Returns

library(psych)

library(plm)

library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(sandwich)

library(ggplot2)

## 
## Attaching package: 'ggplot2'

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##     %+%, alpha

library(dplyr)

## 
## Attaching package: 'dplyr'

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##     between, lag, lead

## The following objects are masked from 'package:stats':
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##     intersect, setdiff, setequal, union

library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

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##     recode

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library(AER)

## Loading required package: survival

library(texreg)

## Version:  1.39.3
## Date:     2023-11-09
## Author:   Philip Leifeld (University of Essex)
## 
## Consider submitting praise using the praise or praise_interactive functions.
## Please cite the JSS article in your publications -- see citation("texreg").

library(dynlm)

df <- read.csv('msci.csv')
df

library(TSstudio)

df$Date.1 <- NULL
df

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

#convert character to date format
df$Date <- ymd(df$Date)
df

class(df$Date)

## [1] "Date"

ts_plot(df)

library(plotly)

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library(TSstudio)
ts_plot(df,
        title = "MSCI Daily Cumulative Returns",
        Xtitle = "Time",
        Ytitle = "%",
        slider = TRUE
       )

# Initialize an empty vector to store R-squared values
rsquared_values <- numeric()
Synchronicity.Russia.World <- numeric()

# Number of observations in your dataset
n_obs <- nrow(df)

# Loop through the dataset
for (i in 262:(n_obs - 1)) {
  # Subset the data for the current window
  window_data <- df[(i - 261):i, ]
  
  # Fit the regression model
  model <- lm(MSCI.Russia.Daily.Return ~ MSCI.World.Daily.Return, data = window_data)
  
  # Calculate R-squared
  rsquared <- summary(model)$r.squared
  synch <- log(rsquared/(1-rsquared))
  
  # Store R-squared value
  rsquared_values <- c(rsquared_values, rsquared)
  Synchronicity.Russia.World <- c(Synchronicity.Russia.World, synch)
}

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# Initialize an empty vector to store R-squared values
rsquared_values_kor <- numeric()
Synchronicity.Korea.World <- numeric()

# Number of observations in your dataset
n_obs <- nrow(df)

# Loop through the dataset
for (i in 262:(n_obs - 1)) {
  # Subset the data for the current window
  window_data <- df[(i - 261):i, ]
  
  # Fit the regression model
  model <- lm(MSCI.Korea.Daily.Return ~ MSCI.World.Daily.Return, data = window_data)
  
  # Calculate R-squared
  rsquared <- summary(model)$r.squared
  synch <- log(rsquared/(1-rsquared))
  
  # Store R-squared value
  rsquared_values_kor <- c(rsquared_values_kor, rsquared)
  Synchronicity.Korea.World <- c(Synchronicity.Korea.World, synch)
}

# Initialize an empty vector to store R-squared values
rsquared_values_br <- numeric()
Synchronicity_BRIC <- numeric()

# Number of observations in your dataset
n_obs <- nrow(df)

# Loop through the dataset
for (i in 262:(n_obs - 1)) {
  # Subset the data for the current window
  window_data <- df[(i - 261):i, ]
  
  # Fit the regression model
  model <- lm(MSCI.Russia.Daily.Return ~ MSCI.BRIC.Daily.Return, data = window_data)
  
  # Calculate R-squared
  rsquared <- summary(model)$r.squared
  synch <- log(rsquared/(1-rsquared))
  
  # Store R-squared value
  rsquared_values_br <- c(rsquared_values_br, rsquared)
  Synchronicity.Russia.BRIC <- c(Synchronicity_BRIC, synch)
}

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# Initialize an empty vector to store R-squared values
rsquared_values_em <- numeric()
Synchronicity_EM <- numeric()

# Number of observations in your dataset
n_obs <- nrow(df)

# Loop through the dataset
for (i in 262:(n_obs - 1)) {
  # Subset the data for the current window
  window_data <- df[(i - 261):i, ]
  
  # Fit the regression model
  model <- lm(MSCI.Russia.Daily.Return ~ MSCI.Emerging.Markets.Daily.Return, data = window_data)
  
  # Calculate R-squared
  rsquared <- summary(model)$r.squared
  synch <- log(rsquared/(1-rsquared))
  
  # Store R-squared value
  rsquared_values_em <- c(rsquared_values_em, rsquared)
  Synchronicity.Russia.EM <- c(Synchronicity_EM, synch)
}

## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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## Warning in summary.lm(model): essentially perfect fit: summary may be
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df_synch <- data.frame(Synchronicity.Russia.World, Synchronicity.Korea.World, Synchronicity.Russia.BRIC, Synchronicity.Russia.EM)

date <- data.frame(df$Date)
date$number <- row.names(date)
df_synch$number <- row.names(df_synch)
finaldata <- merge(date, df_synch, by = "number", all = TRUE)
finaldata <- finaldata[order(finaldata$df.Date) , ]

library(plotly)
library(TSstudio)
ts_plot(finaldata,
        title = "Synchronicity",
        Xtitle = "Time",
        Ytitle = "%",
       )