Advanced Securities Pricing with Monte Carlo Simulations — Securities Pricing There are a variety of methods used to price securities. I would argue that this topic is as much an art as it is a science. This article discusses the advantages of using simulations (mainly in the form of stochastic inputs) for pricing financial instruments. …

Python and Black-Scholes Pricing for Dynamic Hedges — Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details. Option Portfolios Equities that have a…

divitiae et educatione — There are several reasons why one may wish to simulate multiple Brownian motions with an underlying correlation structure. Take for example a stylized fact of the market: the leverage effect…

divitiae et educatione — Implied volatility can be a tricky topic. This article aims to provide an intuitive and clear understanding of what implied volatility is and how to compute it numerically using Python. First, let’s consider the problem space… Suppose we are trading an option contract on an underlying equity with a European…

divitiae et educatione — Welcome to Quant Guild. My name is Roman Paolucci and I am a full-time quantitative researcher and course director here at Quant Guild. Whether you have been following my blog since the start, or are brand new, I want to sincerely thank you for stopping by. Quant Guild is dedicated…

Explanation, Derivation and Applications in Python — Introduction The linear algebra essential to data science, machine learning, and artificial intelligence is often overlooked as most introductory courses fail to display the big picture. Concepts such as eigendecomposition and singular value decomposition (SVD) are incredibly important from a practitioner's standpoint; they are the core of dimensionality reduction techniques including…

A Curated Selection of Books by Topic — This list comprises books that I have found helpful on my journey in quantitative finance. This list is by no means exhaustive, but I can personally vouch for the usefulness of each of these books as I have read them myself. Note: I am in no way affiliated with the…

A Quickstart Guide to Generative Machine Learning with Code — Introduction Generative machine learning can be helpful in a variety of contexts. One, for example, is when some closed-form or quasi-closed-form solution is available but computationally expensive. In this case, a neural network may provide a timely solution with an acceptable degree of accuracy. Nevertheless, to train such a network, data…

With Real Strategies Developed by Quantitative Research — Quantitative Research Backtesting quantitative research prior to implementation in a live trading environment (see Algorithmic Trading with Python or Dynamic Algorithmic Trading Systems) is as essential as it is the fruit of a quantitative researcher’s labor. It’s critical to note that the outcome of a backtest is directly related to the quality…