The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by [Lopez de Prado, 2016], applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk based allocation methods, HRP is also a function of the estimate of the covariance...

Can the degree of predictability found in the data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R-squares of predictive regressions. Using data on the market and component portfolios, we find that the empirical R-squares are significantly greater...

The importance of solar energy has been growing in recent years. This raises the need for efficient modelling and forecasting methods. The existing methods are predominantly based on weather predictions or forecast solar radiation, which is not easy to convert into production forecast. Instead...

The objective of this paper is to analyze what are the main determinants of the exchange rate risk premium (ERP). The empirical case is conducted for the daily Mexican peso-USD for a simple period from 2007 until 2015. According to the results the ERP is influenced by several financial variables...

Hint: these are not the Fama-French 3 factors and they are not even spanned by the Fama-French 5 factors. More importantly, they feature superior out-of-sample pricing performance compared to standard asset pricing models. What is “common” about these factors? We identify the factor space...

Value-at-Risk (VaR) has emerged as the standard tool for measuring and reporting financial market risk. Currently, more than eighty commercial vendors offer enterprise or trading risk management systems which report VaR-like measures. Risk managers are therefore often left with the daunting task...

We model the conditional distribution of high frequency financial returns by means of a two-component quantile regression model. Using three years of 30-minute returns, we show that the conditional distribution depends on past returns and on the time of the day. Two practical applications...

We present a generalisation of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coefficients for both mean and variance. The model satisfies the empirical evidence of changing memory observed in average temperature series and can provide useful improvements in the...

This paper shows that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, (unconditional) combinations, and hybrid forecasts. Hence, it finds empirical evidence that both, combining...

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...