nep-for New Economics Papers
on Forecasting
Issue of 2022‒02‒28
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models By Elie Bouri; Christina Christou; Rangan Gupta
  2. Forecasting Italian GDP growth with epidemiological data By Valentina Aprigliano; Alessandro Borin; Francesco Paolo Conteduca; Simone Emiliozzi; Marco Flaccadoro; Sabina Marchetti; Stefania Villa
  3. Big data forecasting of South African inflation By Byron Botha; Rulof Burger; Kevin Kotz; Neil Rankin; Daan Steenkamp
  4. Conditional Heteroskedasticity in the Volatility of Asset Returns By Ding, Y.
  5. Stock returns predictability with unstable predictors By Calonaci, Fabio; Kapetanios, George; Price, Simon
  6. Conflict Prediction using Kernel Density Estimation By Tapsoba, Augustin
  7. Inflation-Forecast Targeting: A New Framework for Monetary Policy? By PINSHI, Christian P.

  1. By: Elie Bouri (School of Business, Lebanese American University, Lebanon); Christina Christou (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: The returns of cryptocurrencies tend to co-move, with their degree of co-movement being contingent on the (bullish- or bearish-) states. Given this, we use standard factor models and regime-switching factor loadings to forecast the returns of a specific cryptocurrency based on its lagged information and informational contents of 14 other cryptocurrencies, with these 15 together constituting 65% of the market capitalization. Considering top five cryptocurrencies namely, Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin, we find significant forecastability and evidence that factor models, in general, outperform the benchmark random-walk model, with the regime-switching versions standing out in the majority of the cases.
    Keywords: Cryptocurrencies, Factor Model, Markov-switching, Forecasting
    JEL: C22 C53 G15
    Date: 2022–02
  2. By: Valentina Aprigliano (Bank of Italy); Alessandro Borin (Bank of Italy); Francesco Paolo Conteduca (Bank of Italy); Simone Emiliozzi (Bank of Italy); Marco Flaccadoro (Bank of Italy); Sabina Marchetti (Bank of Italy); Stefania Villa (Bank of Italy)
    Abstract: The COVID-19 epidemic affected the ability of traditional forecasting models to produce reliable scenarios for the evolution of economic activity. We combine macroeconomic variables with epidemiological indicators to account for the COVID-19 shock and predict the short-term evolution of Italian GDP growth. In particular, we use a mixed-frequency dynamic factor model together with a sophisticated susceptible-infectious-recovered epidemic model featuring endogenous policy responses. First, we simulate different scenarios of economic growth depending on the course of the pandemic in Italy. Second, we evaluate the forecast performance of the model for the period August 2020-March 2021. We find that taking epidemiological indicators into consideration is important for obtaining reliable projections.
    Keywords: foreign direct investment, capital controls, national security
    JEL: F21 F38 F52
    Date: 2021–12
  3. By: Byron Botha; Rulof Burger; Kevin Kotz; Neil Rankin; Daan Steenkamp
    Abstract: BigdataforecastingofSouthAfricaninflatio n
    Date: 2022–02–22
  4. By: Ding, Y.
    Abstract: We propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models.
    Keywords: forecasting, GARCH, SHARV, volatility, volatility of volatility
    JEL: C22 C32 C53 C58 G17
    Date: 2021–11–09
  5. By: Calonaci, Fabio; Kapetanios, George; Price, Simon
    Abstract: We re-examine predictability of US stock returns. Theoretically well-founded models predict that stationary combinations of I(1) variables such as the dividend or earnings to price ratios or the consumption/asset/income relationship often known as CAY may predict returns. However, there is evidence that these relationships are unstable, and that allowing for discrete shifts in the unconditional mean (location shifts) can lead to greater predictability. It is unclear why there should be a small number of discrete shifts and we allow for more general instability in the predictors, characterised by smooth variation variation, using a method introduced by Giraitis, Kapetanios and Yates. This can remove persistent components from observed time series, that may otherwise account for the presence of near unit root type behaviour. Our methodology may therefore be seen as an alternative to the widely used IVX methods where there is strong persistence in the predictor. We apply this to the three predictors mentioned above in a sample from 1952 to 2019 (including the financial crisis but excluding the Covid pandemic) and find that modelling smooth instability improves predictability and forecasting performance and tends to outperform discrete location shifts, whether identified by in-sample Bai-Perron tests or Markov-switching models.
    Keywords: returns predictability; long horizons; instability
    Date: 2022–02–18
  6. By: Tapsoba, Augustin
    Abstract: Being able to assess conflict risk at local level is crucial for preventing political violence or mitigating its consequences. This paper develops a new approach for predicting the timing and location of conflict events from violence history data. It adapts the methodology developed in Tapsoba (2018) for measuring violence risk across space and time to conflict prediction. Violence is modeled as a stochastic process with an unknown underlying distribution. Each conflict event observed on the ground is interpreted as a random realization of this process and its underlying distribution is estimated using kernel density estimation methods in a three-dimensional space. The optimal smoothing parameters are estimated to maximize the likelihood of future conflict events. An illustration of the practical gains (in terms of out-of-sample forecasting performance) of this new methodology compared to standard space-time autoregressive models is shown using data from Côte d’Ivoire.
    Keywords: Conflict; Insecurity; Kernel Density Estimation
    JEL: C1 O12 O13
    Date: 2022–01–26
  7. By: PINSHI, Christian P.
    Abstract: This article provides an overview of inflation-forecast targeting (IFT) to build credibility and maintain stability. We show how inflation-forecast targeting is a transparent approach and an ideal strategy for monetary policy. In addition, public understanding would be essential to foster confidence and ensure the effectiveness of monetary policy. To this end, adequate management of expectations and transparent communication are important.
    Keywords: Inflation-Forecast Targeting, Expectations, Communication, Monetary policy
    JEL: E47 E52 E58
    Date: 2022–01

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