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Computational Methods in Pricing and Model Calibration(으)로 돌아가기

컬럼비아대학교의 Computational Methods in Pricing and Model Calibration 학습자 리뷰 및 피드백

4.0
별점
10개의 평가

강좌 소개

This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments....
필터링 기준:

Computational Methods in Pricing and Model Calibration의 4개 리뷰 중 1~4

교육 기관: daniel h

2022년 10월 5일

교육 기관: Kostya T

2021년 9월 19일

교육 기관: Murray S

2021년 10월 19일

교육 기관: J

2022년 2월 3일