Multiple-block dynamic equicorrelations with realized measures, leverage and endogeneity

Abstract

This paper proposes a new stochastic volatility model with time-varying expected return, which enables us to predict returns based on exponential moving averages of the past returns frequently used in practice. Particularly, exploiting a particle filter in a self-organizing state space framework, we demonstrate that a simple return predictionbased strategy is superior to well-known strategies such as equally-weighted, minimumvariance and risk parity portfolios, which do not depend on return prediction. In addition, we develop three types of anomaly detectors that are easily implemented in the algorithm of the particle filter and apply them to investment decision. As a result, our model robustly outperforms the exponential moving average. Our dataset is monthly total returns of global assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics such as compound returns, Sharpe ratios, Sortino ratios or drawdowns.

Dynamic factor, leverage and realized covariances in multivariate stochastic volatility

“…A common way to incorporate additional information on the volatilities and covolatilities of asset returns is to use high-frequency data that include information on intraday asset trades. The realized stochastic volatility (RSV) models, for example, are this type of extension and are known to outperform models without realized measures at estimating model parameters, forecasting volatilities and portfolio performance (Takahashi et al (2009), Hansen et al (2012), Koopman and Scharth (2013), Takahashi et al (2016), Shirota et al (2017), Kurose and Omori (2019), Yamauchi and Omori (2019)). However, it is not straightforward to extend the FMSV model using the realized covariance matrices in a similar manner, since the realized covariance matrices do not directly correspond to the factor loading matrix and the idiosyncratic volatilities that are not explained by the factors.…”

Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance

“…In addition, such a problem will also be useful for covariance matrix modeling in a low-frequency setting because it often suffers from the curse of dimensionality due to the increase of the number of unknown parameters to be estimated, and thus it is a common practice to impose a certain structure on covariance matrices for reducing the number of unknown parameters in models. For example, Tao et al [64] have proposed fitting a matrix factor model to daily covariance matrices which are estimated from high-frequency data using the methodology of [68], while Kurose & Omori [46,47] have introduced a dynamic (multiple-block) equicorrelation structure to multivariate stochastic volatility models. The afore-mentioned testing will be useful for examining the validity of such specification.…”

Stochastic volatility model with range-based correction and leverage

“…Note that the posterior predictive density of α n+1 is given by N Kurose and Omori (2020) extended this model to the multivariate case and developed an efficient Bayesian estimation method. I assume that the prior distributions of the parameters as Kurose and Omori (2020) and employ the estimation algorithm for the univariate RSV model.…”