The Black–Litterman model is one of the most popular models in quantitative finance, with
numerous theoretical and practical achievements. From the standpoint of investment theory …

The authors present a machine learning approach to regime-based asset allocation. The
framework consists of two primary components:(1) regime modeling and prediction and (2) …

The complexity of machine learning models presents a substantial barrier to their adoption
for many investors. The algorithms that generate machine learning predictions are …

Financial data science and econometrics are highly complementary. They share an
equivalent research process with the former's intellectual point of departure being statistical …

Its intuitiveness and the simplicity of its calculations make the seminal Black-Scholes-Merton
option pricing model the most commonly known and used among all asset pricing models …

The author introduces a modular machine learning framework for model validation in which
the output from one procedure serves as the input to another procedure within a single …

Calculating true volatility is an essential task for option pricing and risk management. It is
made difficult, however, by market microstructure noise. Particle filtering has been proposed …

Making reliable causal inferences is integral to both explaining past events and forecasting
the future. Although there are various theories of economic causality, there has not yet been …

The scientific analysis of financial data, both for practical and theoretical purposes, has
continuously been an active and evolving area. Traditionally, financial modeling and …

Understanding the causal relations that drive markets is integral to both explaining past
events and predicting future developments. Although there are various theories of economic …