For improving forecasting accuracy and trading performance, this paper proposes a new multi‐objective least squares support vector machine with mixture kernels to forecast asset prices. Firstly, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data‐driven procedure. Secondly, a multi‐objective fitness function

Gold as a vital hedging asset plays increasing critical roles in risk management during turmoil macroeconomic environments. For the massive and indistinct impactors of gold price volatility, this paper tries to investigate whether the short‐ and long‐term asymmetry, extreme observations, and jump components in past gold volatility help to obtain higher forecasting accuracy in future volatility from

This paper investigates whether and how investor sentiment affects stock market volatility forecasting from a nonlinear theory perspective. With the use of a novel dataset that contains massive articles about stock market analysis obtained from a Chinese investors' community, we construct four sentiment indices to measure investor sentiment by applying textual analysis techniques. Differing from the

Financial fraud has great negative impact on capital markets. It misleads investors and the government and will hurt the development of the capital market in China. To detect corporate fraud, an artificial intelligence‐based method is proposed to evaluate the fraud risk. A new model for assessing the fraud risk of listed companies in China is put forward. The proposed approach collects multisource

In this work we present our findings of the so‐called CIR#, which is a modified version of the Cox, Ingersoll & Ross (CIR) model, turned into a forecasting tool for any term structure. The main feature of the CIR# model is its ability to cope with negative interest rates, cluster volatility and jumps. By considering a dataset composed of money market interest rates during turmoil and calmer periods

This paper forecasts the daily Baltic Dry Index (BDI) using time series and machine learning methods. Significant business cycles and freight rate volatility present in the ocean‐going shipping industry, make the ability to forecast freight rates and cycles extremely important for business decisions. Data‐driven model selection based on data characteristics is performed through ARIMA, Fractional ARIMA

This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR

This manuscript extends the literature on the application of geometric Brownian motion. Forecasted drift and diffusion terms estimated separately and recursively are plugged into the framework to forecast S&P500 index values. Expected index values are estimated from 100,000 simulated index values and probabilities. The results of comparing expected index values with actual values indicate that although

Financial markets are interconnected and fragile making them vulnerable to systemic contagion, and measuring this risk is crucial for regulatory responsiveness. This study introduces a new set of measures for systemic risk using a copula‐based (CB) estimation method with a focus on U.S. Bank Holding Companies. Unlike most of the prevailing systemic risk measures, CB methodology relies on balance sheet

This paper extends the hybrid multivariate exponentially weighted moving average (EWMA) approach to incorporate realized variance for conditional covariance matrix forecast. The proposed estimator employs a realized variance‐based EWMA specification to estimate the conditional variance of returns and a standard return‐based EWMA specification to estimate the correlation between each pair of returns

An underlying assumption in Multivariate Singular Spectrum Analysis (MSSA) is that the time series are governed by a linear recurrent continuation. However, in the presence of a structural break, multiple series can be transferred from one homogeneous state to another over a comparatively short time breaking this assumption. As a consequence, forecasting performance can degrade significantly. In this

In the world of finance, the volatility of asset prices plays a crucial role, for example, in portfolio optimization or the valuation of derivatives. Macroeconomic news announcements are among the most important factors that influence volatility in financial markets. This paper focuses on the effect of scheduled macroeconomic news announcements on the realized volatility of the most traded currency

This paper uses non‐linear 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 a.m.

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by applying topological metrics of correlation networks to the challenge of financial forecasting. These network‐based metrics are retrieved with the help of graph theory and quantify the interconnectedness of financial assets. In this

This paper analyzes the prediction performance of human resources (HR) variables in corporate failure modeling. We define corporate failure as a two‐phase process from financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two‐phase

This paper shows that lagged information transmission between industry portfolio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period 1963–2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long‐term common factor between industry portfolio and market cumulative returns, has

We investigate the information content of five uncertainty indices for the US natural gas futures volatility forecasting. Our investigation is based on the GARCH‐MIDAS framework. The in‐sample outcomes suggest that most of uncertainty indices have a crucial effect on natural gas futures volatility. And the out‐of‐sample prediction results indicate that the geopolitical risk (GPR) and equity market

No abstract is available for this article.

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem of model uncertainty is assessed explicitly by concentrating on specification selection based on the quality of short‐term out‐of‐sample forecasts (1 to 12 months ahead) for the price of wheat, soybeans and corn. Univariate and multivariate

With the development of information technology, online transactions and e‐commerce are gradually replacing conventional consumption patterns. To obtain a competitive advantage, industries proactively engage in digital transformations and the management of e‐commerce platforms. Faced with changes in market patterns, e‐commerce channels and online advertising firms hope to extend users' website browsing

This paper examines the prudential role and the effectiveness of banking regulation in EU. Using a unique sample of banks' balance and off‐balance sheet data, we focus on the capital requirements (tier1) and the liquidity characteristics of the banking sector. The latter factor is accounted for by the Basel III net stable funding ratio (NSFR) that was launched on 2010 with effect on 2018 and refers

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 characterize the time series of commodity prices, cryptocurrency exchange rates, and other financial and macroeconomic variables. The convolution model is a structural mixture of independent

This paper investigates the relation between yield curve and macroeconomic factors for 10 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

We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent 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

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. The empirical assessment comprises German stocks in both calm and

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

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

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

No abstract is available for this article.

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

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

Professional forecasters can rely on econometric models, on their personal expertise or on both. To accommodate for adjustments to model forecasts, this paper proposes to use two stage least squares (TSLS) (and not ordinary least squares [OLS]) for the familiar Mincer–Zarnowitz regression when examining bias in professional forecasts, where the instrumental variable is the consensus forecast. An illustration

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

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

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 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 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

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

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

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

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

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

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

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 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

No abstract is available for this article.