This post is my first collection of notes from following CS324 at Stanford. I will be working through this course on my own to better understand LLM’s
Keywords:
LLM’s, ML, NLP
Machine Learning:
LLMs
Date
May 31 2023
Solutions to Chapter 2 of “Machine Learning in Finance” by Dixon, Halperin, and Bilokon. All problems worked out in full detail. Hopefully useful for fellow self-studiers.
Keywords:
ML in Finance Book, Chapter, Solutions
QuantFin
Machine Learning in Finance
Date
April 08 2023
Risk Neutrality is a core concept to understanding option pricing theory. I present a novel interpretation of Risk Neutrality that tries to link ideas from economics such as utility to more probabilistic interpretations of decision making under uncertainity and finally to ideas of continuous time finance
Keywords:
Risk Neutrality, Utility, Time
QuantFin
Risk Neutrality
Date
Feb 13 2023
A Neural Differential Equation (NDE) is a differential equation that is specified through a Neural Network. The potential theoretical links between two seemingly disparate fields is fascinating. In this post I begin to explore the properties and potential connections between NDEs, Timeseries, SDEs, and Probability Measures
Keywords:
Neural Differential Equations, Machine Learning
Machine Learning:
NDEs
Date
Feb 06 2023
A strong understanding of Quadratic Variation can help us make sense of different option pricing models and their properties. This post is an introductory discussion of Quadratic Variation
Keywords:
Quadratic Variation, Replication, Black-Scholes, Option Pricing Basics
Quant Finance:
Option Pricing Basics
Date
Jan 23 2023