ago. When duration between two injections increased, I was able to do much better work and I was not being able to do any creative work earlier despite trying very hard. And now bad people who do n…

Please explain to me like I'm 12, in what way is numerical inversion of a CDF an "explicit" formula? Statistics: Posted by ISayMoo — 47 minutes ago

thanks. and nice to see some people in here are still interested in options stuff. now research focus shall be on computing the IG quantile function more efficiently, i guess. Statistics: Posted by tags — 4 minutes ago

Friends, another possible route to solution of above problem when explaining variables are correlated is to somehow orthogonalize the explaining variables and then use the orthogonalized variables in our analysis. I had thought about this yesterday but problem is that every hermite polynomial component has its own correlation matrix and there is no single correlation matrix that would have to be …

and the compact form (AI made) Public Function BSIV_IG(ByVal Px As Double, ByVal F As Double, ByVal K As Double, ByVal T As Double, ByVal DF As Double, Optional ByVal IsCall As Boolean = True) As Variant On Error GoTo e Dim c As Double, kk As Double, q As Double, m As Double, x As Double If Px < 0 Or F <= 0 Or K <= 0 Or T <= 0 Or DF <= 0 Then GoTo n c = Px / (DF * F) If Not IsCall Then c = c + 1 …

VBA (AI generated, it at least gave the correct implied vol, also very far OTM), But if I have to use Bisection to do the Inverse Gaussian quantile, then is it really much more efficient... well I guess we can to better than this AI code ? ' ============================================================ ' Implied volatility from Schadner arXiv:2604.24480 ' Handles: ' - OTM calls ' - ITM calls ' - O…

" This paper identifies what appears to be the first explicit formula for Black–Scholes implied volatility, resolving a 50-year-old problem in option pricing. The key observa tion is that the call price can be written as a survival probability of an inverse Gaussian distribution." https://arxiv.org/pdf/2604.24480 Statistics: Posted by Collector — 39 minutes ago

Calculation of Conditional Density of Y Given K Explaining Random Variables  \(X_1, X_2, X_3, \ldots \,,X_K\) Are Correlated With Each Other. We re-write the equations of conditional mean of Y and other equations. Then conditional mean of Y given all of \(X_1, X_2, X_3, \ldots \,,X_K\) is given as \[\,E \left[Y\,| \left(Z_{x_1},\,Z_{x_2},\ldots,\,Z_{x_K}\right)\,\right]\,=\,\,ch_0\,+\,\sum\limits…

Friends, when I am trying to write this post about extension of the above algorithm to conditional density generation of Y given several other variables, mind control agents have started to focus microwaves on my exposed right temple so that I would lose nerve or become upset and stop working. Calculation of Conditional Density of Y Given K Correlated Random Variables  \(X_1, X_2, X_3, \ldots \,,…

Friends, I had some of my health tests taken yesterday. Basically LFTs, RFTs, Complete Blood count, and some others. Many of the most important enzymes of my liver are very high and much more than their upper bound since antipsychotic medication affects the liver. My liver has seen a lot of forced antipsychotic drugs that severely damaged it in the past. I used to take Hepa-Merz which is a syrup …

We copy the Bivariate Z-series of Y given X from previous post. Our task is to find correlations \(\rho_n\) in this given Z-series so that Hermite correlations of Y with X are perfectly retrieved. \begin{equation} Y(Z_x\,,\,Z_{\tilde{y}})=\,c_0\,+\sum\limits_{n=1}^{N} c_n\, \, {(\rho_n\, Z_x\,+ \,\zeta_n\,Z_{\tilde{y}})}^n \end{equation} When we expand all binomials in the above expansion, it res…

Ok The reason I asked about variance of variance is that if you want it to be finite, then also E[sigma(z)] must be finite, and then your quadratic relation implies that E[z^2] is finite - so no heavy tails for returns IDK maybe this is a trivial observation, but it seems to me that it narrows down the scope of processes to consider. Statistics: Posted by ISayMoo — 8 minutes ago

Though I believe the canonical reference is: Quant A (1951) Help, my inverse-gamma model just blew up! A field guide. Statistics: Posted by DavidO — 53 minutes ago

Friends, I will write about explaining the algorithms used in matlab program in the afternoon but summing up past few posts, I want to explain how all conditional central moments of a random variable dependent on specific realizations of correlated random variables can be found. Calculation of Conditional Central Moments of Y given Specific Realizations of K Correlated Random Variables \(X_1, X_2…

No, not yet. It seems from my googling that people call more than one thing by this name. Can you give me a reference? Judging by the entries there are plenty of inverse-gamma experts here, so you've come to the right place! Statistics: Posted by DavidO — 21 minutes ago

Coming back to the proposed quadratic relationship: why did we reject inverse Gamma process as its realisation? Because of infinite variance of variance? Have you tried simulating a path? (My apologies if you've already done this here.) No, not yet. It seems from my googling that people call more than one thing by this name. Can you give me a reference? Statistics: Posted by ISayMoo — less than a…

Friends, I was reading my previous notes and then all of a sudden I realized that there could be another different approach to solution of the problem. I had tried to work with this several months earlier but, at that time, I was not thinking clearly because of high medication. However, now I realize, if successfully implemented, it could be a better approach for the solution of the problem so I …

Friends just a reminder, for example in following equations, we have not specifically applied conditionality. In the equations for conditional mean and conditional variance, when we specifically apply conditionality Then conditional mean of Y given all of \(X_1, X_2, X_3, \ldots \,,X_K\) is given as \[\,E \left[Y\,| \left(Z_{x_1},\,Z_{x_2},\ldots,\,Z_{x_K}\right)\,\right]\,=\,\,ah_0\,+\,\sum\limi…