High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialised estimators have been developed, they have had limited availability in open source software. HighFrequencyCovariance...

This paper analyzes the implications of autoregressive betas in single factor models for the statistical properties of stock returns. It is demonstrated that this assumption alone is sufficient to account for the most important stylized facts of stock returns, namely conditional...

Value-at-Risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a new simple approach to estimation of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting...

The sample covariance matrix is known to contain substantial statistical noise, making it inappropriate for use in financial decision making. Leading researchers have proposed various filtering methods that attempt to reduce the level of noise in the covariance matrix estimator. In most cases,...

We first consider an extension of the generalized autoregressive conditional heteroskedasticity (GARCH) model that allows for a more flexible weighting of financial squared-returns for the filtering of volatility. The parameter for the squared-return in the GARCH model is time-varying with an...

We analyze the properties of different estimators of multivariate volatilities in the presence of microstructure noise, with particular focus on the Fourier estimator. This estimator is consistent in the case of asynchronous data and robust to microstructure effects; further we prove the...

At its core, portfolio and risk management is about gathering and processing market-related data in order to make effective investment decisions. To this end, risk and return statistics are estimated from relevant financial data and used as inputs within the investment process. It is this...

Deriving estimators from historical data is common practice in applied quantitative finance. The availability of ever larger data sets and easier access to statistical algorithms has also led to an increased usage of historical estimators. In this research note, we illustrate how to assess the...

Expected returns can hardly be estimated from time series data. Therefore, many recent papers suggest investing in the global minimum variance portfolio. The weights of this portfolio depend only on the return variances and covariances, but not on the expected returns. The weights of the global...

The objective of this paper is to evaluate the behaviour of Nigerian Stock Exchange (NSE) sector indices. Specifically, the paper analyzes the returns correlation, serial correlation and heteroscedasticity on the NSE All-share Index, Banking Index, Consumer Goods Index, Oil & Gas Index, NSE 30...