Publications by Montserrat Guzmán
Workshop7, Financial Econometrics2
1 Spurious Regression When we want to examine the relationship between two non-stationary variables by running a regression model, we have the risk to end up with a non-valid - spurious - regression. #we need to install wbstats package library(wbstats) ## Warning: package 'wbstats' was built under R version 4.0.5 # Mexico - Infant mortality inf...
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Workshop6, Financial Econometrics2
1 Forecasting consumer demand with an ARIMAX model Assume that this industry is an oligopoly where there are few main players in the market that account for most of the market share. In an oligopoly, it is very common that price elasticity of demand is high and sensitive. In other words, when a player decreases the price of a product, it is very ...
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Workshop5, Financial Econometrics V1
1 Modelimg the Mexican Economy 1.1 Data Download library(readxl) library(xts) library(zoo) library(tseries) library(forecast) library(astsa) library(lmtest) igae <- read_xls("IGAE.xls", sheet = "Página 1") # I create the a ts dataset indicating the dates: igae.ts<-ts(coredata(igae$IGAE),start=c(1993,1),frequency=12) igae.ts ## ...
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Workshop4, Financial Econometrics
1 Abstract In this workshop we learn how to design/calibrate a seasonal ARIMA model (SARIMA) for a time series. 2 Introduction to ARIMA-SARIMA Models An ARIMA/SARIMA model is applied to monthly or quarterly time series. It shows: 1 Historical monthly or quarterly sales data 2 Historical monthly or quarterly price data 3 Historical monthly or qua...
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Workshop2, Financial Econometrics2
1 Non Stationay Variables- the random walk model for stock prices The random walk hypothesis in Finance (Fama, 1965) states that the natural logarithm of stock prices behaves like a random walk with a drift. A random walk is a series (or variable) that cannot be predicted. Yt=φ0+Yt−1+εt If we want to simulate a random walk, we need the values...
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Workshop3, Financial Econometrics2
1 Abstract The main purpose of this workshop is to illustrate the foundations of Autoregressive Moving Average models - ARMA. We will apply the ARMA model to a dataset of sales of real consumer products. This methodology is part of the Box-Jenkins models for time series, which provide a series of tools to understand past behaviour and predict fut...
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Workshop1, Financial Econometrics
1 Introduction to Time Series First we should clear the environment and download financial data from Yahoo Finance We star clearing our R environment rm(list=ls()) # To avoid scientific notation for numbers: options(scipen=999) I will load the quantmod package library(quantmod) ## Warning: package 'quantmod' was built under R version 4.0.3 ## ...
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Workshop5, Financial Econometrics
1 Modelimg the Mexican Economy 1.1 Data Download library(readxl) library(xts) library(zoo) library(tseries) library(forecast) library(astsa) library(lmtest) igae <- read_xls("IGAE.xls", sheet = "Página 1") # I create the a ts dataset indicating the dates: igae.ts<-ts(coredata(igae$IGAE),start=c(1993,1),frequency=12) igae.ts ## ...
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Exam1-Financial Econometrics 2
1 Modeling the Mexican peso exchange rate (MX/USD) 1.1 Get data library(quantmod) ## Warning: package 'quantmod' was built under R version 4.0.3 ## Loading required package: xts ## Loading required package: zoo ## Warning: package 'zoo' was built under R version 4.0.3 ## ## Attaching package: 'zoo' ## The following objects are masked from 'pac...
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Workshop10, Financial Econometrics2
# Set all the working directory library(readxl) library(dplyr) library(xts) library(zoo) library(tseries) library(forecast) library(astsa) library(lmtest) #Cumulative volatility and VaR library(quantmod) ## Warning: package 'quantmod' was built under R version 4.0.3 ## Loading required package: TTR MXindex<-getSymbols("^MXX", from="2018-...
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