Point process models, such as Hawkes and recursive models, have recently been shown to offer improved accuracy over more traditional compartmental models for the purposes of modeling and forecasting the spread of disease epidemics. To explicitly test the performance of these two models in a real-world and ongoing epidemic, we compared the fit of Hawkes and recursive models to outbreak data on Ebola

This work marks a thorough analysis of a confidential real-time dataset consisting of the Eurosystem/ECB staff macroeconomic projections since their existence. By applying techniques widely employed in the literature of forecast evaluation we examine their statistical properties with a special emphasis on optimality and rationality. Long-term GDP projections are biased (tendency to over-predict), do

In this article we propose an extension of singular spectrum analysis for interval-valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set-valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent

Motivated by the application to German interest rates, we propose a time-varying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far

No abstract is available for this article.

Estimation of the value at risk (VaR) requires prediction of the future volatility. Whereas this is a simple task in ARCH and related models, it becomes much more complicated in stochastic volatility (SV) processes where the volatility is a function of a latent variable that is not observable. In-sample (present and past values) and out-of-sample (future values) predictions of that unobservable variable

In the event studies, the accuracy of the abnormal returns assessment is highly dependent on the accuracy of the preceding expected return model. If the expected return model is inadequate, there is a possibility that a part of returns is labeled as abnormal returns even though they are not. Currently, we have a variety of options to set up an expected return model. To obtain unbiased abnormal returns

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH-MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic

This study proposes a novel Markov regime-switching negative binomial generalized autoregressive conditional heteroskedasticity model for analyzing count data time series. We develop a likelihood-based method for parameter estimation and give the one-step-ahead forecasting algorithms for the mean, variance, and quantiles. An empirical analysis of both the U.S. initial public offering (IPO) and Chinese

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. First, 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. Second, a multi-objective fitness function

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR

Few existing literatures used the text information of the public opinions as the input index for volatility forecasting. This paper uses the text comment information of stockholders to construct a text sentiment factor that integrates the influence of comments and then combines other transaction information on volatility forecasting based on high-frequency finance data with the deep learning model

No abstract is available for this article.

Relying on the uncovered equity parity (UEP), we formulate a predictive model that links movements in exchange rate to stock return differential between the domestic market and the foreign (US) market. We also test for any probable asymmetric relationship between the two variables while also accounting for the role of observed common (global) factor such as oil price. We find a positive relationship

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

In this work, we present our findings of the so-called CIR#, which is a modified version of the Cox, Ingersoll, and 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

Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM2.5 concentration prediction and its application in health effects and economic loss assessment

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

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

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

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

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 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 paper examines the recession probabilities for the Eurozone along four different dimensions: First, we identify the best performing indicators for a recession within the next 12 months based on 43 underlying single variables and their different transformations in a benchmark model. We find that a modified version of the yield curve incorporating the shadow interest rate removes the downward rigidity

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

We forecast macroeconomic and financial uncertainties of the US over the period of 1960:Q3 to 2018:Q4, based on a large data set 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

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 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

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

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.

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

No abstract is available for this article.

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