Time-frequency forecast of the equity premium

Abstract

Any time series can be decomposed into cyclical components uctuating at dierent frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and nd that, by selecting the relevant frequencies for equity premium forecasting, this method signicantly improves in both statistical and economic sense upon standard time series forecasting methods. This improvement is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.

Forecasting Inflation with the New Keynesian Phillips Curve: Frequencies Matter*

“…Our approach extends the method proposed by Faria and Verona (2018, 2021). In those papers, each frequency component of the variable to be forecast depends only on the same frequency component of the predictor variables.…”

“…Gallegati et al . 2011, Gallegati and Ramsey 2013, Ortu, Tamoni and Tebaldi 2013, and Faria and Verona 2018, 2021).…”

“…The second step of our approach builds on a literature that uses discrete wavelet methods to forecast out‐of‐sample economic and financial time series. Examples include Rua (2011, 2017), who forecast GDP growth and inflation using a factor‐augmented wavelets approach; Zhang, Gençay and Yazgan (2017), Faria and Verona (2018, 2020, 2021), and Stein (2024) who focus on forecasting stock market returns; Caraiani (2017), who forecasts exchange rates; and Kilponen and Verona (2022), who use different proxies for Tobin's Q and cash flow to forecast investment.…”

“…We build on the literature that improves forecasts of a variable of interest by forecasting its different frequency components separately rather than the aggregate (see, e.g. Faria and Verona 2018, 2021). Our method consists of decomposing the time series of the NKPC into several frequency bands, forecasting separately each frequency component of inflation, and then summing up those forecasts to obtain the forecast for aggregate inflation.…”

Innovative Teaching and Learning of Quantum Finance and Intelligent Trading Strategies

“…They presented clearly how stochastic dynamics, self-similarity and scaling phenomena can be applied to model financial markets. After that, numerous R&D projects on Econophysics and quantitative finance have been conducted, resulting in various financial applications including derivative pricing (De Spiegeleer et al, 2018), financial forecast (Faria & Verona, 2021), risk management (Cont, 2009), and portfolio analysis (Bauder et al, 2021).…”