Quantocracy’s Daily Wrap for 08/24/2020

Risk-neutral probability distributions (RND) are used to compute the fair value of an asset as a discounted conditional expectation of its future payoff. In 1978, Breeden and Litzenberger presented a method to derive this distribution for an underlying asset from observable option prices [1]. The derivation of the relationship is well presented in A Simple and Reliable Way to Compute Option-Based

The world has unquestionably be sent on a wild ride in 2020. We entered the year full of optimism and hope. Markets were at or near all-time highs, unemployment was low, living on easy street was good. Then the impact of COVID-19 ripped through the market and the economy with enough force to make the winds of even a double hurricane green with envy. This massive and rapid readjustment of the

In the previous article on the topic of artificial neural networks we introduced the concept of the perceptron. We demonstrated that the perceptron was capable of classifying input data via a linear decision boundary. However we postponed a discussion on how to calculate the parameters that govern this linear decision boundary. Determining these parameters by means of 'training' the

The Value factor is often explained as representing a risk premium or a behavioral bias However, financial analysts regard cheap stocks as less risky than expensive ones Data shows that expensive stocks were riskier than cheap ones, which challenges the risk premium theory INTRODUCTION Which of the following two portfolios comprised of US stocks would you consider riskier? Portfolio A: Amazon,

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical restrictions on unexpected changes to this multivariate linear model allow identifying economically interpretable shocks. In particular, one can distinguish news on growth, monetary policy, common risk premia and