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1.
Viviana Fernandez 《Journal of forecasting》2008,27(7):637-648
This article applies two novel techniques to forecast the value of US manufacturing shipments over the period 1956–2000: wavelets and support vector machines (SVM). Wavelets have become increasingly popular in the fields of economics and finance in recent years, whereas SVM has emerged as a more user‐friendly alternative to artificial neural networks. These two methodologies are compared with two well‐known time series techniques: multiplicative seasonal autoregressive integrated moving average (ARIMA) and unobserved components (UC). Based on forecasting accuracy and encompassing tests, and forecasting combination, we conclude that UC and ARIMA generally outperform wavelets and SVM. However, in some cases the latter provide valuable forecasting information that it is not contained in the former. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
2.
Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first‐generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second‐generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
3.
Suleyman Gokcan 《Journal of forecasting》2000,19(6):499-504
ARCH and GARCH models are substantially used for modelling volatility of time series data. It is proven by many studies that if variables are significantly skewed, linear versions of these models are not sufficient for both explaining the past volatility and forecasting the future volatility. In this paper, we compare the linear(GARCH(1,1)) and non‐linear(EGARCH) versions of GARCH model by using the monthly stock market returns of seven emerging countries from February 1988 to December 1996. We find that for emerging stock markets GARCH(1,1) model performs better than EGARCH model, even if stock market return series display skewed distributions. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
4.
Empirical mode decomposition (EMD)‐based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD‐based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD‐based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD‐based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy. 相似文献
5.
António Rua 《Journal of forecasting》2011,30(7):666-678
It has been acknowledged that wavelets can constitute a useful tool for forecasting in economics. Through a wavelet multi‐resolution analysis, a time series can be decomposed into different timescale components and a model can be fitted to each component to improve the forecast accuracy of the series as a whole. Up to now, the literature on forecasting with wavelets has mainly focused on univariate modelling. On the other hand, in a context of growing data availability, a line of research has emerged on forecasting with large datasets. In particular, the use of factor‐augmented models have become quite widespread in the literature and among practitioners. The aim of this paper is to bridge the two strands of the literature. A wavelet approach for factor‐augmented forecasting is proposed and put to test for forecasting GDP growth for the major euro area countries. The results show that the forecasting performance is enhanced when wavelets and factor‐augmented models are used together. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
6.
Based on the concept of ‘decomposition and ensemble’, a novel ensemble forecasting approach is proposed for complex time series by coupling sparse representation (SR) and feedforward neural network (FNN), i.e. the SR‐based FNN approach. Three main steps are involved: data decomposition via SR, individual forecasting via FNN and ensemble forecasting via a simple addition method. In particular, to capture various coexisting hidden factors, the effective decomposition tool of SR with its unique virtues of flexibility and generalization is introduced to formulate an overcomplete dictionary covering diverse bases, e.g. exponential basis for main trend, Fourier basis for cyclical (and seasonal) features and wavelet basis for transient actions, different from other techniques with a single basis. Using crude oil price (a typical complex time series) as sample data, the empirical study statistically confirms the superiority of the SR‐based FNN method over some other popular forecasting models and similar ensemble models (with other decomposition tools). Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
7.
Gongyue Jiang;Gaoxiu Qiao;Lu Wang;Feng Ma; 《Journal of forecasting》2024,43(6):2378-2398
From the cross-market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data-driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH-type models with jumps, and the forecasting effects of five non-parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross-market research. 相似文献
8.
This paper presents an analysis of shift-contagion in energy markets, testing whether linkages between returns in energy markets increase during crisis periods. The research presented herein demonstrates how common movement between energy markets increases due to (i) shift-contagion across energy markets, reflected by structural transmission of shocks across markets and (ii) larger common shocks operating through standard cross-market interdependences. A regime-switching model was developed to detect shift-contagion across energy markets. In the approach adopted herein, the occurrence of shift-contagion is endogenously estimated rather than being exogenously assigned. The results show that shift-contagion has been a major feature of energy markets over the last decade. Evidence is presented which demonstrates that the linkages between energy markets do not appear to be stable. These results are remarkably accurate for forecasting Brent and natural gas for horizons for up to 50 days. Conversely, for WTI (West Texas Intermediate oil) and coal, the model performs well only for forecasting very short horizons (up to 20 days). For all products, the model shows significant biases for long horizons. 相似文献
9.
This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profits systematically in a betting market. We present the results of an empirical study that uses data from 678–837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting‐based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule‐based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state‐owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profits if punters exploited the information from prediction markets and bet only on a selected number of games. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
10.
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 component and partial least squares approaches), but also create “global” predictors by combining the country-specific variables across the five emerging economies. We find that, while information on own-country factors can outperform the historical average model, global factors tend to produce not only greater statistical and economic gains, but also enhance market timing ability of investors, especially when we use the target variable (bond premium) approach under the partial least squares method to extract our factors. Our results have important implications not only for fund managers but also for policymakers. 相似文献
11.
Forecasts of interest rates for different maturities are essential for forecasts of asset prices. The growth of derivatives markets coupled with the development of complex theories of the term structure of interest rates have provided forecasters with a rich array of variables for predicting interest rates and yield spreads. This paper extends previous work on forecasting future interest rates and yield spreads using market data for T-bills, T-Notes, and Treasury Bond spot and futures contracts. The information conveyed in technical models that use market data is also assessed, using a recent innovation in interest rate modelling, the maximum smoothness approach. Forecasts from this model are compared with predicted yields and yield spreads derived from futures prices as well as with those of the random walk model. The results show some evidence of market segmentation, with more arbitrage evident for nearby maturities. Market participants appear to show a greater degree of consensus on short-term interest rates than on longer-term interest rates. There is some indication that forecasts from the futures markets are marginally better than those provided by those of the maximum-smoothness approach, consistent with the informational advantages of futures markets. Finally, futures and maximum-smoothness market forecasts are shown to outperform those of the random walk model.© 1997 John Wiley & Sons, Ltd. 相似文献
12.
In this paper we assess opinion polls, prediction markets, expert opinion and statistical modelling over a large number of US elections in order to determine which perform better in terms of forecasting outcomes. In line with existing literature, we bias‐correct opinion polls. We consider accuracy, bias and precision over different time horizons before an election, and we conclude that prediction markets appear to provide the most precise forecasts and are similar in terms of bias to opinion polls. We find that our statistical model struggles to provide competitive forecasts, while expert opinion appears to be of value. Finally we note that the forecast horizon matters; whereas prediction market forecasts tend to improve the nearer an election is, opinion polls appear to perform worse, while expert opinion performs consistently throughout. We thus contribute to the growing literature comparing election forecasts of polls and prediction markets. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
13.
Yi Hong;Xiaofan Xu;Chen Yang; 《Journal of forecasting》2024,43(8):3104-3127
This paper investigates the high-frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance () and realized bi-power variations () are constructed at various frequencies by virtue of 1-minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in-sample explanatory power of the realized variance dynamics and the out-of-sample prediction of realized variance across different horizons, leading to four new HAR-RV-type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium-term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in-sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out-of-sample periods. Finally, these results are robust toward various market/model setups, over day- and night-trading hours, and across a range of prediction horizons and relative to prediction benchmarks. 相似文献
14.
In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies. 相似文献
15.
M. A. Kaboudan 《Journal of forecasting》1999,18(5):345-357
Based on the standard genetic programming (GP) paradigm, we introduce a new probability measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) from GP runs. One value belongs to a subject series, while the other belongs to the same series after it is randomly shuffled. Theoretically, the boundaries of the measure are between zero and 100, where zero characterizes stochastic processes while 100 typifies predictable ones. To evaluate its performance, we first apply it to experimental data. It is then applied to eight Dow Jones stock returns. This measure may reduce model search space and produce more reliable forecast models. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献
16.
Matteo Bonato;Oguzhan Cepni;Rangan Gupta;Christian Pierdzioch; 《Journal of forecasting》2024,43(2):456-472
We analyze the predictive value of (the surprise component of) state-level business applications, as a proxy of local investor sentiment, for the state-level realized US stock-market volatility. We use high-frequency data for the period from September 2011 to October 2021 to compute realized volatility. Using an extended version of the popular heterogeneous autoregressive realized volatility model and accounting for the possibility that users of forecasts have an asymmetric loss function, we show that business applications tend to have predictive value for realized state-level stock-market volatility, as well as for upside (“good”) and downside (“bad”) realized volatility, for users of forecasts who suffer a larger loss from an underprediction of realized volatility than from an overprediction of the same (absolute) seize, after controlling for realized moments (realized skewness, realized kurtosis, realized jumps, and realized tail risks). We also highlight that the COVID-19 period is a major driver of our empirical results. 相似文献
17.
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 A-share IPO markets indicates that our method is very efficient in forecasting monthly IPO volumes and detecting hot/cold issue markets. The first-day IPO return is positively correlated with the IPO volume in a hot issue market but negatively correlated with the IPO volume in a cold issue market, in both the U.S. and Chinese IPO markets. However, the average first-day return in the previous hot issue market has a significant positive impact on the current IPO volume for only the U.S. IPO market. Our approach helps to more accurately model and understand the behavior of hot/cold IPO issue markets. 相似文献
18.
The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange‐traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well‐known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized‐AutoContouR (G‐ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process. 相似文献
19.
This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting. 相似文献
20.
This paper examines the information content of implied volatility for crude oil options as it relates to future realized volatility. Using data for the period 1996 to 2011 we find that implied volatility is an effective predictor of the month‐ahead realized volatility. We show that implied volatility subsumes the information content of contemporaneous volatility, and it contains incremental information on future volatility after controlling for contemporaneous volatility. Furthermore, incorporating risk‐neutral skewness, and especially kurtosis, improves the forecasting of realized volatility. Overall, the association between implied volatility and month‐ahead realized volatility is consistent with evidence documented for other asset classes, leading us to conclude that implied volatility serves as a reasonable proxy for expected volatility. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献