Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to forecast correlations exploiting both estimated realized correlation matrices and exogenous factors. The Fisher-z transformation guarantees robustness of correlation estimators under elliptically constrained departures from normality. For the purpose of performance comparison we contrast our methodology with prominent parametric and nonparametric alternatives to correlation modeling. Based on economic performance criteria, we distinguish dynamic factor models as having the highest predictive content. --Correlation forecasting,Epps effect,Fourier method,Dynamic panel model,Dynamic factor model

The conditional autoregressive wishart model for multivariate stock market volatility

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex dynamic interdependence between the variances and covariances of assets. In addition, it accounts for symmetry and positive definiteness of covariance matrices without imposing parametric restrictions, and can easily be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to time series of daily realized variances and covariances for five New York Stock Exchange (NYSE) stocks. --Component volatility models,Covariance matrix,Mixed data sampling,Observation-driven models,Realized volatility

General uncertainty in portfolio selection: a case-based decision approach

Often a portfolio investor can hardly imagine all states of nature relevant to his investment problem, causing general uncertainty concerning an asset allocation model. We quantify general uncertainty as the weakness of an investor’s belief in a conventional portfolio procedure, then we develop the case-based decision-making approach for determining the optimal belief degree. The economic effect of the proposed case-based methodology is investigated in the empirical study. The empirical results suggest two successful patterns of case-based decisions that could be linked to the issue of market efficiency. Moreover, our case-based modeling reflects some behavioral phenomena observed on financial markets

Correcting intraday periodicity bias in realized volatility measures

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. We investigate how this intraday periodicity (IP) affects both finite sample as well as asymptotic properties of several popular realized estimators of daily integrated volatility which are based on functionals of M intraday returns. We demonstrate that most of the estimators considered in our study exhibit a finite-sample bias due to IP, which can however get negligible if the number of intraday returns diverges to infinity. We suggest appropriate correction factors for this bias based on estimates of the IP. The adequacy of the new corrections is evaluated by means of a Monte Carlo simulation study and an empirical example

The effect of intraday periodicity on realized volatility measures

U-shaped intraday periodicity (IP) is a typical stylized fact characterizing intraday returns on risky assets. In this study we focus on the realized volatility and bipower variation estimators for daily integrated volatility (IV ) which are based on intraday returns following a discrete-time model with IP. We demonstrate that neglecting the impact of IP on realized estimators may lead to non-valid statistical inference concerning IV for the commonly available number of intraday returns, moreover, the size of daily jump tests may be distorted. Given the functional form of IP, we derive corrections for the realized measures of IV . We show in a Monte Carlo and an empirical study that the proposed corrections improve commonly point and interval estimators of the IV and tests for jumps

Correcting intraday periodicity bias in realized volatility measures

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. We investigate how this intraday periodicity (IP) affects both finite sample as well as asymptotic properties of several popular realized estimators of daily integrated volatility which are based on functionals of M intraday returns. We demonstrate that most of the estimators considered in our study exhibit a finite-sample bias due to IP, which can however get negligible if the number of intraday returns diverges to infinity. We suggest appropriate correction factors for this bias based on estimates of the IP. The adequacy of the new corrections is evaluated by means of a Monte Carlo simulation study and an empirical example

No-transaction bounds and estimation risk

This paper considers a mean-variance portfolio investor facing proportional transaction costs and willing to account for estimation risk with a shrinkage approach. In such a situation the optimal portfolio policy can be characterized by no-transaction bounds existing both due to transaction cost and estimation risk effects. The paper derives analytically the optimal portfolio policy and provides a simulation study to illustrate the obtained results.Portfolio theory, Statistical methods, Econometric of financial markets, Financial markets,

Sequential monitoring of minimum variance portfolio

Statistical process control, EWMA control charts, Volatility timing, Covariance matrix estimation,