Author: Harbourfront Technologies

Harry M. Markowitz is the founder of Modern Portfolio Theory (MPT) which originated from his 1952 essay on portfolio selection. He was later awarded a Nobel Prize in Economics. His work founded the concept of an efficient frontier, and it allows for the determination of portfolio mixes that provide an optimal return for the least…

Read More Modern Portfolio Theory-Portfolio Management in Python

Pair trading, or statistical arbitrage, is one of the oldest forms of quantitative trading. In this post, we are going to present some relevant statistical tests for analyzing the Australia/Canada pair. We chose this pair because these countries’ economies are tied strongly to the commodity sector, therefore they share similar characteristics and could be a…

Read More Statistical Analysis of an ETF Pair-Quantitative Trading In Python

A recent podcast on Bloomberg offers some interesting perspectives on quantitative investing. Interest in quantitative investing strategies continues to grow; however, as the space gets more competitive, making money and winning gets harder and harder. Computation costs alone can be prohibitive. On the latest episode, we speak with Columbia Business School professor Ciamac Moallemi about…

Read More What It Takes to Win at Quantitative Investing

In a previous post, we presented theory and a practical example of calculating implied volatility for a given stock option. In this post, we are going to implement a model for forecasting the implied volatility. Specifically, we are going to use the Autoregressive Integrated Moving Average (ARIMA) model to forecast the volatility index, VIX. In…

Read More Forecasting Implied Volatility with ARIMA Model-Volatility Analysis in Python

On July 27, 2017 the Financial Conduct Authority, the U.K.’s top regulator, tasked with overseeing Libor, announced the benchmark will be phased out by 2021. Alternative risk-free rates are being set up for the different currencies. For the US Dollar, the US Fed’s Alternative Reference Rates Committee (ARRC) has recommended using the Secured Overnight Financing…

Read More FASB Proposes Scope Clarification for Reference Rate Relief

In a previous post, we presented an example of volatility analysis using Close-to-Close historical volatility. In this post, we are going to use the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to forecast volatility. In econometrics, the autoregressive conditional heteroscedasticity (ARCH) model is a statistical model for time series data that describes the variance of the…

Read More Forecasting Volatility with GARCH Model-Volatility Analysis in Python

Volatility measures market expectations regarding how the price of an underlying asset is expected to move in the future. There are two types of volatility: historical volatility and implied volatility. In a series of previous posts, we presented methods and provided Python programs for calculating historical volatilities. In this post, we are going to discuss…

Read More Implied Volatility of Options-Volatility Analysis in Python

Last month, institutionalinvestor.com reported that AI-powered hedge funds outperformed their peers, Hedge funds with artificial intelligence capabilities showed a huge competitive edge over investors that didn’t use AI, new research indicates. AI-led hedge funds produced cumulative returns of 34 percent in the three years through May, a report Tuesday from consulting and research firm Cerulli…

Read More Are AI-Powered Hedge Funds Outperforming?

In a previous post, we presented a method for calculating a stock beta and implemented it in Python. In this follow-up post, we are going to implement the calculation in Excel. We continue to use Facebook as an example. Recall that, In finance, the beta (market beta or beta coefficient) is a measure of how…

Read More How to Calculate Stock Beta in Excel-Replicating Yahoo Stock Beta