This paper proposes an Exponential HEAVY (EHEAVY) model. The model specifies the dynamics of returns and realized measures of volatility in an exponential form, which guarantees the positivity of volatility without restrictions on parameters and naturally allows the asymmetric effects. It...

This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast...

This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen, Kinnebrock and Shephard (2010) and Bollerslev, Li, Patton and Quaedvlieg (2020a) to allow for a finer decomposition of realized (co)variances. The new "realized...

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

Value at risk (VaR) and expected shortfall (ES) are two of the most widely used risk measures in economics and finance. In this paper, we use a semiparametric method, together with realized variance measures, to jointly estimate structural models for the two risk measures. The semiparametric...

Innovations in volatility constitute a potentially important asset pricing risk factor that can be tested using the return on variance swaps. We characterize the exposures of returns on equities, bonds and currencies in all regions of the world to U.S. based equity variance risk. We explore...

We introduce the class of affine forward variance (AFV) models of which both the conventional Heston model and the rough Heston model are special cases. We show that AFV models can be characterized by the affine form of their cumulant generating function, which can be obtained as solution of a...

The risk premium of stocks due to priced variance risk is summarized to two variables -- the stock-specific price of variance risk (the difference between realized and option-implied variance) and the quantity (i.e., how stock prices respond to their variance shocks) of variance risk....

We provide a comprehensive treatment for the problem of testing jointly for structural changes in both the regression coefficients and the variance of the errors in a single equation system involving stationary regressors. Our framework is quite general in that we allow for general mixing‐type...

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects...

The study tests whether realised moments of stock returns (mean, variance, skewness and kurtosis) computed from daily returns over the last month, quarter and year can predict the 1-month cross-sectional stock returns of 40 US-traded liquid stocks in the period 1986-2019. The performed...

Statisticians are constantly looking for methods of statistical inference that would be both effective and would require meeting as few assumptions as possible. Permutation tests seem to fit here, as using them makes it possible to perform statistical inference in situations where classical...

This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model the DCC-HEAVY model as key ingredients are the Engle (2002) DCC model and Shephard and Sheppard (2012) HEAVY model. We discuss the models' dynamics and highlight their...

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict...

The distance to default (DD) and the probability of default (PD) are the essential credit risks in the finance world. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations. It is crucial to know which parameter effects more on DD and PD so that...

This paper studies the estimation of high-dimensional minimum variance portfolio (MVP) based on the high frequency returns which can exhibit heteroscedasticity and possibly be contaminated by microstructure noise. Under certain sparsity assumptions on the precision matrix, we propose estimators...

The risk return relationship is analysed in bivariate models for return and realised variance (RV) series. Based on daily time series from 21 international market indices for more than 13 years (January 2000 to February 2013), the empirical findings support the arguments of risk return tradeoff,...

We present a new model to decompose total daily return volatility into a filtered (high-frequency based) open-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to limit the impact of incidental large observations. Applying our...

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of...

We propose a dynamic factor state-space model for high-dimensional covariance matrices of asset returns. It uses observed risk factors and assumes that the latent covariance matrix of assets and factors is observed through their realized covariance matrix with a Wishart measurement density. The...

In this study we provide an analytical characterization of the impact of zero returns on the popular realized covariance estimator of Barndorff-Nielsen and Shephard (2004). In our framework, efficient price processes evolve as a semimartingale with some likelihood of repeated prices. We show...

Significant portfolio variance biases arise when contrasting multi-period portfolio returns based on the assumption of fixed continuously rebalanced portfolio weights as opposed to buy-and-hold weights. Empirical evidence obtained using S&P500 constituents from 2003 to 2011 demonstrates that,...

This paper establishes the first analytical formula for optimal nonlinear shrinkage of large-dimensional covariance matrices. We achieve this by identifying and mathematically exploiting a deep connection between nonlinear shrinkage and nonparametric estimation of the Hilbert transform of the...

This article develops unbiased weighted variance and skewness estimators for overlapping return distributions. These estimators extend the variance estimation methods constructed in Bod et. al. and Lo and MacKinlay. In addition, they may be used in overlapping return variance or skewness ratio...

This paper develops a statistical theory to estimate an unknown factor structure based on financial high-frequency data. We derive an estimator for the number of factors and consistent and asymptotically mixed-normal estimators of the loadings and factors under the assumption of a large number...

Limit spectral theory of sample covariance matrices of increasing dimension was recently used as a base for the development of improved non-degenerating methods of multivariate statistical analysis. We present results of a numerical investigation of fundamental relations of this theory (of the...

This study explores the predictive power of new estimators of the equity variance risk premium and conditional variance for future excess stock market returns, economic activity, and financial instability, both during and after the last global financial crisis. These estimators are obtained from...