Browsing by Subject "wavelets"

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  • Martins, Manuel M. F.; Verona, Fabio (2020)
    Bank of Finland Research Discussion Papers 4/2020
    We show that the New Keynesian Phillips Curve (NKPC) outperforms standard benchmarks in forecasting U.S. inflation once frequency-domain information is taken into account. We do so by decomposing the time series (of inflation and its predictors) into several frequency bands and forecasting separately each frequency component of inflation. The largest statistically significant forecasting gains are achieved with a model that forecasts the lowest frequency component of inflation (corresponding to cycles longer than 16 years) flexibly using information from all frequency components of the NKPC inflation predictors. Its performance is particularly good in the returning to recovery from the Great Recession.
  • Faria, Gonçalo; Verona, Fabio (2016)
    Bank of Finland Research Discussion Papers 29/2016
    Published in Journal of Empirical Finance, 45, January, 2018, 228–242 https://doi.org/10.1016/j.jempfin.2017.11.009
    We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method signi cantly improves upon the original sum-of-the-parts and delivers statistically and economically gains over historical mean forecasts, with monthly out-of-sample R2 of 2.60% and annual utility gains of 558 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the parts with the highest predictive power, and from the fact that the selected frequency-decomposed parts carry complementary information that captures di erent frequencies of stock market returns.
  • Faria, Gonçalo; Verona, Fabio (2018)
    Journal of Empirical Finance January 2018
    Published in Bank of Finland Research Discussion Papers 29/2016.
    We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method significantly improves upon the original sum-of-the-parts and delivers statistically and economically gains over historical mean forecasts, with monthly out-of-sample R2 of 2.60% and annual utility gains of 558 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the parts with the highest predictive power, and from the fact that the selected frequency-decomposed parts carry complementary information that captures different frequencies of stock market returns.
  • Martins, Manuel M. F.; Verona, Fabio (2021)
    Bank of Finland Research Discussion Papers 8/2021
    Policymakers and researchers see inflation characterized by cyclical fluctuations driven by changes in resource utilization and temporary shocks, around a trend influenced by inflation expectations. We study the in-sample inflation dynamics and forecast inflation out-of-sample by analyzing a New Keynesian Phillips Curve (NKPC) in the frequency domain. In-sample, while inflation expectations dominate medium-to-long-run cycles, energy prices dominate short cycles and business-to-medium cycles once expectations became anchored. While statistically significant, unemployment is not economically relevant for any cycle. Out-of-sample, forecasts from a low-frequency NKPC significantly outperform several benchmark models. The long-run component of unemployment is key for such remarkable forecasting performance.
  • Verona, Fabio (2016)
    Bank of Finland Research Discussion Papers 14/2016
    Published in Economics Letters, Volume 144, July 2016: 75–79 https://doi.org/10.1016/j.econlet.2016.04.024
    Despite an increase in research – motivated by the global financial crisis of 2007-08 – empirical studies on the financial cycle are rare compared to those on the business cycle. This paper adds some new evidence to this scarce literature by using a different empirical methodology – wavelet analysis – to extract financial cycles from the data. Our results confirm that the U.S. financial cycle is (much) longer than the business cycle, but we do not find strong evidence supporting the view that the financial cycle has lengthened during the Great Moderation period.
  • Voutilainen, Ville (2017)
    Bank of Finland Research Discussion Papers 11/2017
    We propose a wavelet-based approach for construction of a financial cycle proxy. Specifically, we decompose three key macro-financial variables – private credit, house prices, and stock prices – on a frequency-scale basis using wavelet multiresolution analysis. The resulting “wavelet-based” sub-series are aggregated into a composite index representing our cycle proxy. Selection of the sub-series deemed most relevant is done by emphasizing early warning properties. The wavelet-based financial cycle proxy is shown to perform well in detecting banking crises in out-of-sample exercises, outperforming the credit-to-GDP gap and a financial cycle proxy derived using the approach of Schüler et al. (2015).