This project contains 4 sections.
Section A)
Let X be a d-dimensional random vector expressed as column vector. Without loss of generality, assume X has zero empirical mean. We want to find a d-by-d orthonormal transformation matrix P such that
Y = PTX
With the constraint that
Cov(Y) is a diagonal matrix and P-1 = PT
Section B)
Let S(t) be a positive stochastic process satisfying
dS(t) = μ(t)S(t)dt + σ(t)S(t)dW(t)
Where μ(t), σ(t) are processes adapted to filteration F(t) for t ≥ 0 associated with the Brownian motion W(t). Compute d {log(S(t))} to show that -
S(t) = S(0)exp{0∫t(μ(s) - (σ2(s)/2)ds + σ(s)dW(s)}
Section C)
Let S(t) be a positive stochastic process satisfying
dS(t) = μ(t)S(t)dt + σ(t)S(t)dW(t)
Where µ(t), σ(t) are processes adapted to filteration F(t) for t ≥ 0 associated with the Brownian motion W(t). Compute d{(s(t))P} for p > 0
Section D)
Write a Matlab or R function to compute the value of a convertible bond. Please use two different models to price convertible bonds. Explain the difference and sources of optionality of the two models.