Welcome to Prof. Daniel P. Palomar's video channel!
Here you will find some of his technical presentations.
Prof. Daniel Palomar is with the Hong Kong University of Science and Technology (HKUST) Homepage: www.danielppalomar.com GitHub: github.com/dppalomar Google Scholar: scholar.google.com/citations?user=qlReqq8AAAAJ
Wouldn't framing this problem as a reinforcement learning (RL) task be more fitting than using LSTM or other predictive models? The challenge lies in determining the appropriate output (target) for the LSTM (tomorrows increase? next week? next month?).
porfavor nunca deje de subir videos! desearia poder seguir estudiando en universidad para tomar estas oportunidades, lo bueno es que pude encontrarlo en youtube y aprender de sus papers, libros y videos, gracias por compartir lo que sabe por aqui! espero poder seguir viendo mas de usted
Daniel, que bendición poder encontrar un canal como este, estudie mecatronica y vi temas como llaman o espacio de estados y los aplique en robots pero jamás pensé que pudieran aplicarse en el mercado de valores, espero sigas subiendo más videos ya que esta aplicación es fascinante. Thanks also to the great student that present the lecture 😊
He repeatedly misspelled 'Markov' in his slides. What a weird mistake to make for someone who works in his field. (that aside the talk was very interesting and he presented it masterfully ...)
6:35. SP500 log returns are locally stationary but that the argument for assigning it a non-stationary designation via the existence of volatility clustering of any type is not valid. Volatility clustering doesn't not necessarily imply non-stationary. Slowly varying unconditional volatility implies non-stationarity but GARCHian class volatility for most parameters does not.
Sure, it worths a trial! Consider modeling the joint movement of daily volume & daily volatility (named as Mixed Distribution Hypothesis in the literature). Then the intraday periodic components may not be needed in that case.
This is absolutely amazing, currently an undergraduate studying mathematics with limited statistical knowledge however your concepts especially discussing co-integration and using Kalman filter for pairs trading was incredibly insightful. Took plenty of notes and will definitely be returning to this video. Huge thanks Professor!
where can I learn more of these, risk parity portfolio or its graph version? Is there books/ lecture recommendation for these? Your signal processing book chapter 9 covers it, but I want even more 😂. Thanks in advance
I wish that I had you here teaching and motivating on such great topics in one of the universities here in Switzerland ,, we really miss such great talents here in Switzerland , thank Prof.Palomar
following on my earlier comment , the very huge blib of march 2020 is a big concern of any investor whose objective is to maximize cagr or equivalently geometric returns , do you agree ?
