This paper is inspired by medical studies in which the same patients with multiple sclerosis are examined at several successive visits (doctor's appointments) and described by fractional anisotropy tract profiles, which can be represented as f unctions. Since the observations for each patient...

This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio...

This paper examines optimal portfolio selection using quantile-based risk measures such as Valueat-Risk (VaR) and Conditional Value-at-Risk (CVaR). We address the case of a singular covariance matrix of asset returns, which leads to an optimization problem with infinitely many solutions. An...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...

Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types....

We examine two major topics in the field of cryptocurrencies. On the one hand, we investigate possible long-run equilibrium relationships among ten major cryptocurrencies by applying two different cointegration tests. This analysis aims at constructing cointegrated portfolios that enable...

This study examines the ways in which firms recover from stagnation or sales decline, with a focus on two key aspects: traditional high‑growth companies and growth restarts within the framework of organizational life cycle theory. Analyzing a dataset of 1883 Russian firms from 2013 to 2021,...

In this paper, we study hierarchical Bayes (HB) estimators based on different priors for small area estimation. In particular, we use inverse gamma and flat priors for variance components in the HB small area models of You and Chapman (2006) and You (2021). We evaluate the HB estimators through...

This study proposes a novel multivariate neural network model, coined as Realized Covariance Matrix NBEATSx (RCM-NBEATSx), for forecasting realized covariance matrices (RCMs) of financial securities. RCM-NBEATSx ensures that the predicted RCMs are positive semidefinite through the employment of...

In this study, we examine the nexus between oil tail risks and the realized variance of consumer prices in six advanced economies, namely, Canada, France, Germany, Japan, the United Kingdom, and the United States. Importantly, we estimate the oil tail risks following the Conditional...

The aim of this study is to analyze different forecasting approaches for the variance of future earnings, compare the respective forecast accuracy and test whether the forecasted information are relevant to equity or debt markets. The results, in line with former research, indicate that...

Covariates selection is essential when faced with many variables in modern causal inference in a data-rich environment. Particularly, the efficiency of the average causal effect (ACE) can be improved by including covariates only related to the outcome and reduced by including covariates related...

This note provides closed-form expressions for spatial Greeks (Delta and Gamma) for discretely monitored realized variance swaps under several common parametric model assumptions. We derive closed-form results for stochastic volatility and exponential L´evy models, as well as some...

In this paper, we perform a comprehensive study of different covariance and precision matrix estimation methods in the context of minimum variance portfolio allocation. The set of models studied by us can be broadly categorized as: Gaussian Graphical Model (GGM) based methods, Shrinkage Methods,...

Certificates are structured financial instruments that aim to provide investors with investment solutions tailored to their needs. Certificates can be modeled using a bond component and a derivative component, typically an options strategy. The pricing of certificates is typically performed...

The main problem in pricing variance, volatility, and correlation swaps is how to determine the evolution of the stochastic processes for the underlying assets and their volatilities. Thus, sometimes it is simpler to consider pricing of swaps by so-called pseudo-statistics, namely, the...

We study conditional correlations between pairs of risks in normal variance mixture models, which are widely used in risk management and finance. In particular, we examine up- and down-correlations defined as the conditional correlation between the sum of risks and an individual component,...

Graphical models have demonstrated exceptional performance in uncovering the conditional dependence structure among a given set of variables. This paper introduces two novel graphical modeling techniques: Gslope and Tslope, which use the Sorted L1-Penalized Estimator (Slope) to directly estimate...

I propose an exact finite sample test of the risk reduction of the global minimum variance (GMV) portfolio. The GMV test statistic is proportional to the reduction in the variance of the GMV portfolio and has a straightforward geometric and portfolio interpretation and complements the celebrated...

The main contribution of this paper is the derivation of the asymptotic behaviour of the out-of-sample variance, relative loss, and of their empirical counterparts in the high-dimensional setting. The results are obtained for the traditional estimator of the global minimum variance portfolio,...

In this paper, we investigate the calibration of the smoothing parameter in an exponentially weighted moving average (EWMA) for realized covariance matrices. Although it is the crucial determinant in steering the dynamics of the EWMA, little attention is drawn on its calibration in many...

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...

Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constraint. To fill this gap, we review the...

Zusammenfassung In diesem Beitrag werden Arbeitsanforderungen, Ressourcen und Gesundheit von Basisarbeitenden in den Dienstleistungssektoren Handel, Finanzwesen und öffentliche Dienstleistungen untersucht. Als Datengrundlage dient die aktuelle Erhebung der BIBB/BAuA-Erwerbstätigenbefragung...

Taking a simplified approach to statistics, this textbook teaches students the skills required to conduct and understand quantitative research. It provides basic mathematical instruction without compromising on analytical rigor, covering the essentials of research design; descriptive statistics;...