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When you are in 101 of risk management is usual to confuse Bar, VAR and VaR, the first one refers to a place that you should buy (it is a bad idea, do not do it), the second is Vector Autoregressive and the last one Value at Risk, our matter.

## What is Value at Risk?

In simple words, it is the maximum possible loss of your money in a period and the formal definition is:

VaR is a method of assessing risk that uses standard statistical techniques used routinely in other technical fields. Loosely, VAR summarizes the worst loss over a target horizon that will not be exceeded with a given level of confidence.

(Jorion, 2007)

## How to compute?

There are two methods to compute

• Parametric
• No Parametric

The parametric method assumes that the returns follow a normal distribution, hence the VaR of a portfolio is:

F         Factor of confidence level

S          Total amount of the investment

σ          Volatility

## RCode

library(pacman) p_load(tidyverse,tidyquant)

Import dataset Technical Trading Rule Composite Data

data(ttrc) data <- as_tibble(ttrc)

Select Close prices

close <- data %>% select(Close)

Parametric VaR function

VaRP <- function(x,p,s,t){ x1 <- as.numeric(unlist(x)) r1 <- (x1/lag(x1))-1 r1 <- as.numeric(unlist(r1)) sig<- sd(r1,na.rm = T) p <- as.numeric(qnorm(p)) h <- sqrt(t/252) pvar <- sig*p*h*s print(paste("The VaR is $",prettyNum(pvar,big.mark = ","))) } Arguments x Object that contains close prices p Percent confidence level s Total amount of the investment t Time horizon (days) ## Example Using the close prices of ttrc , we want to know the VaR of$ 100 million, with a holding period of 7 days and 99 confidence level

VaRP(close,.99,100000000,7)

 "The VaR is \$ 552,757"

There is a simple way to compute parametric VaR. I know you can improve it. In future posts I am going to show how to compute with no parametric methods.

Reference

De Lara, A. (2018) Medición y control de riesgos financieros. México: Limusa

Jorion, P. (2007). Value at Risk. McGraw Hill.