By Gianluca Fusai
This booklet offers and develops significant numerical equipment presently used for fixing difficulties bobbing up in quantitative finance. Our presentation splits into parts.Part I is methodological, and gives a entire toolkit on numerical tools and algorithms. This contains Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula services, transform-based tools and quadrature techniques.Part II is sensible, and contours a couple of self-contained instances. every one case introduces a concrete challenge and provides a close, step by step resolution. machine code that implements the circumstances and the ensuing output can also be included.The circumstances surround a large choice of quantitative matters bobbing up in markets for fairness, rates of interest, credits chance, power and unique derivatives. The corresponding difficulties hide version simulation, by-product valuation, dynamic hedging, portfolio choice, probability administration, statistical estimation and version calibration.
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Additional info for Implementing Models in Quantitative Finance - Methods and Cases
2 Broadly speaking, the Lebesgue measure on a given space is defined as a sigma-additive measure which is invariant up to rotations and translations of subsets in the space. To illustrate this property, we may think of the area of a given square in the plane. This number does not change if the square is translated and rotated. The Lebesgue measure on the real line (resp. plane; resp. 3D space) measures the length (resp. area; resp. volume) of a set. A definition given in Chapter 1 states that the density function of a probability measure is the Radon–Nikodym derivative of its cumulative distribution function with respect to the Lebesgue measure.
13) to the function ψ : (y1 , y2 ) → (x1 , x2 ) defined above. As the Jacobian of ψ is J = 1/2, we have: fX1 ,X2 (x1 , x2 ) = 1 1 −x1 /2 1 1 e × = e−x1 /2 × 2π 2 2 2π for x1 ∈ (0, ∞) and x2 ∈ (0, 2π). By integrating over appropriate domains, we can compute the marginals of X1 and X2 . We easily read that X1 is exponential with rate 1/2 and X2 is uniformly distributed on the interval [0, 2π]. d. are independent standard normals. Furthermore, if U1 , U2 ∼ U[0, 1], then −2 ln U1 is exponential with rate 1/2 and 2πU2 is uniform on [0, 2π].
Example (Absolute normal distribution) The exponential density is gE (1) (x) := e−x 1R+ (x). The absolute value of a standard normal N (0, 1) has density: f|N (0,1)| (x) := d d P(|Z| ≤ x) = dx dx x 2 2 fN (0,1) (x) dx = √ e−x /2 . 2π −x An upper bound for the ratio between f|N (0,1)| and gE (1) is given by f|N (0,1)| (x) 2 2 = √ e−x /2+x = fExp (x) 2π 2e −(x−1)2 /2 e ≤ π 2e =: C. π The method provides a sample from the absolute value of a normal by sampling from the exponential distribution. f.