Forecasting realized volatility of international REITs: The role of realized skewness and realized kurtosis

Abstract

We use an international dataset on 5-minutes interval intraday data covering nine leading markets and regions to construct measures of realized volatility, realized jumps, realized skewness, and realized kurtosis of returns of international Real Estate Investment Trusts (REITs) over the daily period of September, 2008 to August, 2020. We study out-of-sample the predictive value of realized skewness and realized kurtosis for realized volatility over and above realized jumps, where we also differentiate between measures of "good" realized volatility and "bad" realized volatility. We find that realized skewness and realized kurtosis significantly improve forecasting performance at a daily, weekly, and monthly forecast horizon, and that their contribution to forecasting performance outweighs in terms of significance the contribution of realized jumps. Our results have important implications for investors and policymakers.

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

“…For instance, Lee et al (2002) show that volatility increases with pessimistic sentiment. The similar results are found in Liang et al (2020), Bonato et al (2022) and other literature. More importantly, Table 3 shows that the R 2 for basic HAR-RV is 0.535 while the R 2 for HAR-RV-IPI is 0.601.…”

“…In this section, we test the forecasting power of IPI for future realized volatility. The impact of investor sentiment on volatility is short-lived (Bonato et al, 2022;Chiu et al, 2018;Renault, 2017). Therefore, we focus on short-horizon (one-day-ahead) volatility forecasting, namely, h = 1 in model ( 4)-( 6).…”

“…Previous literature has used numerous different predictors to examine its impact on volatility forecasting. For example, Bonato et al (2022) show that realized skewness and realized kurtosis significantly improve the performance of volatility forecasting. Additionally, macro factors (Conrad & Loch, 2014; Hu et al, 2020; Smiech et al, 2015), economic policy uncertainty (Li et al, 2019; Liu & Zhang, 2015), and extreme events (Ma et al, 2019; Schwert, 2011) also play important roles in volatility forecasting.…”

Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets

“…It is well understood that cross-market spillovers represent an important aspect of financial modeling and forecasting, asset pricing, and portfolio and risk management. 2 Recent research has highlighted the importance of analyzing spillovers in higher-order moments via elements such as volatility, skewness, kurtosis, and the jump components of volatility, all of which can reveal useful information about asymmetry and fat-tail risks across markets (Bonato et al 2022 ; Gkillas et al 2020a ; Amaya et al 2015 ; Lai and Sheu 2010 ). This is particularly relevant when asset return distributions are generally non-normal, skewed, and prone to fat tails, a stylized fact for equity, cryptocurrency, and commodity markets (Kristjanpoller et al 2020 ; Gkillas and Katsiampa 2018 ; Osterrierder and Lorenz 2017 ).…”

Moments and momentum in the returns of securitized real estate: A cross-country study of risk factors driving real estate investment trusts before and during COVID-19

“…Doan et al [ 2 ] pointed out that both skewness and kurtosis explain Australian stock returns and consistently influence US stock returns. Other studies have employed a four-moment capital asset pricing model (CAPM) to estimate risk premium, and the MHAR-RV (Multivariate heterogeneous autoregressive-realized volatility) model to capture the long memory of REIT market variance [ 3 , 4 ]. However, no previous studies have yet analyzed the REIT market using the Fama–French five-factor model.…”