This article develops a model that takes into account skewness risk in risk parity portfolios. In this framework, asset returns are viewed as stochastic processes with jumps or random variables generated by a Gaussian mixture distribution. This dual representation allows us to show that skewness...

Volatility is usually considered as a synonym for risk. Mainstream financial theory states that higher portfolio volatility is translated into higher expected returns while diversification helps eliminate idiosyncratic risks. This leaves us with an apparent anomaly as low-risk (low-beta) stocks...

In this article, we show how to take into account skewness risk in portfolio allocation. Until recently, this issue has been seen as a purely statistical problem, since skewness corresponds to the third statistical moment of a probability distribution. However, in finance, the concept of...

Classical quantitative finance models such as the Geometric Brownian Motion or its later extensions such as local or stochastic volatility models do not make sense when seen from a physics-based perspective, as they are all equivalent to a negative mass oscillator with a noise. This paper...

In this paper, we try to identify the relationship between the ESG scores and stocks' performance and risk measures. Using the ESG database of MSCI, we split the global investment universe into three regions: Europe, North America and Asia-Pacific. The investment universe of each region is...

The aim of this paper is to present the method for estimating the cost of capital of typical portfolios available on the Warsaw Stock Exchange. The authors introduce the three factor Fama-French model and its two modifications. They also apply the bootstrap method to evaluate the variability of...

This paper suggests a solution to what has become known as the "private equity premium puzzle" (Moskowitz and Vissing-Jorgensen (2002)). We interpret occupational choice as a dynamic portfolio choice problem of a life-cycle investor facing a liquidity constraint and imperfect information about...

We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

We show analytically under quite general conditions that implied rates of return based on analysts' earnings forecasts are only a downward biased estimator for future expected one-period returns and therefore not suited for computing market risk premia. The extent of this bias is substantial as...

The most relevant practical impediment to an application of the Markowitz portfolio selection approach is the problem of estimating return moments, in particular return expectations. We analyze the consequences of using return estimates implied by analysts' dividend forecasts under the explicit...