Machine learning in trading: theory, models, practice and algo-trading - page 1378
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Financial mathematics, presented without Ito's stochastic calculus, looks very mysterious and vague.
You mean there aren't enough formulas in the lectures? :D
Modeling Models or Next ModelAfter introducing standard jargon to describe parameter sensitivity, examples of the effect of parameter stochasticity on simple and non-complex prices are reviewed, a brief review of empirical observations is given, standard stochastic models are given, and their application to real life is discussed
Here's the whole list... got hooked yesterday, really like https://www.lektorium.tv/speaker/3058
You mean there aren't enough formulas in the lectures? :D
Ito's integral is a complicated thing, but if you learn it, everything becomes easier and you don't have to come up with crutches for every single problem.
It is like before Newton problems on the form of a chain line were solved by very complicated methods and now they are available even to high school students.
Ito's integral is complicated, but if you learn it, everything becomes easier and you don't have to come up with crutches for every single problem.
This is similar to how problems like the shape of a chain line were solved before Newton by very complicated methods, and now they are quite accessible even to high school students.
I've been reading Investments, Black Scholes, etc., all over Europe. I don't remember any of it.) Maybe I should study it.
I haven't figured it out yet, either.
He's got a course of lectures there, if from the 1st
it's an in-depth analysis of the market structure and models
in general, interesting. Quantum from JP Morgan or who knows.
I walked through Europe with my head in Investments, Black Scholes, etc. Can't remember anything ) Might need to look into it.
The Black-Scholes itself is not very useful, but modifications (perturbations, variations, etc.) are built on it, and for this ito knowledge is very useful.
Black-Scholes itself is not very useful, but modifications (perturbations, variations, etc.) are built on it and for this - ito knowledge is very useful.
I wonder how it can be connected with modern methods of MO, i.e. it will be possible to build models somehow supported by market theory
I wonder how one can relate to modern MO methods, i.e. it would be possible to build models somehow supported by market theory
We need to see how Markovian processes with continuous time are studied by MO methods. In the matstat for such processes the maximum likelihood methods are often used, which is quite similar to MO.
It is necessary to see how Markovian processes with continuous time are studied by MO methods. Matstat often uses maximum likelihood methods for such processes, which is quite similar to MO.
I.e. is the model taken as an efficient market model? or a fractal one, as he, for example, describes in his later lectures. And still Brownian motion, i.e. model of a random walk can also be presented as fractal.
It is not very clear what this theory has come to in the end, and whether it has come to :) it is necessary to study, it is interesting. Or maybe these both are just good approximations and it is possible to take any one of them and work with it.
I.e. is the model taken as an effective market? or all the same fractal, as he, for example, in later lectures describes. And still the Brownian motion, i.e. the model of a random walk can also be presented as fractal.
It is not very clear what this theory has come to in the end, and whether it has come to :) it is necessary to study, it is interesting. Or these both are just good approximations, you can take any of them and work with it.
I'm not strong in economic interpretation, but from the matstat point of view both are processes given by some stochastic diffusers. That is, for all of them the Markovian probability is fulfilled.
I am not strong in the economic interpretation, but from the point of view of the matstat both are processes set by some or other stochastic diffusers. That is, Markovianity is satisfied for all of them.
Interesting how the girls dance... i.e. through a Markovian process a process with "memory" is also defined, through latent states, for example
there was some confusion in my head... and so it turns out that yes, for all is performed. If I understand correctly.