nep-for New Economics Papers
on Forecasting
Issue of 2016‒10‒09
eight papers chosen by
Rob J Hyndman
Monash University

  1. Learning Time-Varying Forecast Combinations By Antoine Mandel; Amir Sani
  2. The Best of All Possible Worlds: Using Analysts' Scenario-Based Valuations to Assess Target Price Optimism By Piotroski, Joseph D.; Joos, Peter
  3. Asset market response to monetary policy news from SNB press releases By Hüning, Hendrik
  4. Vulnerable growth By Adrian, Tobias; Boyarchenko, Nina; Giannone, Domenico
  5. A fully non-parametric heteroskedastic model By Matthieu Garcin; Clément Goulet
  6. Decoupling the short- and long-term behavior of stochastic volatility By Mikkel Bennedsen; Asger Lunde; Mikko S. Pakkanen
  7. Recession forecasting using Bayesian classification By Davig, Troy A.; Smalter Hall, Aaron
  8. Do Inflation Expectations Granger Cause Inflation? By Pär, Österholm; Pär, Stockhammar

  1. By: Antoine Mandel (Centre d'Economie de la Sorbonne - Paris School of Economics); Amir Sani (Centre d'Economie de la Sorbonne - Paris School of Economics)
    Abstract: Combining forecasts has been demonstrated as a robust solution to noisy data, structural breaks, unstable forecasters and shifting environmental dynamics. In practice, sophisticated combination methods have failed to consistently outperform the mean over multiple horizons, pools of varying forecasters and different endogenous variables. This paper addresses the challenge to “develop methods better geared to the intermittent and evolving nature of predictive relations”, noted in Stock and Watson (2001), by proposing an adaptive non-parametric “meta” approach that provides a time-varying hedge against the performance of the mean for any selected forecast combination approach. This approach arguably solves the so-called “Forecast Combination Puzzle” using a meta-algorithm that adaptively hedges weights between the mean and a specific forecast combination algorithm or pool of forecasters augmented with one or more forecast combination algorithms. Theoretical performance bounds are reported and empirical performance is evaluated on the seven country macroeconomic output and inflation dataset introduced in Stock and Watson (2001) as well as the Euro-area Survey of Professional Forecasters.
    Keywords: Forecast combinations; Forecast combination puzzle; Machine learning; Econometrics
    JEL: C71 D85
    Date: 2016–04
  2. By: Piotroski, Joseph D. (Stanford University); Joos, Peter (?)
    Abstract: Using a unique dataset of scenario-based investment reports, we examine whether the placement of an analyst's valuation forecast, relative to his/her own subjective assessment of the distribution of scenario-based valuations for the covered firm (i.e., embedded "tilt"), conveys information to investors. We document that this embedded tilt is a function of both fundamental risk attributes and behavioral factors. We find that among analysts forecasting price appreciation over the next twelve months, embedded tilt incrementally predicts ex-post valuation errors and realized returns, with valuations tilted towards upside (downside) scenarios generating more negative (positive) valuation errors and returns. In contrast, when analysts forecast price declines, the predictive value of tilt disappears, suggesting that the negative valuation forecast is a sufficient statistic for analyst pessimism. Additional analyses reveal that estimates of implied tilt derived from observable firm characteristics can be used by investors to distinguish among target price forecasts lacking scenario-based information. Although we cannot distinguish between rational and behavioral explanations for our results, we show that analyst valuation forecasts that predict equally optimistic implied returns to investors are not all created equal, and that observable firm characteristics correlated with asymmetric valuation risks and biases can be used to distinguish between investments.
    Date: 2015–08
  3. By: Hüning, Hendrik
    Abstract: This paper analyses the effects of Swiss National Bank (SNB) communication on asset prices. It distinguishes between different monetary policy news contained in press releases following a monetary policy decision. Employing a latent variable approach and event-study methods, I find that medium- and long-term bond yields respond to changes in the communicated inflation and GDP forecasts as well as to the degree of pessimism expressed in press releases. Exchange rates mainly react to changes in the GDP forecast while stocks do not react to SNB communication on monetary policy announcement days. Additionally, short-term expectations about the future path of the policy rate are driven by the communicated inflation forecast. The results underline the role of qualitative news next to quantitative forecasts in influencing market expectations and asset prices.
    Keywords: monetary policy communication,asset markets
    JEL: E43 E52 G12
    Date: 2016
  4. By: Adrian, Tobias (Federal Reserve Bank of New York); Boyarchenko, Nina (Federal Reserve Bank of New York); Giannone, Domenico (Federal Reserve Bank of New York)
    Abstract: We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in conditional volatility and a decline in the conditional mean of GDP growth, leading to a highly skewed distribution. The lower quantiles of GDP growth exhibit strong variation as a function of financial conditions, while the upper quantiles are stable over time. Although measures of financial conditions have significant influence in forecasts of downside vulnerability, measures of economic conditions have significant predictive power only for the median of the distribution. These findings are robust both in and out of sample and to the inclusion of different measures of financial conditions. We quantify GDP vulnerability as relative entropy between the empirical conditional and unconditional distribution. We show that this measure of vulnerability is highly asymmetric: The contribution to the total relative entropy of the probability mass below the median of the conditional distribution is larger and more volatile than the contribution of the probability mass above the median. The asymmetric response of the distribution of GDP growth to financial and economic conditions—with adverse financial conditions increasing downside vulnerability of growth but not the median forecast—is challenging for standard models of the macroeconomy. We argue that the inclusion of a financial sector is crucial for generating the observed dynamics of growth vulnerability.
    Keywords: Downside risk; entropy; quantile regressions
    JEL: C22 E17 E37
    Date: 2016–09–29
  5. By: Matthieu Garcin (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Natixis Asset Management - SAMS, LABEX Refi - ESCP Europe); Clément Goulet (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LABEX Refi - ESCP Europe)
    Abstract: In this paper we propose a new model for estimating returns and volatility. Our approach is based both on the wavelet denoising technique and on the variational theory. We assess that the volatility can be expressed as a non-parametric functional form of past returns. Therefore, we are able to forecast both returns and volatility and to build confidence intervals for predicted returns. Our technique outperforms classical time series theory. Our model does not require the stationarity of the observed log-returns, it preserves the volatility stylised facts and it is based on a fully non-parametric form. This non-parametric form is obtained thanks to the multiplicative noise theory. To our knowledge, this is the first time that such a method is used for financial modeling. We propose an application to intraday and daily financial data.
    Keywords: Volatility modeling,non variational calculus,wavelet theory,trading strategy
    Date: 2015–09
  6. By: Mikkel Bennedsen; Asger Lunde; Mikko S. Pakkanen
    Abstract: We study the empirical properties of realized volatility of the E-mini S&P 500 futures contract at various time scales, ranging from a few minutes to one day. Our main finding is that intraday volatility is remarkably rough and persistent. What is more, by further studying daily realized volatility measures of more than five thousand individual US equities, we find that both roughness and persistence appear to be universal properties of volatility. Inspired by the empirical findings, we introduce a new class of continuous-time stochastic volatility models, capable of decoupling roughness (fine properties) from long memory and persistence (long-term behavior) in a simple and parsimonious way, which allows us to successfully model volatility at all intraday time scales. Our prime model is based on the so-called Brownian semistationary process and we derive a number of theoretical properties of this process, relevant to volatility modeling. Finally, in a forecasting study, we find that our new models outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
    Date: 2016–10
  7. By: Davig, Troy A. (Federal Reserve Bank of Kansas City); Smalter Hall, Aaron (Federal Reserve Bank of Kansas City)
    Abstract: The authors demonstrated the use of a Naïve Bayes model as a recession forecasting tool. The approach has a close connection to Markov-switching models and logistic regression but also important differences. In contrast to Markov-switching models, Naïve Bayes treats National Bureau of Economic Research business cycle turning points as data rather than hidden states to be inferred by the model. Although Naïve Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data.
    Keywords: Forecasting; Naïve Bayes model; Recession
    JEL: C11 C5 E32 E37
    Date: 2016–08–01
  8. By: Pär, Österholm (Örebro University School of Business); Pär, Stockhammar (National Institute of Economic Research)
    Abstract: In this paper, we investigate whether survey measures of inflation expec-tations in Sweden Granger cause Swedish CPI-inflation. This is done by studying the precision of out-of-sample forecasts from Bayesian VAR models using a sample of quarterly data from 1996 to 2016. It is found that the inclusion of inflation expectations in the models tends to improve forecast precision. However, the improvement is typically small enough that it could be described as economically irrelevant. One exception can possibly be found in the expectations of businesses in the National Insti-tute of Economic Research’s Economic Tendency Survey; when included in the models, these improve forecast precision in a meaningful way at short horizons. Taken together, it seems that the inflation expectations studied here do not provide a silver bullet for those who try to improve VAR-based forecasts of Swedish inflation. The largest benefits from using these survey expectations may instead perhaps be found among analysts and policy makers; they can after all provide relevant information concerning, for example, the credibility of the inflation target or challenges that the central bank might face when conducting monetary policy.
    Keywords: Bayesian VAR; Granger causality; Out-of-sample forecasts
    JEL: C32 F43
    Date: 2016–10–03

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