This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (2013a). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes. To tackle this problem, this paper extends the Bernoulli model by introducing a novel modeling approach called Markov Dimension

In the value‐at‐risk (VaR) literature, many existing works assume that the noise distribution is the same over time. To take into account the potential time‐varying dynamics of stock returns, we propose a dynamic asymmetric exponential distribution‐based framework. The new method includes a time‐varying shape parameter to control the dynamic shape of the distribution, a time‐varying probability parameter

No abstract is available for this article.

Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data‐driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically,

In this paper, we consider Google trends as a measure of investors’ attention in the predictability of stock returns across eleven major US sectors. The theoretical motivation for our paper is clear. In seeking information to guide investment decisions, investors’ sentiments are shaped by news that could induce changes in the prices of stocks which Google trends data have been argued more fitting.

Models based on factors such as size or value are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid assets, this measure is difficult to obtain for asset pricing factors such as size and value that include smaller illiquid stocks that are not traded at

We develop a model for optimizing multiple‐asset portfolios with semi‐parametric liquidity‐adjusted value‐at‐risk (LVaR), whereby linear correlations are substituted by the multivariate nonlinear Kendall's tau dependence measure, under multiple credible operational and budget constraints. When considering a diversified large portfolio of international stock markets of both developed and emerging economies

We investigated the out‐of‐sample forecasting performance of six bias‐corrected estimators that have recently emerged in the literature for the Ornstein–Uhlenbeck process: the naïve estimator, the Tang and Chen estimator, the Bao et al. estimator, the bootstrap estimator, the Wang et al. estimator, and the bootstrap estimator based on the Euler method, along with the benchmark least squares (LS) estimator

We have investigated methodologies for predicting radiologists' workload in a short time interval by adopting a machine learning technique. Predicting for shorter intervals requires lower execution time combined with higher accuracy. To deal with this issue, an ensemble model is proposed with the fixed‐batch‐training method. To excel in the execution time, a fixed‐batch‐training method is used. On

We provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for value‐at‐risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR forecasting, and they

Using real‐time data from 1985:Q1 to 2017:Q3 and simple vector autoregression (VAR) models, we show that there is a substantial payoff in combining credit supply indicators with house prices for forecasting real economic activity in the USA. Consistent with the findings in the literature, we show that the forecasts from a bivariate VAR model of real activity and credit conditions dominate the forecasts

This paper explains cross‐market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross‐sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the

In this paper, we forecast stock returns using time‐varying parameter (TVP) models with parameters driven by economic conditions. An in‐sample specification test shows significant variation in the parameters. Out‐of‐sample results suggest that the TVP models outperform their constant coefficient counterparts. We also find significant return predictability from both statistical and economic perspectives

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook “people talking about this” (PTAT) statistics, opinion polls, and prediction

No abstract is available for this article.

The directional news impact curve (DNIC) is a relationship between returns and the probability of next period's return exceeding a certain threshold—zero in particular. Using long series of S&P500 index returns and a number of parametric models suggested in the literature, as well and flexible semiparametric models, we investigate the shape of the DNIC and forecasting abilities of these models. The

Using receiver operating characteristic (ROC) techniques, we evaluate the predictive content of the monthly main economic indicators (MEI) of the Organization for Economic Co‐operation and Development (OECD) for predicting both growth cycle and business cycle recessions at different horizons. From a sample that covers 123 indicators for 32 OECD countries as well as for Brazil, China, India, Indonesia

The Birnbaum‐Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of this distribution and the development of many review articles, few papers have considered data with a dependency structure. To fill this gap, we introduce

This study systematically broadens the relevance of possible model performance asymmetries across business cycles in the spirit of the recent state‐dependent forecast evaluation literature (e.g. Chauvet & Potter, 2013) to hundreds of macroeconomic indicators and deepens the forecast evaluation of the recent factor model literature on hundreds of target variables (e.g. Stock & Watson, 2012b) in a state‐dependent

This paper examines the performance of nonlinear short‐term forecasts of noncausal processes from closed‐form functional predictive density estimators. The processes considered have mixed causal‐noncausal MAR(1,1) dynamics and non‐Gaussian distributions with either finite or infinite variance. The quality of point forecasts is affected by spikes and bubbles in the trajectories of these processes, which

No abstract is available for this article.

In the referenced article the dataset link provided in footnote 4 changed recently. The note is updated to reflect this. 4The dataset can be downloaded from: http://urn.fi/urn:nbn:fi:csc-kata20170601153214969115

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non‐normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand‐pull

Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides more information about the data generation process. Based on the well‐established “linear and nonlinear” modeling framework, a hybrid model is proposed by coupling the vector error correction model (VECM) with artificial intelligence models which consider the cointegration

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive

The popularity of a fashion item depends on its color, shape, texture, and price. For different items (with all attributes identical except color) of a specific product, fashion retailers need to learn consumer color preference and decide their order quantities accordingly to match their products to consumer demand. This study aims to predict consumer color preference using the knowledge learned from

The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors

This paper proposes a Bayesian vector autoregression (BVAR) model with the Kalman filter to forecast the Italian industrial production index in a pseudo real‐time experiment. Minnesota priors are adopted as a general framework, but a different shrinkage pattern is imposed for both the VAR coefficients and the Kalman gain, depending on the informative contribution of each variable investigated at frequency

The primary purpose of this paper is to investigate whether a novel Markov regime‐switching mixed‐data sampling (MRS‐MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump‐driven

Inspired by the commonly held view that international stock market volatility is equivalent to cross‐market information flow, we propose various ways of constructing two types of information flow, based on realized volatility (RV) and implied volatility (IV), in multiple international markets. We focus on the RVs derived from the intraday prices of eight international stock markets and use a heterogeneous

This paper constructs a financial distress prediction model that includes not only traditional financial variables, but also several important corporate governance variables. Using data from Taiwan, the empirical results show that the best in‐sample and out‐of‐sample prediction models should combine the financial variables with the corporate governance variables. Moreover, the prediction accuracy is

We decompose economic uncertainty into "good" and "bad" components according to the sign of innovations. Our results indicate that bad uncertainty provides stronger predictive content regarding future market volatility than good uncertainty. The asymmetric models with good and bad uncertainties forecast market volatility in a better way than the symmetric models with overall uncertainty. The combination

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry‐level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component

Fiscal transparency entails fiscal information disclosure, ensuring good quality of such information, public and parliamentary scrutiny, and the oversight of fiscal risks, while the concept of budget credibility involves an assessment of the extent of conformity of budgetary outturn to the planned forecasts. There are two main channels through which fiscal transparency can affect budget credibility:

We develop a Bayrewriting the error term esian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced‐form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation‐by‐equation

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT‐FCD‐MLGRU

Successful market timing strategies depend on superior forecasting ability. We use a sentiment index model, a kitchen sink logistic regression model, and a machine learning model (least absolute shrinkage and selection operator, LASSO) to forecast 1‐month‐ahead S&P 500 Index returns. In order to determine how successful each strategy is at forecasting the market direction, a “beta optimization” strategy

The effectiveness of road traffic control systems can be increased with the help of a model that can accurately predict short‐term traffic flow. Therefore, the performance of the preferred approach to develop a prediction model should be evaluated with data sets with different statistical characteristics. Thus a correlation can be established between the statistical properties of the data set and the

Primary delays are the driving force behind delay propagation, and predicting the number of affected trains (NAT) and the total time of affected trains (TTAT) due to primary delay (PD) can provide reliable decision support for real‐time train dispatching. In this paper, based on real operation data from 2015 to 2016 at several stations along the Wuhan–Guangzhou high‐speed railway, NAT and TTAT influencing

We investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out‐of‐sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables

We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out‐of‐sample predictions. The algorithms, which are specified in both single‐ and multi‐equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated

This paper finds the yield curve to have a well‐performing ability to forecast the real gross domestic product growth in the USA, compared to professional forecasters and time series models. Past studies have different arguments concerning growth lags, structural breaks, and ultimately the ability of the yield curve to forecast economic growth. This paper finds such results to be dependent on the estimation

This paper focuses on the expectation formation process of professional forecasters by relying on survey data on forecasts regarding gross domestic product growth, consumer price index inflation and 3‐month interest rates for a broad set of countries. We examine the interrelation between macroeconomic forecasts and also the impact of uncertainty on forecasts by allowing for cross‐country interdependencies

Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high‐frequency financial time series. With the improvement in storage capacity and computing power of high‐frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high‐frequency

In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism

We propose moving average threshold heterogeneous autoregressive (MAT‐HAR) models as a novel combination of heterogeneous autoregression (HAR) and threshold autoregression (TAR). The MAT‐HAR has multiple groups of lags of a target series, and a threshold term can appear in each group. The threshold is a moving average of lagged target series, which guarantees time‐varying thresholds and simple estimation

The purpose of this paper is to present the result of a systematic literature review regarding the application and development of forecasting models in the industrial context, especially the context of manufacturing processes and operations management. The study was conducted considering the preparation of an established research protocol to know, discuss, and analyze the main approaches adopted by

No abstract is available for this article.

Large Bayesian vector autoregressions with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data‐driven manner can often substantially improve forecast performance. We propose a computationally efficient method to obtain the optimal hyperparameters based on automatic differentiation, which is

This paper investigates the role of bank credit in predicting US recessions since the 1960s in the context of a bivariate probit model. A set of results emerge. First, credit booms are shown to have strong positive effects in predicting declines in the business cycle at horizons ranging from 6 to 9 months. Second, I propose to isolate the effect of credit booms by identifying the contribution of excess

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic

In this paper, we forecast local currency debt of five major emerging market countries (Brazil, Indonesia, Mexico, South Africa, and Turkey) over the period January 2010 to January 2019 (with an in‐sample period: March 2005 to December 2009). We exploit information from a large set of economic and financial time series to assess the importance not only of “own‐country” factors (derived from principal

Agricultural productivity highly depends on the cost of energy required for cultivation. Thus prior knowledge of energy consumption is an important step for energy planning and policy development in agriculture. The aim of the present study is to evaluate the application potential of multiple linear regression (MLR) and machine learning tools such as support vector regression (SVR) and Gaussian process

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak‐form efficiency. An application of this periodic

This paper develops a dynamic factor model that uses euro area country‐specific information on output and inflation to estimate an area‐wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country‐specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent

Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the ensemble empirical mode decomposition (EEMD) technique to decompose six different categories of agricultural futures

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. Dynamic factors are then 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

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight‐choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short‐ and long‐term effects caused by extreme events, we extend the standard GARCH‐MIDAS model to characterize

Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The Cholesky–artificial neural networks specification here presented provides a twofold advantage for this topic. On the one hand, the use of the Cholesky decomposition ensures positive definite forecasts. On the other hand, the implementation of artificial neural networks allows us to specify nonlinear