Sovereign bond return prediction with realized higher moments

Abstract

This paper analyzes whether realized higher moments are able to predict out-ofsample sovereign bond returns using high-frequency data from the European bond market. We study bond return predictability over tranquil and crisis periods and across core and periphery markets at the index and country level. We provide fresh evidence that realized kurtosis is the dominant predictor of subsequent returns among higher moments and other predictors such as CDS spreads, short-term interest rates and implied stock market volatility. Our findings further underline that sovereign bond return predictability is stronger during crisis periods and more pronounced for bonds of lower credit ratings.

Geometric and arithmetic realized comoments

“…Mei et al (2017) show realized third and fourth moments are related to future volatility. Kinateder and Papavassiliou (2019) show that realized fourth moment can predict sovereign bond returns during a crisis. Ahmed and Al Mafrachi (2021) show that realized moments up to the fifth-order can explain cryptocurrency returns.…”

“…Kraus and Litzenberger (1976) and Dittmar (2002) demonstrate the relationship between higher-order moments and expected returns, and the concept of the realized variance has been extended to realized higher-order moments. In many studies, including those of Amaya et al (2015), Sim (2016), Kim (2016), Mei et al (2017), Kinateder and Papavassiliou (2019), and Ahmed and Al Mafrachi (2021) [1], a realized kth order moment is defined as a sum of kth orders of sub-periodical returns. However, according to Amaya et al (2015) and Bae and Lee (2021), these conventional realized higher-order moments can reflect neither the volatility of volatility nor cross-period relation among sub-periodical returns and are, therefore, flawed.…”

Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems

“…Concerning the use of moments in forecasting models, some authors consider the lowest moments of the distributions (the second in our case, i.e., the variance) to be more efficient than the higher moments (the third, skewness, and the fourth, kurtosis), which are attested to be less stable and reliable (see, e.g., [11], [12], [13], and [14]).…”

“…• We also compare the distributions in (14) with a naive benchmark error E B given by the errors made without the use of rational expectations, as follows:…”

Developing a crisis model based on higher-order moments

“…This article analyzes the causal relationship not only between earnings and the overall variance of the gold and oil markets, but also jumps of volatility, skewness, and kurtosis. Higher-order moments are also used to predict the return on government bondsthis is the subject of the research [21]. This paper analyzes whether realized higher moments can predict sovereign bond return out of the sample using high-frequency data from the European bond market.…”