This paper studies the intraday volatility of European government bonds under the framework of the multiplicative component GARCH model (Engle and Sokalska, 2012). Intraday return volatility is specified as the product of daily volatility, intraday seasonality, and a unit GARCH process. The...

This study compares the efficacy of Black–Scholes implied volatility (BSIV) with model-free implied volatility (MFIV) in providing volatility forecasts for 13 North American, European, and Asian stock market indexes: S&P 500 (United States), S&P/ASX 200 (Australia), S&P/TSX 60 (Canada), AEX...

Locally explosive behavior is observed in many economic and financial time series when bubbles are formed. We introduce a time-varying parameter model that is capable of describing this behavior in time series data. Our proposed model can be used to predict the emergence, existence and burst of...

Utilizing the recently developed measure of global risk aversion by Xu (2017), we show that global risk aversion is a significant determinant of international equity correlations, consistently across all emerging markets examined. The positive effect of risk aversion on emerging market...

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by an in-depth analysis of topological metrics of correlation networks applied to financial forecasting. While academic research...

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

Measuring the dynamic relationship between banking and industries with systemic importance has attracted much attention after the recent financial crisis. This paper examines the dynamic conditional correlations and volatility spillover using three popular multivariate GARCH models in the...

We improve on our Vigilant Asset Allocation (VAA) by the introduction of a separate “canary” universe for signaling the need for crash protection, using the concept of breadth momentum. The amount of cash is now governed by the number of canary assets with bad (non-positive) momentum. The...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

Using a simple model of equity valuation, we de fine stock market bubbles and anti-bubbles as periods in which the dynamics of valuation is temporarily explosive. We identify a mechanism for the creation and destruction of bubbles and anti-bubbles that depends on the interaction between...