R and Julia Chapter 6. Analytical Value-at-Risk for Options and Bonds

Chapter 6. Analytical Value-at-Risk for Options and Bonds

R and Julia

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Listing 6.1/6.2
Black-Scholes function in R
bs = function(X, P, r, sigma, T){
	d1 = (log(P/X) + (r + 0.5*sigma^2)*(T))/(sigma*sqrt(T))
	d2 = d1 - sigma*sqrt(T)
	Call = P*pnorm(d1,mean=0,sd=1)-X*exp(-r*(T))*pnorm(d2,mean=0,sd=1)
	Put = X*exp(-r*(T))*pnorm(-d2,mean=0,sd=1)-P*pnorm(-d1,mean=0,sd=1)
	Delta.Call = pnorm(d1, mean = 0, sd = 1) 
	Delta.Put = Delta.Call - 1
	Gamma = dnorm(d1, mean = 0, sd = 1)/(P*sigma*sqrt(T))
	return(list(Call=Call,Put=Put,Delta.Call=Delta.Call,Delta.Put=Delta.Put,Gamma=Gamma))
}
Listing 6.1/6.2
Black-Scholes function in Julia
using Distributions;
function bs(; X = 1, P = 1, r = 0.05, sigma = 1, T = 1)
    d1 = (log.(P/X) .+ (r .+ 0.5 .* sigma.^2).*T)./(sigma .* sqrt.(T))
    d2 = d1 .- sigma * sqrt.(T)
    Call = P .* cdf.(Normal(0,1), d1) .- X .* exp.(-r * T) .* cdf.(Normal(0,1), d2)
    Put = X .* exp(-r .* T) .* cdf.(Normal(0,1),-d2) .- P .* cdf.(Normal(0,1), -d1)
    Delta_Call = cdf.(Normal(0,1), d1)
    Delta_Put = Delta_Call .- 1
    Gamma = pdf.(Normal(0,1), d1) ./ (P .* sigma .* sqrt(T))
    return Dict("Call" => Call, "Put" => Put, "Delta_Call" => Delta_Call, "Delta_Put" => Delta_Put, "Gamma" => Gamma)
end
Listing 6.3/6.4
Black-Scholes in R
f = bs(X = 90, P = 100, r = 0.05, sigma = 0.2, T = 0.5)
print(f)
Listing 6.3/6.4
Black-Scholes in Julia
f = bs(X = 90, P = 100, r = 0.05, sigma = 0.2, T = 0.5)
Financial Risk Forecasting
Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.
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