The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Since 2009, stock markets have resided in a long bull market regime. Passive investment strategies have succeeded during this low-volatility growth period. From 2018 on, however, there was a transition into a more volatile market environment interspersed by corrections increasing in amplitude...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

We investigate the feedback effect of option hedging activity on the stability of the price of the underlying. While previous literature has focused on the effect of hedging activity on the volatility of the underlying, this paper focuses on directional instabilities arising from feedback...

We propose a model-free method for measuring the jump skewness risk premium via a tradingstrategy. We find that in the S&P 500 option market, the premium is positive and greater inabsolute terms than the variance premium. The trading strategy allows for examining the premiumin different holding...

Nominal yields can be expressed as the sum of an expectation, term premium, and convexity component, and in turn of their real and inflation counterparts. We extract these terms from the yield curve of the U.S., Euro Area, U.K., and Japan using a term structure model that explicitly captures the...

We incorporate a latent stochastic volatility factor and macroeconomic expectations in an affine model for the term structure of nominal and real rates. We estimate the model over 1999-2016 on U.S. data for nominal and TIPS yields, the realized and implied volatility of T-bonds and survey...

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market...