Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model

We consider a model that provides flexible parameterizations of the exogenous influences on inefficiency. In particular, we demonstrate the model's unique property of accommodating non-monotonic efficiency effect. With this non-monotonicity, production efficiency no longer increases or decreases monotonically with the exogenous influence; instead, the relationship can shifts within the sample. Our empirical example shows that variables can indeed have non-monotonic effects on efficiency. Furthermore, ignoring non-monotonicity is shown to yield an inferior estimation of the model, which sometimes results in opposite predictions concerning the data.     

Value at risk forecasting for volatility index

Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.     

Estimating gravity equation models in the presence of sample selection and heteroscedasticity

Gravity models are widely used to explain patterns of trade. However, two stylized features of trade data, sample selection and heteroscedasticity challenge the estimation of gravity models. We propose a two-step method of moments (TS-MM) estimator that deals with both issues. The Monte-Carlo experiments show that the TS-MM estimates are resistant to various combinations of sample selection and heteroscedasticity. Moreover, the TS-MM estimator performs reasonably well even when the data generating process deviates from the TS-MM assumptions. We revisit the world trade in 1990 to illustrate the usefulness of the proposed model, with emphasis on the identification of the extensive margin of trade.     

Testing for weak-form efficiency of crude palm oil spot and future markets: new evidence from a GARCH unit root test with multiple structural breaks

There is a sizeable literature that tests for weak-form efficiency in commodity and energy spot and future prices. While many studies now allow for multiple structural breaks to address the criticism that conventional unit root tests have low power to reject the unit root null in the presence of structural change, the extant literature overlooks the fact that conventional unit root tests are biased in the presence of conditional heteroscedasticity. We apply a recently developed generalized autoregressive conditional heteroscedasticity (GARCH) unit root test with multiple structural breaks to crude palm oil spot and future prices and find much more evidence against weak-form efficiency than that found using tests that fail to allow for conditional heteroscedasticity. Our results point to the importance of allowing for heteroscedasticity when testing for efficiency in commodity and energy spot and future prices.     

The link between inflation and inflation uncertainty: Evidence from G7 countries

In this paper we adopt the Markov-switching heteroscedasticity model to analyse the inflation series for G7 countries and examine the interaction between inflation rate and its uncertainty over both the short- and long-run. It is found that the relationship between inflation and inflation uncertainty depends on whether the shock is permanent or transitory. The relationship also differs from country to country. High uncertainty about long-run inflation is associated with a significant positive shift in inflation for Canada, Germany, and Japan. High uncertainty about short-run inflation is associated with a significant positive shift in inflation for Germany and USA, and a significant negative shift in inflation for Canada. The modelling approach employed in this paper is empirically supported by various diagnostics including the Vuong test. We also derive the two components of the variance of inflation forecast for a particular forecast horizon. It is found that the inflation uncertainty increases at all horizons in the middle of 1970s and return to the low level in the middle of 1980s.First version received: June 2001/Final version received: October 2003We would like to thank three anonymous referees for many helpful comments and suggestions.     

Sensitivity analysis of efficiency rankings to distributional assumptions: applications to Japanese water utilities

This article examines the robustness of efficiency score rankings across four distributional assumptions for trans-log stochastic production-frontier models, using data from 1221 Japanese water utilities (for 2004 and 2005). One-sided error terms considered include the half-normal, truncated normal, exponential and gamma distributions. Results are compared for homoscedastic and doubly heteroscedastic models, where we also introduce a doubly heteroscedastic variable mean model, and examine the sensitivity of the nested models to a stronger heteroscedasticity correction for the one-sided error component. The results support three conclusions regarding the sensitivity of efficiency rankings to distributional assumptions. When four standard distributional assumptions are applied to a homoscedastic stochastic frontier model, the efficiency rankings are quite consistent. When those assumptions are applied to a doubly heteroscedastic stochastic frontier model, the efficiency rankings are consistent when proper and sufficient arguments for the variance functions are included in the model. When a more general model, like a variable mean model is estimated, efficiency rankings are quite sensitive to heteroscedasticity correction schemes.     

On Jumps and ARCH Effects in Natural Resource Prices: An Application to Pacific Northwest Stumpage Prices

Continuous-time models of natural resource prices usually preclude the possibility of large changes (jumps) resulting from unexpected events. To test for the presence of jumps and/or ARCH effects, we combine bounds and the Monte Carlo test technique to obtain finite-sample, level-exact p -values. We apply this methodology to stumpage prices from the Pacific Northwest and find evidence of jumps and ARCH effects. To assess the impact of neglecting jumps on the decision to harvest old-growth timber, we develop an autonomous, infinite-horizon stopping model for which we provide a new method of resolution. Our numerical results show the importance of modeling jumps explicitly.     

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

Abstract

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.