nep-for New Economics Papers
on Forecasting
Issue of 2019‒07‒22
three papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models By Massimo Guidolin; Manuela Pedio
  2. A long short-term memory stochastic volatility model By Nghia Nguyen; Minh-Ngoc Tran; David Gunawan; R. Kohn
  3. Identifying US business cycle regimes using dynamic factors and neural network models By Soybilgen, Baris

  1. By: Massimo Guidolin; Manuela Pedio
    Abstract: We use monthly data on the US riskless yield curve for a 1982-2015 sample to show that mixing simple regime switching dynamics with Nelson-Siegel factor forecasts from time series models extended to encompass variables that summarize the state of monetary policy, leads to superior predictive accuracy. Such spread in forecasting power turns out to be statistically significant even controlling for parameter uncertainty and sample variation. Exploiting regimes, we obtain evidence that the increase in predictive accuracy is stronger during the Great Financial Crisis in 2007-2009, when monetary policy underwent a significant, sudden shift. Although more caution applies when transaction costs are accounted for, we also report that the increase in predictive power owed to the combination of regimes and of monetary variables that capture the stance of unconventional monetary policies is tradeable. We devise and test butterfly strategies that trade on the basis of the forecasts from the models and obtain evidence of riskadjusted profits both per se and in comparisons to simpler models.
    Keywords: Term structure of interest rates, Dynamic Nelson-Siegel factors, regime switching, butterfly strategies, unconventional monetary policy
    Date: 2019
  2. By: Nghia Nguyen; Minh-Ngoc Tran; David Gunawan; R. Kohn
    Abstract: Stochastic Volatility (SV) models are widely used in the financial sector while Long Short-Term Memory (LSTM) models have been successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods non trivially and proposes a model for capturing the dynamics of financial volatility process, which we call the LSTM-SV model. The proposed model overcomes the short-term memory problem in conventional SV models, is able to capture non-linear dependence in the latent volatility process, and often has a better out-of-sample forecast performance than SV models. The conclusions are illustrated through simulation studies and applications to three financial time series datasets: US stock market weekly index SP500, Australian stock weekly index ASX200 and Australian-US dollar daily exchange rates. We argue that there are significant differences in the underlying dynamics between the volatility process of SP500 and ASX200 datasets and that of the exchange rate dataset. For the stock index data, there is strong evidence of long-term memory and non-linear dependence in the volatility process, while this is not the case for the exchange rates. An user-friendly software package together with the examples reported in the paper are available at
    Date: 2019–06
  3. By: Soybilgen, Baris
    Abstract: We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Then, dynamic factors are extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in sample and out of sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.
    Keywords: Dynamic Factor Model; Neural Network; Recession; Business Cycle
    JEL: C38 E32 E37
    Date: 2018–07–05

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