Enroll Course: https://www.coursera.org/learn/financial-engineering-computationalmethods
In the dynamic world of finance, accurate pricing and robust model calibration are paramount. Coursera’s “Computational Methods in Pricing and Model Calibration” course offers a deep dive into these critical areas, providing a comprehensive understanding of both theoretical underpinnings and practical applications.
This course is structured to guide learners through the complexities of financial product pricing, with a particular emphasis on options and interest rate instruments. The initial module sets a strong foundation by introducing various types of market options. It then seamlessly transitions into numerical techniques essential for their pricing, highlighting the power of Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. The curriculum doesn’t shy away from the mathematical models that drive stock price evolution, dedicating significant attention to the Black-Merton-Scholes (BMS), Heston, and Variance Gamma (VG) models. The practical inclusion of Python code for these techniques is a significant advantage, allowing learners to immediately apply theoretical concepts.
Following the pricing of options, the course delves into the crucial aspect of model calibration. This module tackles how to select appropriate models and parameters by fitting them to market data. It covers essential concepts like bid and ask prices, implied volatility, and the practicalities of calibration recipes, including objective functions and initial parameter sets. The exploration of optimization routines such as brute-force search, Nelder-Mead, and BFGS algorithms, again accompanied by Python code, empowers students to perform calibration in real-world scenarios.
The latter half of the course shifts focus to interest rates and their instruments. Part I introduces fundamental interest rate concepts, including forward rates, spot rates, swap rates, and term structures. It demonstrates how to calibrate LIBOR and swap curves and use them for pricing bonds and swaps, reinforcing learning with practical Python examples. Part II builds upon this by exploring more complex stochastic models like the Vasicek and CIR models for bond pricing. It showcases regression techniques for fitting these models to market data, providing invaluable insights for market makers and speculators alike.
Overall, “Computational Methods in Pricing and Model Calibration” is an exceptional course for anyone looking to enhance their quantitative finance skills. The blend of theoretical depth, practical implementation through Python, and coverage of essential financial models makes it a highly recommended resource for aspiring quants, traders, and financial analysts.
Enroll Course: https://www.coursera.org/learn/financial-engineering-computationalmethods