使用动态面板数据模型和gmm型估计器的欧洲能源市场日内波动的决定因素

P. Solibakke, Torbjørn Årethun, Ove Oklevik
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摘要

斯堪的纳维亚国际未来能源市场的波动性是基于一个众所周知的日内波动范围测量来检查的。本文的主要目的是确定能源市场日内波动的决定因素。首先,调查是一个逐合约的、基于区间的波动率度量。研究发现:(1)最长合约(一年)的波动率记忆较长,但最短合约(一个月)的波动率记忆较长;(2)同期和一天滞后的交易量度量对所有合约的每日波动率都有积极和一致的影响;(3)交易量似乎主导了萨缪尔森(到期)和未平仓假设,从而体现了未来市场的早期发现。其次,本文采用了一种相对较新的方法来处理动态面板数据模型,其中市场中的每个合约都被组织为一个面板。实现了Arellano[1]动态面板数据的gmm型估计器。通过适当的规格残差检验,首次证明了波动率序列相关的重要性。这个估计也表明,每日交易量的变化是日内波动的主要决定因素。与分段分析相比,到期日时间对日内波动率的影响有所增加,而买卖价差和未平仓合约的影响则有所下降。因此,GMM的主要发现是交易量的变化(包括滞后)和到期时间是日内波动的主要决定因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determinants for European energy markets intra-day volatility using dynamic panel data models and GMM-type estimators
The volatility of the Scandinavian international future energy market is examined based on a well known intra-day range-based measure of volatility. The main purpose of the paper is to identify determinants of the energy market's intra-day volatility. Firstly, the investigation is a contract-by-contract, range-based volatility measure. The findings are (1) long memory in volatility the longest contracts (year), but not for the shortest (month), (2) the first difference contemporaneous and one-day lagged trading volume measures influences daily volatility positively and consistently over all contracts, (3) trading volume seems to dominate the Samuelson (maturity) and the open-interest hypotheses thus manifesting earlier findings of future markets. Secondly, the paper applies a relatively new methodology to cope with dynamic panel data models, where every contract in the market is organized as a panel. The GMM-type estimator for dynamic panel-data of Arellano [1] is therefore implemented. The results show appropriate specification residual tests and demonstrate firstly the importance of volatility serial correlation. This estimator also shows that the change in daily trading volume is the major determinant of intra-day volatility. In contrast to the segmented analyses, time to maturity shows increased intra-day volatility influence while the measures of bid-ask spread and open interest show decreased significance. The main GMM findings are therefore that the change in trading volume (including lags) and time to maturity are major determinants of intra-day volatility.
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