In the paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and advises a suboptimal portfolio for...

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on...

A considerable theoretical and empirical literature studies the corporation's capital structure. Economists have paid less attention to capital structure in other enterprise forms such as partnerships, which typically operate under different legal constraints and appeal to smaller enterprises....

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on...

We propose a new methodology for abnormal return detection and correction, and evaluate the economic impacts of outliers on asset allocations with higher-order moments (Cf. Jurczenko et al., 2008). Indeed, extreme returns and outliers greatly affect empirical higher-order moment estimations (Cf....

We propose an extension of standard asymmetric volatility models in the generalized autoregressive conditional heteroskedasticity (GARCH) class that admits conditional non-Gaussianities in a tractable fashion. Our "bad environment-good environment" (BEGE) model utilizes two gamma-distributed...

To study the characteristics-sorted factor model in asset pricing, we develop a bottom-up approach with state-of-the-art deep learning optimization. With an economic objective to minimize pricing errors, we train a non-reduced-form neural network using firm characteristics [inputs], and generate...

This paper combines asset pricing theory with deep learning for pricing the cross section of corporate bonds. The proposed deep learning model can flexibly introduce the well-established factors and provide us with deep factors that are not subsumed in those existing factors. The deep factors...

This paper introduces the class of volatility modulated Lévy-driven Volterra (VMLV) processes and their important subclass of Lévy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot...

This paper proposes a new information share for price discovery based on Russell's (1999) autoregressive conditional intensity model. While previous studies rely on equally spaced high frequency data, we use the information conveyed by trade intensities to determine a market's contribution to...