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|Title:||Price behaviour in China's commodity futures markets||Authors:||Xin, Yu||Keywords:||Hong Kong Polytechnic University -- Dissertations
Commodity futures -- Prices -- China
Stocks -- Prices -- China
Securities -- Prices -- China
|Issue Date:||2003||Publisher:||The Hong Kong Polytechnic University||Abstract:||As an emerging market in a transitional economy, China's commodity futures market has experienced ups-and-downs during the past ten years. Currently, some futures markets have begun to play an important role in China's economy. However, few studies to date have systematically investigated the price bahaviour of futures contracts traded in China. Thus, this study systematically, for the first time, investigates the price bahaviour of futures contracts traded in China. The random walk hypothesis, the contemporaneous and lead-lag relationship between volume and return/absolute return, the response of return and volume to different information shocks, the response of prices to different determinants, and the price discovery process between products are examined. The main interesting empirical evidence is summarized as follows: For both closing and settlement price series, the random walk hypothesis is consistently supported by all tests only with regard to copper (sub-sample 2) and aluminum futures, and not with regard to soybean, wheat and copper (sub-sample l) futures. Generally, the contemporaneous correlations between absolute return and trading volume are significantly positive in all futures markets. The evidence of a significant causal relationship following from absolute return to trading volume contradicts the mixture of distributions hypothesis and supports the sequential information arrival hypothesis in all examined futures markets except for aluminium futures. There is significant causality following flom trading volume to absolute settlement-to-settlement return in the copper (sub-sample l) futures market. The informational/permanent components are the dominant components for futures return movements, while the non-informational/transitory components are the dominant components for trading volume. A non- informational/transitory shock initially has a significantly positive effect on trading volume, but its effect declines gradually over time. An informational/permanent shock initially has a significantly positive effect on the return on futures, and this adjustment is rapid. There is all over-reaction to informational/permanent shocks on the return on wheat futures; while the volume of aluminium futures responds to non-informational/transitory information more slowly than that of other futures; and the volume of copper futures responds to non-informational/transitory information more quickly during the second sub- sample period.
A significantly positive time-to-maturity effect can be clearly observed in all futures markets. A significantly positive relationship between trading volume and price volatility can be found in copper(all-sample and two sub-samples) and soybean futures markets, while a significantly negative relationship between open interest and price volatility can be found only in the copper(all-sample), copper (sub-sample 2) and soybean futures markets. The estimated coefficients associated with volume shocks are significantly positive only for copper(all-sample and two sub-samples 2) futures, and they are not obviously higher than those associated with expected volume. There is no significant relationship between unexpected open interest and price volatility. The relationship between unexpected volume shocks and contemporaneous volatility is asymmetric, with positive volume shocks having an economically and statistically larger effect on volatility, in copper and soybean futures markets. The asymmetrical effects of unexpected open interest only exist in the copper (sub-sample 2) and soybean futures markets. Higher trading volume leads to higher positive effects of trading volume on price volatility for all futures except for wheat. There is a long-run equilibrium relationship between copper and aluminium futures (feedback/bi-directional), and between soybean and wheat futures (single direction following from soybean to wheat). The empirical results of the error correction model imply that when disequilibrium occurs it is copper/wheat prices that adjust to the long-run equilibrium, and bear the majority of the burden of convergence. Based on the empirical results, I found that (1) market efficiency/effectiveness improved during 1999-2002 relative to 1996-l998 in copper futures due to the implementation of the second powerful adjustment from 1999; (2) during 1999-2002, the market efficiency and effectiveness of metal futures were better than those of agricultural futures; (3) during 1999-2002, the market efficiency and effectiveness of soybean futures (with active trading) were better than those of wheat futures (with relatively inactive trading); and (4) during 1999-2002, the comparison of market efficiency and effectiveness between copper (with active trading) and aluminium (with relatively inactive trading) futures was inconclusive, which needs to be further investigated in future studies. The main contributions of this study include:(1) for the first time, this study thoroughly and systematically examines the price behaviours in China's commodity futures markets; (2) by estimating the level of efficiency and effectiveness of main futures markets, this study enhance our understanding of China's commodity futures markets, and provides important reference for, and detailed information to, market regulators; (3) from the perspective of policy implications, this study provides powerful empirical evidence to support the improvement of market efficiency and effectiveness for copper futures, and gives a positive evaluation for the regulatory effect of the second adjustment; (4) this study finds that higher trading volume is very helpful for the efficiency and effectiveness of futures markets, which means that some policy measures need to be considered to further animate futures transactions; (5) In terms of research methodology, this study is the first to employ BMAR and BVAR methodologies in futures markets, and a relatively integrative framework for modelling the volatility of futures prices is constructed.
|Description:||xii, 200 p. : ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P AF 2003 Xin
|URI:||http://hdl.handle.net/10397/3689||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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