Dr Kihun Nam, Monash University
Recent advances in machine learning have enabled the use of novel numerical techniques in solving challenging problems in financial mathematics. This course will introduce the basics of stochastic calculus and machine learning, establish connections between probabilistic and PDE formulations of stochastic models, and demonstrate how all these elements can be combined to solve financial mathematics problems such as derivative pricing and portfolio selection.
Week 1: Brownian Motion and Stochastic Calculus
Week 2: Derivative Pricing and Monte Carlo Method
Week 3: Neural Networks and Stochastic Gradient Descent
Week 4: Derivative Pricing with Deep Learning
Check back for pre-enrolment QUIZ details so you can self-evaluate and get a measure of the key foundational knowledge required.
Dr Kihun Nam is a lecturer studying financial mathematics at Monash University, specialising in backward stochastic differential equations (BSDEs) in stochastic optimization and stochastic differential games. His expertise lies in the stability analysis of BSDE solutions with respect to underlying noise, employing advanced techniques rooted in high-dimensional analysis. Recent works of Dr. Nam explores deep connections between BSDEs and parabolic or elliptic PDEs. Through both teaching and research, he advances the integration of machine learning and stochastic analysis.