Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data

Abstract

This paper studies in some details the joint-use of high-frequency data and economic variables to model financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes in macroeconomic variables in a MIDAS framework and allows macroeconomic information to be included directly into the estimation and forecast procedure.Using more than 10 years of high-frequency transactions for 55 U.S. stocks, we argue that the combination of low-frequency exogenous economic indicators with high-frequency financial data improves our ability to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multiperiod Value-at-Risk. We document that nominal corporate profits and term spreads generate accurate risk measures forecasts at horizons beyond two business weeks.

Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

“…This has primarily been used to empirically support countercyclicality in stock market volatility (see e.g. Engle et al (2013) and Dominicy and Vander Elst (2015)). Inspired by the findings of Mikosch and Stȃricȃ (2004) which show that long-range dependence and the integrated GARCH effect may be explained by level shifts in the unconditional variance, Amado and Teräsvirta (2013) propose a multiplicative component version of the GJR-GARCH model for capturing volatility persistence.…”

“…We propose in the following sections two ways to parsimoniously formulate f (•; η) using non-overlapping weekly and monthly averages of the realized measure to be consistent with the idea of a slow-moving, low-frequency component. 3 We model low-frequency movements in conditional variance using (aggregates of) past information of the realized measure rather than tying it to macroeconomic state variables as in Engle et al (2013) and Dominicy and Vander Elst (2015). This procedure renders the model in (2)-(5) complete with dynamic specifications of all variables included in the model.…”

“…that typically rely on additional assumptions on the dynamics of the exogenous variables (e.g. random walks (Dominicy and Vander Elst, 2015)), outside-generated forecasts (usually from a standard autoregressive specification) of the exogenous variables in the model (Conrad and Loch, 2015) or the assumption that the longterm component is constant for the forecasting horizon (Engle et al, 2013). 4 We do, however, emphasize that our proposed model accommodates well the inclusion of exogenous information such as (possibly low-frequency) macroeconomic variables if deemed appropriate.…”

[Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

“…When predicting financial market volatility, macroeconomic indicators are important (Andersen et al [4]; Conrad and Loch [10]; Dorion [11]). e GARCH-MIDAS model has been the most popular model adopted to investigate the correlations between aggregate financial volatility and macroeconomic or financial variables (Conrad et al [12]; Conrad et al [13]; Pan et al [14]; Su et al [15]; Conrad and Kleen [6]; Opschoor et al [16]; Dominicy and Vander Elst [17]; Lindblad [18]; Amendola et al [19]; Conrad et al [12]; and Borup and Jakobsen [20]).…”

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

“…By allowing for a mixed-frequency setting, this approach bridges the gap between daily stock returns and low-frequency (e.g., monthly, quarterly) explanatory variables. For further applications of GARCH-MIDAS-type models see, for example, Conrad, Loch, and Rittler (2014), Opschoor, van Dijk, and van der Wel (2014), Dominicy and Vander Elst (2015), Lindblad (2017), Amendola, Candila, and Scognamillo (2017), Pan, Wang, Wu, and Yin (2017), Conrad, Custovic, and Ghysels (2018), and Borup and Jakobsen (2019). For a recent survey on multiplicative component models see Amado, Silvennoinen, and Teräsvirta (2019).…”