Is Quant’s Life Hard or Easy?

Posted on December 20, 2020 By Harbourfront Technologies

Last month, efinancialcarreeers published a post, stating that quant’s life is getting harder these days. Back in the day, a quant in finance could devise a strategy, sit back and let the money roll in while lounging about in a silk robe with a fat cigar. Such are the halcyon …

Convertible Bond Arbitrage Using the Volatility Surface

Posted on December 14, 2020 By Harbourfront Technologies

Convertible bonds are complex, hybrid securities. In finance, a convertible bond or convertible note or convertible debt (or a convertible debenture if it has a maturity of greater than 10 years) is a type of bond that the holder can convert into a specified number of shares of common stock …

Modern Portfolio Theory-The Efficient Frontier

Posted on December 11, 2020 By Harbourfront Technologies

Modern Portfolio Theory (MPT) is a theory developed by Harry Markowitz in 1952, which later earned him a Nobel Prize in Economics. The theory states that investors can create an ideal portfolio of investments that can provide them with maximum returns while also taking an optimal amount of risk. The …

Trading Performance of an ETF Pair Strategy-Quantitative Trading In Python

Posted on December 6, 2020 By Harbourfront Technologies

In a previous post, we presented statistical tests for the Australia/Canada country ETF pair. Specifically, we calculated the return correlation and performed cointegration tests using a training set consisted of 8 years of data. The high correlation and the fact that the pair spread passed 2 cointegration tests made this …

Modern Portfolio Theory-Portfolio Management in Python

Posted on November 30, 2020 By 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 …

Statistical Analysis of an ETF Pair-Quantitative Trading In Python

Posted on November 28, 2020 By Harbourfront Technologies

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 …

What It Takes to Win at Quantitative Investing

Posted on November 12, 2020 By Harbourfront Technologies

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 …

Forecasting Implied Volatility with ARIMA Model-Volatility Analysis in Python

Posted on October 31, 2020 By Harbourfront Technologies

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 …

FASB Proposes Scope Clarification for Reference Rate Relief

Posted on October 30, 2020 By Harbourfront Technologies

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 …

Forecasting Volatility with GARCH Model-Volatility Analysis in Python

Posted on October 26, 2020 By Harbourfront Technologies

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 …