GHAR.model: Compute the GHAR model for realized covariances and returns...

In deanfantazzini/bitcoinFinance: Quantitative Finance with Cryptocurrencies

Compute the GHAR model for realized covariances and returns the 1-day-ahead VaR forecasts and the 1-day-ahead Realized Covariance forecast.

Description

This function computes the GHAR model for realized covariances and returns the 1-day ahead VaR forecasts (for an equally-weighted portfolio), as well as the the 1-day-ahead Realized Covariance forecast.

Usage

GHAR.model(cleaned.rc, daily_ret, weights, quantiles = c(0.05, 0.1))

Arguments

cleaned.rc	is a list containing the realized covariances
daily_ret	is an xts object containing the cryptocurrency daily returns. It should be compatible with the list containing the realized covariances
weights	is a vector of weights for the porfolio VaR
quantiles	the desidered quantiles for the VaR estimates

Details

This function computes the GHAR model for realized covariances by Cech and Baruník (2017) and returns the 1-day ahead #' VaR forecasts (for an equally-weighted portfolio - default case), the 1-day-ahead Realized Covariance forecast, and the GHAR.fit object computed with the systemfit package

Value

results a list containing the 1-day-ahead VaR forecasts, 1-day-ahead Realized Covariance forecast, and the GHAR.fit object computed with the systemfit package

References

Cech, Frantisek, and Jozef Baruník (2017). On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model. Journal of Forecasting 36(2), 181-206.

Examples

## Not run: 
library(highfrequency)
localbtcMXN <-bitcoincharts_single_download(name = "localbtcMXN.csv.gz")
btcdeEUR <-bitcoincharts_single_download(name = "btcdeEUR.csv.gz")
krakenEUR <-bitcoincharts_single_download(name = "krakenEUR.csv.gz")
localbtcINR <-bitcoincharts_single_download(name = "localbtcINR.csv.gz")
coinbaseUSD <-bitcoincharts_single_download(name = "coinbaseUSD.csv.gz")
data5<-list(localbtcMXN =localbtcMXN, btcdeEUR=btcdeEUR, krakenEUR=krakenEUR,
             localbtcINR=localbtcINR, coinbaseUSD=coinbaseUSD)
data_clean<-aggregate_merge_bictoincharts_data(data_list=data5)
cleaned.rc <- rc_pd(data_clean$price_ts)
GHAR_fit <- GHAR.model(cleaned.rc, data_clean$daily_returns, weights =rep(0.2,5))

## End(Not run)

deanfantazzini/bitcoinFinance documentation built on June 12, 2024, 4:10 p.m.