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Volatility modelling in R exercises (Part-4)

July 17, 2017
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Volatility modelling in R exercises (Part-4)

This is the fourth part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will explore GARCH-M and E-GARCH models. We will also use these models to generate rolling window forecasts, bootstrap forecasts and perform simulations. Answers to the exercises are available here. Related exercise sets: Volatility modelling...

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Volatility modelling in R exercises (Part-3)

July 10, 2017
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Volatility modelling in R exercises (Part-3)

This is the third part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use GARCH models to forecast volatility. Answers to the exercises are available here. Exercise 1 Load the rugarch and the FinTS packages. Next, load the m.ibmspln dataset from Related exercise sets: Forecasting: Linear...

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Volatility modelling in R exercises (Part-2)

July 3, 2017
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Volatility modelling in R exercises (Part-2)

This is the second part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use the dmbp dataset from part-1 and extend our analysis to GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models. Answers to the exercises are available here. Exercise 1 Load the Related exercise sets: Volatility modelling...

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Volatility modelling in R exercises (Part-1)

June 26, 2017
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Volatility modelling in R exercises (Part-1)

Volatility modelling is typically used for high frequency financial data. Asset returns are typically uncorrelated while the variation of asset prices (volatility) tends to be correlated across time. In this exercise set we will use the rugarch package (package description: here) to implement the ARCH (Autoregressive Conditional Heteroskedasticity) model in R. Answers to the exercises Related exercise sets: Forecasting: Multivariate...

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Ridge regression in R exercises

June 19, 2017
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Ridge regression in R exercises

Bias vs Variance tradeoff is always encountered in applying supervised learning algorithms. Least squares regression provides a good fit for the training set but can suffer from high variance which lowers predictive ability. To counter this problem, we can regularize the beta coefficients by employing a penalization term. Ridge regression applies l2 penalty to the Related exercise sets: LASSO regression...

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LASSO regression in R exercises

June 12, 2017
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LASSO regression in R exercises

Lease Absolute Shrinkage and Selection Operator (LASSO) performs regularization and variable selection on a given model. Depending on the size of the penalty term, LASSO shrinks less relevant predictors to (possibly) zero. Thus, it enables us to consider a more parsimonious model. In this exercise set we will use the glmnet package (package description: here) Related exercise sets: Evaluate your...

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Quantile Regression in R exercises

June 5, 2017
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Quantile Regression in R exercises

The standard OLS (Ordinary Least Squares) model explains the relationship between independent variables and the conditional mean of the dependent variable. In contrast, quantile regression models this relationship for different quantiles of the dependent variable. In this exercise set we will use the quantreg package (package description: here) to implement quantile regression in R. Answers Related exercise sets:Forecasting: Multivariate...

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Instrumental Variables in R exercises (Part-3)

May 29, 2017
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Instrumental Variables in R exercises (Part-3)

This is the third part of the series on Instrumental Variables. For other parts of the series follow the tag instrumental variables. In this exercise set we will use Generalized Method of Moments (GMM) estimation technique using the examples from part-1 and part-2. Recall that GMM estimation relies on the relevant moment conditions. For OLS Related exercise sets:Instrumental Variables...

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Instrumental Variables in R exercises (Part-2)

May 22, 2017
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Instrumental Variables in R exercises (Part-2)

This is the second part of the series on Instrumental Variables. For other parts of the series follow the tag instrumental variables. In this exercise set we will build on the example from part-1. We will now consider an over-identified case i.e. we have multiple IVs for an endogenous variable. We will also look at Related exercise sets:Instrumental Variables...

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Instrumental Variables in R exercises (Part-1)

May 15, 2017
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Instrumental Variables in R exercises (Part-1)

One of the most frequently encountered issues in econometrics is endogeneity. Consider the simple Ordinary Least Squares (OLS) regression setting in which we model wages as a function of years of schooling (education): One of the main assumption of OLS is that the independent variables are not correlated with the error term. However, this is Related exercise sets:Forecasting: ARIMAX...

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