Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models

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.     

Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

This paper examines the forecasting ability of the nonlinear specifications of the market model. We propose a conditional two‐moment market model with a time‐varying systematic covariance (beta) risk in the form of a mean reverting process of the state‐space model via the Kalman filter algorithm. In addition, we account for the systematic component of co‐skewness and co‐kurtosis by considering higher moments. The analysis is implemented using data from the stock indices of several developed and emerging stock markets. The empirical findings favour the time‐varying market model approaches, which outperform linear model specifications both in terms of model fit and predictability. Precisely, higher moments are necessary for datasets that involve structural changes and/or market inefficiencies which are common in most of the emerging stock markets. Copyright © 2016 John Wiley & Sons, Ltd.     

Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting

Value‐at‐risk (VaR) is a standard measure of market risk in financial markets. This paper proposes a novel, adaptive and efficient method to forecast both volatility and VaR. Extending existing exponential smoothing as well as GARCH formulations, the method is motivated from an asymmetric Laplace distribution, where skewness and heavy tails in return distributions, and their potentially time‐varying nature, are taken into account. The proposed volatility equation also involves novel time‐varying dynamics. Back‐testing results illustrate that the proposed method offers a viable, and more accurate, though conservative, improvement in forecasting VaR compared to a range of popular alternatives. Copyright © 2013 John Wiley & Sons, Ltd.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

Volatility forecasting with bivariate multifractal models

This paper examines volatility linkages and forecasting for stock and foreign exchange markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model that accounts for possible interactions between stock and foreign exchange markets. Examining daily data from major advanced and emerging nations, we show that generalized autoregressive conditional heteroskedasticity models generally offer superior volatility forecasts for short horizons, particularly for foreign exchange returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer horizons, consistently across most markets. Finally, the bivariate multifractal model provides superior forecasts compared to the univariate alternative in most advanced markets and more consistently for currency returns, while its benefits are limited in the case of emerging markets.     

Trading volume and realized volatility forecasting: Evidence from the China stock market

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.     

Forecasting of dependence,market, and investment risks of a global index portfolio

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.     

Second‐Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

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.     

Price forecasting and evaluation: An application in agriculture

Because of the high volatility of prices of agricultural commodities over the past decade, the importance of accurate price forecasting for decision makers has become even more acute. This paper reviews literature on forecasting and evaluation. An application with forecasting U.S. hog prices is presented which includes both economic and statistical evaluation measures. Seven forecasting approaches are described and their performances are examined over 24 quarters from 1976 to 1981. These methods include exponential smoothing, an autoregressive integrated moving average process, an econometric model, expert judgement, and a composite forecasting approach. The application gives results which support previous findings in the forecasting literature and suggests that forecasting methods can provide valuable information to the decision maker.     

Volatility forecasting with double Markov switching GARCH models

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat‐tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first‐order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value‐at‐risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd.     

Time series forecasting using functional partial least square regression with stochastic volatility,GARCH, and exponential smoothing

We propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.     

Forecasting Interest Rates and Yield Spreads: The Informational Content of Implied Futures Yields and Best-fitting Forward Rate Models

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.     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time-varying beta

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model: the Kalman method. Forecast errors based on 20 UK company daily stock return (based on estimated time-varying beta) forecasts are employed to evaluate out-of-sample forecasting ability of both GARCH models and Kalman method. Measures of forecast errors overwhelmingly support the Kalman filter approach. Among the GARCH models the GJR model appears to provide somewhat more accurate forecasts than the other bivariate GARCH models. Copyright © 2008 John Wiley & Sons, Ltd.     

On the predictability of crude oil market: A hybrid multiscale wavelet approach

Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily–yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.     

Estimation and prediction under structural instability: The case of the U.S. pulp and paper market

The objectives of this paper are: first, to show empirically the relevance of using adaptive estimation techniques over more traditional estimation approaches when economic systems are believed to be structurally unstable over time; and secondly, to compare in an empirical framework two adaptive estimation techniques: Kalman filtering and the Carbone–Longini filter. For that purpose, an econometric model for the U.S. pulp and paper market is examined under the assumption of structural instability and, hence, constitutes the basis for comparing forecasting performances and estimation accuracy achieved by each technique. A version of Kalman filtering, modified in line with the basic idea of ‘tracking’ characterizing the Carbone–Longini filter, is also presented and applied. The analysis of the results shows that it may be worth using adapative estimation methods to estimate structurally unstable models, even if there is no prior knowledge about the patterns of variation of the parameters. Also, it shows the Carbone–Longini filter and Kalman filtering as being complementary estimation techniques. An estimation/forecasting methodology involving a sequential application mode of these two techniques is suggested.     

Forecasting football results and the efficiency of fixed‐odds betting

An ordered probit regression model estimated using 10 years' data is used to forecast English league football match results. As well as past match results data, the significance of the match for end‐of‐season league outcomes, the involvement of the teams in cup competition and the geographical distance between the two teams' home towns all contribute to the forecasting model's performance. The model is used to test the weak‐form efficiency of prices in the fixed‐odds betting market. A strategy of selecting end‐of‐season bets with a favourable expected return according to the model appears capable of generating a positive return. Copyright © 2004 John Wiley & Sons, Ltd.     

Shift-contagion in energy markets and global crisis

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.     

Is implied volatility more informative for forecasting realized volatility: An international perspective

Inspired by the commonly held view that international stock market volatility is equivalent to cross-market information flow, we propose various ways of constructing two types of information flow, based on realized volatility (RV) and implied volatility (IV), in multiple international markets. We focus on the RVs derived from the intraday prices of eight international stock markets and use a heterogeneous autoregressive framework to forecast the future volatility of each market for 1 day to 22 days ahead. Our Diebold-Mariano tests provide strong evidence that information flow with IV enhances the accuracy of forecasting international RVs over all of the prediction horizons. The results of a model confidence set test show that a market's own IV and the first principal component of the international IVs exhibit the strongest predictive ability. In addition, the use of information flows with IV can further increase economic returns. Our results are supported by the findings of a wide range of robustness checks.     

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

In this paper we propose and test a forecasting model on monthly and daily spot prices of five selected exchange rates. In doing so, we combine a novel smoothing technique (initially applied in signal processing) with a variable selection methodology and two regression estimation methodologies from the field of machine learning (ML). After the decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method into a smoothed and a fluctuation component, multivariate adaptive regression splines (MARS) are used to select the most appropriate variable set from a large set of explanatory variables that we collected. The selected variables are then fed into two distinctive support vector machines (SVR) models that produce one‐period‐ahead forecasts for the two components. Neural networks (NN) are also considered as an alternative to SVR. The sum of the two forecast components is the final forecast of the proposed scheme. We show that the above implementation exhibits a superior in‐sample and out‐of‐sample forecasting ability when compared to alternative forecasting models. The empirical results provide evidence against the efficient market hypothesis for the selected foreign exchange markets. Copyright © 2015 John Wiley & Sons, Ltd.