This study builds on two strands of the literature regarding exchange rates—developing methods to forecast them and attempting to find a link between exchange rates and macroeconomic fundamentals (i.e., addressing so called “exchange rate disconnect puzzle”). We propose looking separately at its global component (common for all the currencies) and the local component (country‐specific one) instead

We introduce new methods of filtering and forecasting for the causal‐noncausal convolution model. This model represents the dynamics of stationary processes with local explosions, such as spikes and bubbles, which characterise the time series of commodity prices, cryptocurrency exchange rates and other financial and macroeconomic variables. The convolution model is a structural mixture of independent

In this paper, we provide a thorough study on the relevance of liquidity‐adjusted Value‐at‐Risk (LVaR) and Expected Shortfall (LES) forecasts. We measure additional liquidity of an asset via the difference between its respective bid and ask prices and we assess the non‐normality of bid‐ask spreads, especially in turbulent market times.

This study explores the forecasting ability of jump, jump intensity, and leverage effect for an emerging futures market, China's crude oil futures market, using different kinds of HAR‐type models. From an in‐sample perspective, we find that the HAR components, monthly leverage effect, jump size, and jump intensity have positive effects on future oil volatility. Moreover, out‐of‐sample results show

This study makes use of specific econometric modelling methodologies to forecast US outbound travelling flows to certain destinations: Europe, Caribbean, Asia, Central America, South America, Middle East, Oceania and Africa, spanning the period 2000–2019 on a monthly basis. Both univariate (jointly with business conditions) and multivariate models are employed, whereas out‐of‐sample forecasts are generated

This paper investigates the relation between yield curve and macroeconomic factors for ten emerging sovereign bond markets using the sample from January 2006 to April 2019. To this end, the diffusion indices obtained under four categories (global variables, inflation, domestic financial variables, and economic activity) are incorporated by estimating dynamic panel data regressions together with the

A new link prediction algorithm (ZHA), based on comprehensive influence of predicting nodes and neighbor nodes to improve the accuracy and applicability of link prediction for complex networks, was proposed. Taking the comprehensive influence of predicting nodes and neighbor nodes into account, the new algorithm was constructed on the basis of the information of nodes in complex networks. ZHA was applied

Professional forecasters of economic data are remunerated based on accuracy and positive publicity generated for their firms. This remuneration structure incentivizes them to stick to the median forecast but also to make bold forecasts when they perceive to have superior private information. We find that skewness in the distribution of expectations, potentially created by bold forecasts, predicts economic

Germany's economic composition is heterogenous across regions, which makes regional economic projections based on German gross domestic product (GDP) growth unreliable. In this paper, we develop forecasting models for Baden‐Württemberg's economic growth, a regional economy that is dominated by small‐ and medium‐sized enterprises with a strong focus on foreign trade. For this purpose, we evaluate the

We propose a copula‐based periodic mixed frequency generalized autoregressive (GAS) framework in order to model and forecast the intraday exposure conditional value at risk (ECoVaR) for an intraday asset return and the corresponding market return. In particular, we analyze GAS models that account for long‐memory‐type of dependencies, periodicities, asymmetric nonlinear dependence structures, fat‐tailed

We use heterogeneous autoregression (HAR) and two related HAR extension models to examine volatility forecasting performances before and after the launch of night trading sessions in the Shanghai Futures Exchange (SHFE) gold futures market. To capture fluctuations from external information and volatility of realized volatility (RV), we incorporate the trading volume and jumping into the HAR‐V‐J model

This study explores the possibilities of applying Google Trends to exchange rate forecasting. Specifically, we construct a sentiment index by using Google Trends to capture market sentiment in Japan and the United States. We forecast the USD/JPY rates using three structural models and two autoregressive models and examine whether our sentiment index can improve the predictive power of these models

Croston and the other related methods, such as Syntetos ‐Boylan approximation (SBA), are the most popular methods recommended in the literature for intermittent demand forecasting. However, these conventional methods are not considered suitable in inventory obsolescence as they do not update their forecast in the periods of zero demand. Therefore, in order to add to the methods suitable for the inventory

This paper uses nonlinear methodologies to follow the synchronously reported relationship between the Nordic/Baltic electric daily spot auction prices and geographical relevant wind forecasts in MWh from early 2013 to 2020. It is a well‐known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11.00 a.m

In this paper, we analyse theoretically the distributional properties and the forecastability of cross‐sectional momentum (CSM) and time series momentum (TSM) returns. By decomposing these returns into their fundamental building blocks, we expose their structural similarities and differences that shed valuable insights into the conditions under which one outperforms the other. Considering in detail

This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed‐Data Sampling (GARCH‐MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH)

This study investigates whether the Bank of Japan's (BOJ's) interval forecast publications have an added value relative to its point forecast publications by focusing on the range forecasts released by its policy board members. We find that the width of the BOJ's range forecasts has additional information for predicting inflation over point forecasts for the policy decision periods of 12–18 months

This study presents a comprehensive analysis of artificial intelligence (AI) techniques to predict healthcare expenditure per capita (pcHCE) in Turkey. Well‐known AI techniques such as random forest (RF), artificial neural network (ANN), multiple linear regression (MLR), support vector regression (SVR), and relevance vector machine (RVM) were used to forecast pcHCE. Twenty‐nine years of historical

Apart from the percentage change in the price of crude oil, there is a growing tradition of using various nonlinear transformations of the price of crude oil to forecast real gross domestic product growth rates, equity returns, inflation and other macroeconomic variables. This study attempts to quantify the additional potential predictive power afforded by crude oil price volatility relative to widely

This paper aims to accurately forecast US stock market volatility by using international market volatility information flows. The results show the significant ability of the combined international volatility information to predict US stock volatility. The predictability is found to be both statistically and economically significant. Furthermore, in this framework, we compare the performance of a large

This paper proposes and implements methods for determining whether incorporating technical trading rules accurately forecasts systemic risk and improves the performance of out‐of‐sample portfolios. The proposed methodology considers various trading rules for forecasting and addressing potential systemic risk in portfolio selection problems. The method incorporates major trading rules as early warning

Buildings are typically equipped with smart meters to measure electricity demand at regular intervals. Smart meter data for a single building have many uses, such as forecasting and assessing overall building performance. However, when data are available from multiple buildings, there are additional applications that are rarely explored. For instance, we can explore how different building characteristics

We forecast monthly Value at Risk (VaR) and Conditional Value at Risk (CVaR) using option market data and four different econometric techniques. Independently from the econometric approach used, all models produce quick to estimate forward‐looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever‐changing

We forecast macroeconomic and financial uncertainties of the USA over the period of 1960:Q3 to 2018:Q4, based on a large dataset of 303 predictors using a wide array of constant‐parameter and time‐varying models. We find that uncertainty is indeed forecastable, but while accurate point forecasts can be achieved without incorporating time variation in the parameters of the small‐scale models for macroeconomic

This paper examines the recession probability in the Eurozone within the next 12 months at the zero lower bound (ZLB) and explores two new perspectives: A revised measure of the traditional term spread and a modification to detect unstable dynamics driven by animal spirits. We find that the yield curve largely lost its forecasting ability at the ZLB. To remove the downward rigidity of short‐term rates

This paper shows that shocks to the equity capital ratio of financial intermediaries (CRFI) have predictive ability for stock realized volatility, from both in‐sample and out‐of‐sample perspectives. The revealed predictability is also of economic significance, in that it examines the performance of portfolios constructed on the basis of CRFI forecasts of stock volatility. Robustness test results suggest

This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regime‐switching GARCH‐MIDAS models. The in‐sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market. Moreover, the novel model, which

We consider a situation in which the forecaster has available M individual forecasts of a univariate target variable. We propose a 3‐step procedure designed to exploit the interrelationships among the M forecast‐error series (estimated from a large time‐varying parameter VAR model of the errors, using past observations) with the aim of obtaining more accurate predictions of future forecast errors.

The aim of this paper is twofold. Firstly, we test the rationality of survey‐based and market‐based inflation expectations. Secondly, we investigate whether they indicate a different performance of the central bank in anchoring inflation expectations. Briefly, this paper verifies if inflation expectations’ proxies display the same features regarding rationality and anchoring. Using data from the Brazilian

The Realized generalized autoregressive conditional heteroskedasticity (GARCH) model proposed by Hansen is often applied to forecast volatility in high‐frequency financial data. It is frequently found, however, that the distribution of the estimated residuals from Realized GARCH models has peak fat‐tail characteristics. Considering this feature may be a result of neglected additive outliers (AOs) and

No abstract is available for this article.

This paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe

We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger‐causes the other; in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology example, we find that the sea surface temperature Granger‐causes sea‐level atmospheric

This study provides a retrospective of the Journal of Forecasting (JoF) between 1982 and 2019. Evidence shows a considerable increase in JoF's productivity and influence since 1982. For example, a JoF article, on average, receives more than 18 citations, and contributions from 65 nations appear in the journal. All articles have a forecasting implication, but JoF encourages a wide range of contexts

No abstract is available for this article.

One of the major motivations for the analysis and modeling of time series data is the forecasting of future outcomes. The use of interval forecasts instead of point forecasts allows us to incorporate the apparent forecast uncertainty. When forecasting count time series, one also has to account for the discreteness of the range, which is done by using coherent prediction intervals (PIs) relying on a

We introduce the tensor auto‐regressive (TAR) model for modeling time series data, which is found to be robust to model misspecification, seasonality, and nonlinear trends. We develop a parameter estimation algorithm for the proposed model by using the 𝑡‐product, which allows us to model a three‐dimensional block of parameters. We use the fast Fourier transform, which allows for efficient and parallelizable

Accurate forecasts of mortality rates are essential to various types of demographic research such as population projection, and the pricing of insurance products such as pensions and annuities. Recent studies have considered a spatial temporal autoregressive (STAR) model for the mortality surface, where mortality rates for each age depend (temporally) on their historical values as well as (spatiality)

This paper attempts to answer the question of whether the principle of parsimony can be applied to interval forecasting for multivariate series. Toward that end, this paper proposes the block bootstrap prediction intervals based on parsimonious first‐order vector autoregression. The new intervals generalize standard bootstrap prediction intervals by allowing for serially correlated prediction errors

No abstract is available for this article.

This paper generalizes the exponentially weighted maximum likelihood (EWML) procedure to account for volatility and higher moment dynamics of the returns distribution. Prior research uses EWML to forecast value at risk (VaR) by assuming daily equity returns following a scaled t distribution. This approach does not capture the significant degree of skewness inherent in the data, which potentially leads

This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (Journal of Applied Econometrics, 2013, 28(2), 204–230). 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

Negative interest rates have been present in various marketplaces since mid‐2014, following the negative interest rate policy (NIRP) adopted by the European Central Bank to raise economic growth. The well‐known historical approach (HA) appears to be a good resource. By tweaking the HA, we derive a very tractable data‐driven tool that allows practitioners to generate yield‐curve distributions at future

We evaluate the forecasting performance of time series models for the production side of gross domestic product (GDP)—that is, for the sectoral real value‐added series summing up to aggregate output. We focus on two strategies to model a large number of interdependent time series simultaneously: a Bayesian vector autoregressive model (BVAR) and a factor model structure; and compare them to simple aggregate

This paper presents a factor‐based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to forecast the CFSI out‐of‐sample. Our factor models outperform both the random walk and the autoregressive

Predicting 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, a trading

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,

In this paper, we consider Google trends (G‐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 such as G‐trends that could induce changes in the prices of stocks. Thus, we construct a predictive

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.

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

No abstract is available for this article.

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 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