Multivariate asset return prediction with mixture models

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a quasi-Bayesian prior, is motivated, and shown to be numerically simple and reliable to estimate, unlike the majority of multivariate GARCH models in existence. Equally important, it provides a clear improvement over use of GARCH models feasible for use with a large number of assets, such as constant conditional correlation, dynamic conditional correlation, and their extensions, with respect to out-of-sample density forecasting. A generalization to a mixture of multivariate Laplace distributions is motivated via univariate and multivariate analysis of the data, and an expectation–maximization algorithm is developed for its estimation in conjunction with a quasi-Bayesian prior. It is shown to deliver significantly better forecasts than the mixed normal, with fast and numerically reliable estimation. Crucially, the distribution theory required for portfolio theory and risk assessment is developed.     

The association between qualitative management earnings forecasts and discretionary accounting in the Netherlands

This paper examines whether there is an association between discretionary accounting and the accuracy of long-run forecasts of annual earnings disclosed voluntarily by Dutch companies in the directors’ report. In particular, investigations were made of the consistency in the sign and direction of discretionary accounting techniques and qualitative earnings forecasts. Long-run forecasts are defined, for the purposes of this paper, as forecasts made at least seven months before the year-end. Although not mandatory, qualitative forecasts are released by well over 60% of the listed companies in the Netherlands. Empirical results indicate that there is consistency in the sign and direction of qualitative earnings forecasts and discretionary accounting. After adopting discretionary accounting, the forecast errors are reduced if the company can reach the management earnings forecast (target). In the event that reserves are insufficient to accomplish this goal, managers choose their next best option and take an earnings bath in order to maximize reserves available for future use. By partitioning the sample in various sub-sets it is shown that earnings management and forecast errors occur most in the extreme ranges of financial performance. Overall, the study shows that management engages in discretionary accounting to present results in line with the disclosed qualitative earnings forecasts in their directors’ reports. Whilst discretionary accounting may clearly improve the consistency of companies’ earnings forecasts released via the directors’ reports and the actual earnings, managers’ earnings forecasts are sometimes disclosed in anticipation of planned discretionary accounting actions.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Regime-switching models for exchange rates

This study provides evidence of periodically collapsing bubbles in the British pound to US dollar exchange rate in the post-1973 period. We develop two- and three-state regime-switching (RS) models that relate the expected exchange rate return to the bubble size and to an additional explanatory variable. Specifically, we consider six alternative explanatory variables that have been proposed in the literature as early warning indicators of a currency crisis. Our findings suggest that the RS models are, in general, more accurate than the Random Walk model in terms of both statistical and especially economic evaluation criteria for exchange rate forecasts. Our three-state RS model outperforms the two-state models and among the variables considered in our analysis, the short-term interest rate is the optimal variable, closely followed by imports. Results are more promising for one-month predictions and are qualitatively robust over sample spans. However, various robustness checks based on other exchange rates show that the optimal bubble measures and optimal predictors critically depend on the exchange rate.     

Trend stationarity in the I(2) cointegration model

A vector autoregressive model for I(2) processes which allows for trend-stationary components and restricts the deterministic part of the process to be at most linear is defined. A two-step statistical analysis of the model is derived. The joint test of I(1) and I(2) cointegrating ranks is shown to be asymptotically similar with respect to the drift terms and the asymptotic distribution is tabulated. The cointegrating parameters are shown to be mixed Gaussian and an application for UK monetary data illustrates the proposed analysis.     

对当前我国宏观经济形势的判断及预测

通过对2000年以来我国的经济周期、投资周期、消费周期、信用周期、价格周期和对全球经济周期的分析,本文对当前中国宏观经济形势的判断和预测是:当前中国经济正处在经济周期的高位,投资要求拉动作用在减弱,产能释放所引起的生产过剩风险在增大,有可能导致经济从繁荣走向衰退;受供给面影响,通货膨胀仍会持续,但不表明经济出现过热。2008年中国经济仍将维持10.5%以上的增长速度,通货膨胀率在5%～6%之间。     

基于供应可用度的可修航材需求预测模型

根据航材需求差均比大于、等于或小于1三种情况,提出负二项分布、泊松分布和二项分布三种预测方法。以给定的总保障经费为约束条件,通过总短缺数最小代替供应可用度最大来简化目标函数,建立航材需求预测模型,采用边际分析法寻优并编写了算法。根据库存平衡公式,提出实际需要筹措的需求计算公式。算例证明本模型具有良好的准确性和实用性。     

Bayesian prediction with cointegrated vector autoregressions

A complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choice of cointegration vectors is incorporated into the analysis via the prior distribution. This prior has the effect of weighing the predictive distributions based on the models with different cointegration vectors into an overall predictive distribution. The ideas of Litterman [Mimeo, Massachusetts Institute of Technology, 1980] are adopted for the prior on the short run dynamics of the process resulting in a prior which only depends on a few hyperparameters. A straightforward numerical evaluation of the predictive distribution based on Gibbs sampling is proposed. The prediction procedure is applied to a seven-variable system with a focus on forecasting Swedish inflation.     

The Accuracy of Density Forecasts from Foreign Exchange Options

Financial decision makers often consider the information incurrency option valuations when making assessments about futureexchange rates. The purpose of this article is to systematicallyassess the quality of option-based volatility and density forecasts.We use a unique dataset consisting of more than 10 years ofdaily data on over-the-counter (OTC) currency option prices.We find that the OTC implied volatilities provide largely unbiasedand fairly accurate forecasts of one-month- and three-month-aheadrealized volatility. Furthermore, we find that the one-monthoption implied density forecasts are well calibrated for thecenter of the distribution, but we find evidence of misspecificationin the tail density forecasts.     

Modeling tourism: A fully identified VECM approach

System-based cointegration methods have become popular tools for economic analysis and forecasting. However, the identification of structural relationships is often problematic. Using a theory-directed sequential reduction method suggested by Hall, Henry and Greenslade [Hall, S. G., Henry, S., & Greenslade, J. (2002). On the identification of cointegrated systems in small samples: A modelling strategy with an application to UK wages and prices. Journal of Economic Dynamics and Control, 26, 1517–1537], we estimate a vector error correction model of Hawaii tourism, where both demand and supply-side influences are important. We identify reasonable long-run equilibrium relationships, and Diebold–Mariano tests for forecast accuracy demonstrate satisfactory forecasting performance.