This is a Github page, managed by Ashiq Zaman who replicated some of the key statistical analysis by using Timeseries data with R. This page presents Structural Break Cointegration test, forecasting stock return with ARIMA model, decomposing data, Unit Root test, Cointegration Test, Volatility analysis, Bootstrapping, Bayesian Statistics, Stochastic Process in Finance and Timeseries Regression and Forecasting and Data visualization with Quantmod and ggplot2.
Latest Posts
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Anomaly Detection With Time Series Data
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ARIMA Model for Stock Forecasting
ARIMA (autoregressive integrated moving average) is one of the most common statistical technique which used to fit time series data and forecasting. ARIMA is a generalized version of ARMA (autoregressive...
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ARCH Models for Stock Returns
The autoregressive conditional heteroskedasticity (ARCH) model concerns time series with time-varying heteroskedasticity, where variance is conditional on the information existing at a given point in time.
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ARIMA model for Stock Forecasting: Part One
This is a part one of ARIMA forecasting for STOXX50 index. This part going to show some exploratory Data analysis which shows preparing R with required packages, importing data and...
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Unit Root Tests with Structural Breaks
In this post, I examine the issue of identifying unit roots in the presence of structural breaks. Stationary series have a mean and covariance that do not change over time....
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Structural Break Cointegration Test between Stock Markets
The foundation for estimating breaks in time series regression models was given by Bai (1994) and was extended to multiple breaks by Bai (1997ab) and Bai & Perron (1998). Bai...
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Time-Varrying Volatility with GARCH Model
The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term developed in 1982 by Robert F. Engle, an economist and 2003 winner of the Nobel Memorial Prize for Economics,...
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Time Series Regression and Forecasting
The purpose of econometric analysis is to estimate the model’s parameters and test hypotheses about those parameters; the values and signs of the parameters can determine the validity (or not)...
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In-Sample and Out-of-Sample Stock Return Predictability
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Stochastic Process in Finance
Stochastic modeling presents data and predicts outcomes that account for certain levels of unpredictability or randomness.
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Introduction to Stock Analysis with R
Firstly, we install and load the necessary packages.To analyse stock data and to get stock data from the internet, we will use quantmod and ggplot2. Firstly, we install and load...
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Bayesian Approach & Bootstrapping Computation in Finance
I’m going to implement a Bayesian approach linear regression in R from scratch and use it to forecast FTSE100 return by using Dividend Yield.