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Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes

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ENERGY ECONOMICS 2018年第Jan.期69卷 89-100页

作者： Valadkhani, Abbas Smyth, Russell Swinburne Univ Technol Swinburne Business Sch Hawthorn Vic 3122 Australia Monash Univ Dept Econ Clayton Vic 3800 Australia

This article investigates whether uncompetitive pricing tactics are being employed in the retail petrol market in Australia through examining the effect of a change in daily oil prices on monthly petrol prices. To do so, we incorporate asymmetry into the coefficients of a normalised beta weighting function within an Asymmetric mixed data sampling (AMIDAS) framework. This enables us to examine both the timing, and the lagged marginal effects, of a change in retail petrol prices in response to a change in the oil price. We find evidence of asymmetries in both the timing, and magnitude, of retail petrol prices to a change in the oil price. Specifically, we find that while price falls are slowly and symmetrically passed onto consumers, price increases are more delayed, but higher in intensity over time. Depending on the capital city, when retailers eventually do increase petrol prices, in response to an oil price rise with delay, the price rise is between 2.1 and 3.4 times more than when retailers reduce petrol prices in response to a fall in the price of oil. This finding is consistent with retailers delaying substantial petrol price rises in order to mask the existence of uncompetitive practices. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Petrol price Retail mixed data sampling Australia

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Market states and the risk-return tradeoff

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QUARTERLY REVIEW OF ECONOMICS AND FINANCE 2017年第Aug.期65卷 314-327页

作者： Wang, Zijun Khan, M. Moosa Univ Texas San Antonio Coll Business Dept Finance San Antonio TX 78249 USA Prairie View A&M Univ Prairie View TX USA

We re-examine the risk-return tradeoff in the U.S. equity market by allowing for time variation in the tradeoff and estimating conditional variance by the new mixed data sampling method. The main finding is that the risk-return tradeoff is strongly time-varying with the state of the market and the average of the time-varying tradeoff estimates is 1.43. The lagged market return is found to be the best indicator of market states. The empirical finding holds true for a battery of robustness checks during the post-Compustat sample period. The evidence from the international markets is similar to the U.S. one. (C) 2016 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

关键词： Risk-return tradeoff Time variation Market states mixed data sampling

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Quantitative Easing in Developed Countries and Middle Income Countries' financial markets

Quantitative Easing in Developed Countries and Middle Income...

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作者： Thuthuka Ntuli University of the Witwatersrand

学位级别：硕士

This study examines Quantitative Easing policy programs of developed countries and their potential impact on Middle Income Countries through capital inflows. The study specifically focuses on the United States and European Union Quantitative Easing programs and investigates potential effects through the various transmission channels. An Autoregressive Multifactor MIDAS approach is used to carry out the empirical analysis and the study finds that lagged capital inflows are highly significant across the different models run and that there is evidence of transmission of quantitative easing to capital inflows to Middle Income Countries along the portfolio rebalancing and liquidity channels.

关键词： Quantitative Easing Middle Income Countries Financial/Capital inflows mixed data sampling

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Conditionally Efficient Estimation of Long-Run Relationships Using mixed-Frequency Time Series

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ECONOMETRIC REVIEWS 2016年第6期35卷 1142-1171页

作者： Miller, J. Isaac Univ Missouri Dept Econ 118 Profess Bldg Columbia MO 65211 USA

I analyze efficient estimation of a cointegrating vector when the regressand and regressor are observed at different frequencies. Previous authors have examined the effects of specific temporal aggregation or sampling schemes, finding conventionally efficient techniques to be efficient only when both the regressand and the regressors are average sampled. Using an alternative method for analyzing aggregation under more general weighting schemes, I derive an efficiency bound that is conditional on the type of aggregation used on the low-frequency series and differs from the unconditional bound defined by the full-information high-frequency data-generating process, which is infeasible due to aggregation of at least one series. I modify a conventional estimator, canonical cointegrating regression (CCR), to accommodate cases in which the aggregation weights are known. The correlation structure may be utilized to offset the potential information loss from aggregation, resulting in a conditionally efficient estimator. In the case of unknown weights, the correlation structure of the error term generally confounds identification of conditionally efficient weights. Efficiency is illustrated using a simulation study and an application to estimating a gasoline demand equation.

关键词： Canonical cointegrating regression Cointegration mixed data sampling mixed-frequency series Price elasticity of gasoline demand Temporal aggregation C13 C22 Q41

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Evaluating monthly volatility forecasts using proxies at different frequencies

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FINANCE RESEARCH LETTERS 2016年 17卷 41-47页

作者： Niguez, Trino-Manuel Univ Westminster London W1R 8AL England Bank Spain Madrid Spain

This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained from GARCH and AGARCH models with Normal and Student's t errors are evaluated with respect to proxies for the unobserved volatility obtained through sampling at different frequencies. It is found that aggregation of daily multi-step ahead GARCH-type forecasts provide rather accurate predictions of monthly volatility. (C) 2016 Elsevier Inc. All rights reserved.

关键词： data frequency Leverage effect mixed data sampling Models ranking Volatility proxy

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Choosing a data Frequency to Forecast the Quarterly Yen-Dollar Exchange Rate

Choosing a Data Frequency to Forecast the Quarterly Yen-Doll...

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作者： Benjamin Cann University Of Victoria

学位级别：硕士

Potentially valuable information about the underlying data generating process of a dependent variable is often lost when an independent variable is transformed to fit into the same sampling frequency as a dependent variable. With the mixed data sampling (MIDAS) technique and increasingly available data at high frequencies, the issue of choosing an optimal sampling frequency becomes apparent. We use financial data and the MIDAS technique to estimate thousands of regressions and forecasts in the quarterly, monthly, weekly, and daily sampling frequencies. Model fit and forecast performance measurements are calculated from each estimation and used to generate summary statistics for each sampling frequency so that comparisons can be made between frequencies. Our regression models contain an autoregressive component and five additional independent variables and are estimated with varying lag length specifications that incrementally increase up to five years of lags. Each regression is used to forecast a rolling, one and two-step ahead, static forecast of the quarterly Yen and U. S Dollar spot exchange rate. Our results suggest that it may be favourable to include high frequency variables for closer modeling of the underlying data generating process but not necessarily for increased forecasting performance.

关键词： mixed data sampling forecasting model selection criteria time-series yen dollar exchange rate

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Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks

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COMPUTATIONAL STATISTICS & data ANALYSIS 2014年 76卷 43-60页

作者： Audrino, Francesco Univ St Gallen Dept Econ CH-9000 St Gallen Switzerland

The predictive power of recently introduced components affecting correlations is investigated. The focus is on models allowing for a flexible specification of the short-run component of correlations as well as the long-run component. Moreover, models allowing the correlation dynamics to be subjected to regime-shift caused by threshold-based structural breaks of a different nature are also considered. The results indicate that in some cases there may be a superimposition of the long-term and short-term movements in correlations. Therefore, care is called for in interpretations when estimating the two components. Testing the forecasting accuracy of correlations during the late-2000s financial crisis yields mixed results. In general, component models allowing for a richer correlation specification possess an increased predictive accuracy. Economically speaking, no relevant gains are found by allowing for more flexibility in the correlation dynamics. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Correlation forecasting Component models Threshold regime-switching models mixed data sampling Performance evaluation

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The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach

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JOURNAL OF FORECASTING 2013年第7期32卷 600-612页

作者： Asgharian, Hossein Hou, Ai Jun Javed, Farrukh Lund Univ Dept Econ S-22007 Lund Sweden Knut Wicksell Ctr Financial Studies S-22007 Lund Sweden Southern Denmark Univ Dept Econ & Business Odense Denmark Lund Univ Dept Stat S-22007 Lund Sweden

This paper applies the GARCH-MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： mixed data sampling long-term variance component macroeconomic variables principal component variance prediction

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The conditional autoregressive Wishart model for multivariate stock market volatility

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JOURNAL OF ECONOMETRICS 2012年第1期167卷 211-223页

作者： Golosnoy, Vasyl Gribisch, Bastian Liesenfeld, Roman Univ Kiel Inst Stat & Econometr D-24118 Kiel Germany

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a mixed data sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Component volatility models Covariance matrix mixed data sampling Observation-driven models Realized volatility

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A component model for dynamic correlations

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JOURNAL OF ECONOMETRICS 2011年第1期164卷 45-59页

作者： Colacito, Riccardo Engle, Robert F. Ghysels, Eric Univ N Carolina Dept Econ Chapel Hill NC 27599 USA Univ N Carolina Kenan Flagler Business Sch Div Finance Chapel Hill NC 27599 USA NYU Stern Sch Business Dept Finance New York NY 10003 USA

We propose a model of dynamic correlations with a short- and long-run component specification, by extending the idea of component models for volatility. We call this class of models DCC-MIDAS. The key ingredients are the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model replacing the original DCC dynamics with a component specification and the Engle et al. (2006) GARCHMIDAS specification that allows us to extract a long-run correlation component via mixed data sampling. We provide a comprehensive econometric analysis of the new class of models, and provide extensive empirical evidence that supports the model's specification. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dynamic correlations Forecasting mixed data sampling

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