|
on Forecasting |
| By: | Alexei V. Egorov; Yongmiao Hong; Haitao Li |
| Abstract: | Most existing empirical studies on affine term structure models (ATSMs) have mainly focused on in-sample goodness-of-fit of historical bond yields and ignored out-of-sample forecast of future bond yields. Using an omnibus nonparametric procedure for density forecast evaluation in a continuous-time framework, we provide probably the first comprehensive empirical analysis of the out-of-sample performance of ATSMs in forecasting the joint conditional probability density of bond yields. We find that although the random walk models tend to have better forecasts for the conditional mean dynamics of bond yields, some ATSMs provide better forecasts for the joint probability density of bond yields. However, all ATSMs considered are still overwhelmingly rejected by our tests and fail to provide satisfactory density forecasts. There exists room for further improving density forecasts for bond yields by extending ATSMs. r 2005 Elsevier B.V. All rights reserved. |
| Keywords: | Density forecast; Affine term structure models; Probability integral transform; Financial risk management; Value at risk; Fixed-income portfolio management |
| JEL: | C4 C5 G1 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002064&r=for |
| By: | Christiane Baumeister; Pierre Guérin; Lutz Kilian |
| Abstract: | The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial and energy market data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models can be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred mixed-data sampling (MIDAS) model reduces the mean-squared prediction error by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 80 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time vector autoregressive forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil. |
| Keywords: | Econometric and statistical methods, International topics |
| JEL: | C53 G14 Q43 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:14-11&r=for |
| By: | Croonenbroeck, Carsten; Møller Dahl, Christian |
| Abstract: | We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important stylized facts or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology. -- |
| Keywords: | Censored Regression,Wind Energy,Forecasting |
| JEL: | C34 E27 Q47 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:euvwdp:351&r=for |
| By: | Plakandaras, Vasilios (Democritus University of Thrace, Department of Economics); Gupta, Rangan (Pretoria University, Department of Economics); Papadimitriou, Theophilos (Democritus University of Thrace, Department of Economics); Gogas, Periklis (Democritus University of Thrace, Department of Economics) |
| Abstract: | The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications. |
| Keywords: | house prices forecasting; machine learning; Support Vector Regression; |
| JEL: | C32 |
| Date: | 2014–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:ris:duthrp:2014_010&r=for |
| By: | Ai Han; Yanan He; Yongmiao Hong; Shouyang Wang |
| Abstract: | We propose two parsimonious autoregressive conditional interval-valued (ACI) models to forecast crude oil prices. The ACI models are a new class of time series models proposed by Han et al. (2009). They can characterize the dynamics of economic variables in both level and range of variation in a unified framework and hence facilitate informative economic analysis. A minimum DK-distance estimation method can also simultaneously utilize rich information of level and range contained in interval-valued observations, thus enhancing parameter estimation efficiency and model forecasting ability. Compared to other existing methods, the ACI models deliver significantly better out-ofsample forecasts, not only for interval-valued prices but also for point-valued highs, lows, and ranges. In particular, we find that the oil price range information is more valuable than the oil price level information in forecasting crude oil prices, which is consistent with observed facts of price movements in crude oil markets. We also find that speculation has predictive power for oil prices in our interval framework.. |
| Keywords: | Interval-valued data, crude oil price, ACI model, minimum DK-distance estimation, range |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002040&r=for |
| By: | Yongmiao Hong; Haitao Li; Feng Zhao |
| Abstract: | It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability density of exchange rates given that the probability density forecasts are often needed in many applications in economics and finance.We first develop a nonparametric portmanteau test for optimal density forecasts of univariate time series models in an out-of-sample setting and provide simulation evidence on its finite sample performance. Then we conduct a comprehensive empirical analysis on the out-of-sample performances of a wide variety of nonlinear time series models in forecasting the intraday probability densities of two major exchange rates—Euro/Dollar and Yen/Dollar. It is found that some sophisticated time series models that capture time-varying higher order conditional moments, such as Markov regimeswitching models, have better density forecasts for exchange rates than random walk or modified random walk with GARCH and Student-t innovations. This finding dramatically differs from that on mean forecasts and suggests that sophisticated time series models could be useful in out-of-sample applications involving the probability density. |
| Keywords: | Density forecasts; GARCH; Intraday exchange rate; Jumps; Maximum likelihood estimation;Nonlinear time series; Out-of-sample forecasts; Regime-switching |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002081&r=for |
| By: | Ying Chen; Linlin Niu |
| Abstract: | We propose an Adaptive Dynamic Nelson-Siegel (ADNS) model to adaptively forecast the yield curve. The model has a simple yet flexible structure and can be safely applied to both stationary and nonstationary situations with different sources of change. For the 3- to 12-months ahead out-of-sample forecasts of the US yield curve from 1998:1 to 2010:9, the ADNS model dominates both the dynamic Nelson-Siegel (DNS) and random walk models, reducing the forecast error measurements by between 30 and 60 percent. The locally estimated coefficients and the identified stable subsamples over time align with policy changes and the timing of the recent financial crisis. |
| Keywords: | Yield curve, term structure of interest rates, local parametric models, forecasting |
| JEL: | C32 C53 E43 E47 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002047&r=for |
| By: | Carlo A. Favero; Linlin Niu; Luca Sala |
| Abstract: | This paper addresses the issue of forecasting the term structure. We provide a unified state-space modelling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact of two modelling choices, namely the imposition of no-arbitrage restrictions and the size of the information set used to extract factors, on the forecasting performance. Using US yield curve data, we find that both no-arbitrage and large info help in forecasting but no model uniformly dominates the other. No-arbitrage models are more useful at shorter horizon for shorter maturities. Large information sets are more useful at longer horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting. |
| Keywords: | Yield curve, term structure of interest rates, forecasting, large data set, factor models |
| JEL: | C33 C53 E43 E44 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002005&r=for |
| By: | Carlo A. Favero; Linlin Niu; Luca Sala |
| Abstract: | This paper addresses the issue of forecasting the term structure.We provide a unified state-space modeling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact of two modeling choices, namely the imposition of no-arbitrage restrictions and the size of the information set used to extract factors, on the forecasting performance. Using US yield curve data, we find that both no-arbitrage and large information help in forecasting but no model uniformly dominates the other. No-arbitrage models are more useful at shorter horizon for shorter maturities. Large information sets are more useful atlonger horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting. |
| Keywords: | Yield curve, terms tructure of interest rates, forecasting, large data set,� factor�models |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002125&r=for |
| By: | Zongwu Cai; Linna Chen; Ying Fang |
| Abstract: | This paper models the return series of USD/CNY exchange rate by considering the conditional mean and conditional volatility simultaneously. An index type functional-coefficient model is adopted to model the conditional mean part and a GARCH type model with a policy dummy variable is applied to the conditional volatility model. We show that the government policy indeed has an impact on the exchange rate dynamic. To evaluate the out-of-sample forecasting ability, a prediction interval is computed by employing nonparametric conditional quantile regression. Our method outperforms other popular models in terms of various criteria. |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002135&r=for |
| By: | Eric Gaus (Ursinus College); Arunima Sinha (Santa Clara Univerisity) |
| Abstract: | We find that investors' expectations of U.S. nominal yields, at different maturities and forecast horizons, exhibit significant time-variation during the Great Moderation. Nominal zero-coupon bond yields for the U.S. are used to fit the yield curve using a latent factor model. In the benchmark model, the VAR process used to characterize the conditional forecasts of yields has constant coefficients. The alternative class of models assume that investors use adaptive learning, in the form of a constant gain algorithm and different endogenous gain algorithms, which we propose here. Our results indicate that incorporating time-varying coefficients in the conditional forecasts of yields lead to large improvements in forecasting performance, at different maturities and horizons. These improvements are even more substantial during the Great Recession. We conclude that our results provide strong empirical motivation to use the class of adaptive learning models considered here, for modeling potential investor expectation formation in periods of low and high volatility, and the endogenous learning model leads to significant improvements over the benchmark in periods of high volatility. A policy experiment, which simulates a surprise shock to the level of the yield curve, illustrates that the conditional forecasts of yields implied by the learning models do significantly better at capturing the response observed in the realized yield curve, relative to the constant-coefficients model. Furthermore, the endogenous learning algorithm does well at matching the time-series patterns observed in expected excess returns implied by the Survey of Professional Forecasters. |
| Keywords: | Adaptive learning, Investor beliefs, Monetary policy, Excess returns |
| JEL: | E52 D83 |
| Date: | 2014–04–10 |
| URL: | http://d.repec.org/n?u=RePEc:urs:urswps:14-02&r=for |
| By: | Zongwu Cai; Linna Chen; and Ying Fang |
| Abstract: | This paper models the return series of USD/CNY exchange rate by considering the conditional mean and conditional volatility simultaneously. An index type functional-coefficient model is adopted to model the conditional mean part and a GARCH type model with a policy dummy variable is applied to the conditional volatility model. We show that the government policy indeed has an impact on the exchange rate dynamic. To evaluate the out-of-sample forecasting ability, a prediction interval is computed by employing nonparametric conditional quantile regression. Our method outperforms other popular models in terms of various criteria. |
| Keywords: | Nonlinearity; Functional-coefficient regression model; GARCH model; Index model; Quantile regression. |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002017&r=for |
| By: | Hännikäinen, Jari |
| Abstract: | This paper analyzes the relative performance of multi-step forecasting methods in the presence of breaks and data revisions. Our Monte Carlo simulations indicate that the type and the timing of the break affect the relative accuracy of the methods. The iterated method typically performs the best in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real-time U.S. output and inflation series shows that the alternative multi-step methods only episodically improve upon the iterated method. |
| Keywords: | structural breaks, multi-step forecasting, intercept correction, real-time data |
| JEL: | C22 C53 C82 |
| Date: | 2014–05–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:55816&r=for |
| By: | Cindy Shin-Huei Wang; Luc Bauwens; Cheng Hsiao |
| Abstract: | We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC or Mallows’ Cp) to choose the order of the approximate AR model. Our method avoids the issue of estimation inaccuracy of the long memory parameter and the issue of spurious breaks in finite sample. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing prediction methods. An empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model can be explained, from an econometric perspective, by our theoretical and simulation results. |
| Keywords: | Forecasting, Long memory process, Structural break, HAR model |
| JEL: | C22 C53 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002213&r=for |
| By: | Hajo Holzmann; Matthias Eulert |
| Abstract: | Predictions are issued on the basis of certain information. If the forecasting mechanisms are correctly specified, a larger amount of available information should lead to better forecasts. For point forecasts, we show how the effect of increasing the information set can be quantified by using strictly consistent scoring functions, where it results in smaller average scores. Further, we show that the classical Diebold-Mariano test, based on strictly consistent scoring functions and asymptotically ideal forecasts, is a consistent test for the effect of an increase in a sequence of information sets on $h$-step point forecasts. For the value at risk (VaR), we show that the average score, which corresponds to the average quantile risk, directly relates to the expected shortfall. Thus, increasing the information set will result in VaR forecasts which lead on average to smaller expected shortfalls. We illustrate our results in simulations and applications to stock returns for unconditional versus conditional risk management as well as univariate modeling of portfolio returns versus multivariate modeling of individual risk factors. The role of the information set for evaluating probabilistic forecasts by using strictly proper scoring rules is also discussed. |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1404.7653&r=for |
| By: | Yongmiao Hong; Haitao Li; Feng Zhao |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:001959&r=for |
| By: | Ying Chen; Bo Li; Linlin Niu |
| Abstract: | Our proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local interval is identified in a sequential testing procedure. Numerical analysis and real data application are conducted to illustrate the monitoring function and forecast performance of the proposed model. |
| Keywords: | Adaptive estimation; Multivariate time series; Non-stationarity; Yield curve |
| JEL: | C32 C53 E43 E47 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:wpaper:002208&r=for |
| By: | Hongquan Li; Yongmiao Hong |
| Abstract: | The classical volatility models, such as GARCH, are return-based models, which are constructed with the data of closing prices. It might neglect the important intraday information of the price movement, and will lead to loss of information and efficiency. This study introduces and extends the range-based autoregressive volatility model to make up for these weaknesses. The empirical results consistently show that the new model successfully captures the dynamics of the volatility and gains good performance relative to GARCH model. |
| Keywords: | Volatility modeling; Price range; Forecasting performance; Intraday information;�GARCH |
| JEL: | G32 C01 C53 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002128&r=for |
| By: | Nasr, Adnen Ben; Lux, Thomas; Ajm, Ahdi Noomen; Gupta, Rangan |
| Abstract: | The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of Islamic Sharia rules. In this light, we investigate the statistical properties of the Dow Jones Islamic Stock Market Index (DJIM) and explore its volatility dynamics using a number of up-to-date statistical models allowing for long memory and regime-switching dynamics. We find that the DJIM shares all stylized facts of traditional asset classes, and estimation results and forecasting performance for various volatility models are also in line with prevalent findings in the literature. Overall, the relatively new Markov-switching multifractal model performs best under the majority of time horizons and loss criteria. Long memory GARCH-type models always improve upon the short-memory GARCH specification and additionally allowing for regime changes can further improve their performance. -- |
| Keywords: | islamic finance,volatility dynamics,long memory,multifractals |
| JEL: | G15 G17 G23 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cauewp:201407&r=for |
| By: | Kent Wang; Yuqiang Guo |
| Abstract: | This study supplies new evidence regarding the predictive power of jumps for conditional market returns and volatilities. We change the constant jump intensity as in the LPW and Du models with time-varying intensity following an autoregressive conditional jump intensity (ARJI) process and a squared bessel (SB) process, and apply calibrated jump premiums to predict excess market returns and volatilities. We show that all calibrated jump premiums have significant predictive power in sample and out-of-sample. We find that in the U.S. market LPW’s model forecasts excess returns and volatilities better. The ARJI process of jump intensity predicts excess returns better, and SB process forecasts volatilities better. In the Australian market we find that, the model with ARJI process of jump intensity predicts Australian market returns and volatilities better. |
| Keywords: | Jump intensity; Equity premium; Jump premium; Stock return predictability; Volatility predictability |
| JEL: | C13 C14 G10 G12 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002192&r=for |
| By: | Emmanuel Haven; XiaoquanLiu; Chenghu Ma; LiyaShen |
| Abstract: | Options are believed to contain unique information on the risk-neutral moment generating function(MGF) or the risk-neutral probability density function(PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how therisk-neutralMGFcanbeobtainedusingthewaveletmethod.Withthe Black–Scholes model as the benchmark, we offer anovel method to reveal the implied MGF,and to price in-sample options and forecast out-of-sample option prices with the estimated MGF. |
| Keywords: | Waveletanalysis; Option pricing; Laplace transform. |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002097&r=for |
| By: | Mohanty, Roshni; P, Srinivasan |
| Abstract: | This paper investigates the relationship between stock market returns and volatility in the Indian stock markets using AR(1)-EGARCH(p, q)-in-Mean model. The study considers daily closing prices of two major indexes of Indian stock exchanges, viz., S&P CNX NIFTY and the BSE-SENSEX of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), respectively for the period from July 1, 1997 to December 31, 2013. The empirical results show positive but insignificant relationship between stock returns and conditional variance in the case of NSE Nifty and BSE SENSEX stock markets. Besides, the analysis reveals that volatility is persistent and there exists leverage effect supporting the work of Nelson (1991) in the Indian stock markets. The present study suggests that the capital market regulators, investors and market participants should employ the asymmetric GARCH-type model that sufficiently captures the stylized characteristics of the return, such as time varying volatility, high persistence and asymmetric volatility responses, in determining the hedging strategy and portfolio management and estimating and forecasting volatility for risk management decision making at Indian Stock Exchange. |
| Keywords: | Stock Market Returns, Weak-From Efficiency, India, AR-EGARCH-M model |
| JEL: | C58 G1 G12 |
| Date: | 2014–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:55660&r=for |
| By: | Emmanuel Haven; Xiaoquan Liu; Chenghu Ma; Liya Sh |
| Abstract: | Options are believed to contain unique information on the risk-neutral moment generating function (MGF) or the risk-neutral probability density function (PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how the risk-neutral MGF can be obtained using the wavelet method. With the Black–Scholes model as the benchmark, we offer a novel method to reveal the implied MGF, and to price in-sample options and forecast out-of-sample option prices with the estimated MGF. |
| Keywords: | Wavelet analysis · Option pricing · Laplace transform |
| JEL: | G13 |
| Date: | 2013–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:wyi:journl:002092&r=for |