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Aim for the moon. If you miss, you may hit a star.
Ph.D. candidate at University of Illinois Interested in physics, statistics and quantatitive finance
Happiness and enjoyment
Accmulate in the learning path
Study of Garch model with realized variance and study the dynamic conditional correlation model on 6 stock stock data.
My son turns to one month and he is so cute. He has a name now : Xuanyi Ben Fan (范本轩逸).
Travel Plan I have always been dreaming of traveling to Hawaii and enjoy the beach and sunshine. This spring break we got cheap tickets and make our plan to...
My son is born !
Here is an interesting situation occurred to me. In a scatter plot, it is clear that there are two straight lines and each line is contanminated by some nois...
I am doing a service task for CERN on a hardware project. I learned something on FPGA and some basic vivado code and glad to share here.
I am writing this post is because I recently met some problem of writing an Athena Algorithm for my research. I am using a simple code to get lepton n-tuples...
If we regress $X$ on $Y$ and get $\beta$ as the slope and if we regress $Y$ on $X$ and get slope $\beta’$. What is the product of $\beta$ and $\beta’$
Problem 1 If we have $n$ dice draws with a dice that has $k$ sides. Let $X_i$ to be the random variables that describes the number of times that the dice fac...
First Lecture: Intorduction of autocorrleation for linear time series
Calculate the variance and convergence rates for autocorrelation sample estimator by central limit theorem. Introduce statistical tests for ACF.
Some introduction of AR model and linear model covariance calculation.
Introduce AR(2) model and disucss the condition of weakly stationary for AR(1) and AR(2) models
Periodicity of AR models with complex roots; Asymptotic statistics of AR(p) models
Introduction to PACF and Information criteria (AIC, BIC)
Introduction to MA model and conditional loglikelihood maximization method. Introdcution to ARMA model
Introduction to ARMA model and its AR and MA representation
Introduction of Karhunen-Leove theorem, the spectral theorem and autocovariance generating function
Spectral density as the Fourier transform of autocovariance, the sample version spectral representation theory and sample periodogram
Spectral representation sample version proof. Variance decomposition into Fourier components. Estimate spectral density
Non parametric estimation of spectral density (Kernel Method)
Introduction to Conditional Variance
Introduction to ARCH model
Estimation of ARCH Model with method of moments
Introduction to Generalized ARCH and model estimation and forecast
Introduction to Non-linear Models including TAR ans SETAR
Introduction to unit root models
Construct statistical test for unit root from samples
Random Walk with Drift term and statistical test for non-zero drift
Statistical Test for The Drift Term
Continuous Time Model
Introduction to Ito’s Process and Ito’s Lemma
Introduction to Ito’s Process and Ito’s Lemma
Application of Ito’s Lemma: Derivation of Black-Scholes Formula
Diffusion Processes with Jumps
Continutation of Jump Diffusion Model and Introduction to Multiple Time Series
Introduction to Vector Autoregressive (VAR) model
Introduction to Cointegration and Vector Error Correction Model
Introduction to Vector Error Correction Model
Introduction to state space model, the Kalman Filterl
Continuation of the discussion of Kalman Filter