Monte Carlo simulation
Quantized calibration in local volatility
Pricing of a derivative should be fast and accurate, otherwise it cannot be calibrated efficiently. Here, Giorgia Callegaro, Lucio Fiorin and Martino Grasselli apply a fast quantization methodology, in a local volatility context, to the pricing of…
Two measures for the price of one
Simulating exposures under the real-world measure followed by repricing under the risk-neutral measure is computationally intensive and dangerous shortcuts are often taken. Here, Harvey Stein combines the real-world measure with the risk-neutral measure…
Cutting edge introduction: The only way is backward
Calculating exposures of products with multiple exercise dates is cumbersome, and deal-dependent. Quants at Numerix have developed an algorithm that streamlines this process and can also help reduce operational risk. Nazneen Sherif introduces this month…
Backward induction for future values
Here, Alexandre Antonov, Serguei Issakov and Serguei Mechkov generalise the American Monte Carlo method to efficiently calculate future values (or exposures) of derivatives using an arbitrage-free model. The resulting algorithm is especially attractive…
Pricing American-style options by Monte Carlo simulation: alternatives to ordinary least squares
The authors investigate the performance of the ordinary least squares (OLS) regression method in Monte Carlo simulation algorithms for pricing American options.
Risk evaluation of mortgage-loan portfolios in a low interest rate environment
Volume 16, Issue 5 (2014)
SABR symmetry
Typical implementations of the stochastic alpha beta rho model involve asymptotic expansion approximations, which can generate inaccurate prices for long-dated options. But directly solving a pricing partial differential equation incurs high…