Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze th...Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.展开更多
Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical resul...Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical results are obtained and the corresponding explanations are given. The frequency of positive jumps is quite higher than that of negative jumps. The trading volumes and average trade sizes are all in a high level around positive jumps. The relatively low liquidities around negative jumps show that negative jumps may be generated and enlarged by poor liquidity provision.The price reversal after price jumps is significant, and price reversal lasts longer after positive jumps.Moreover, the size and direction of jumps are significantly correlated with the returns and trades in the post-jump trading time. These findings are believed to be associated with the high proportion of retail investors and their herding behavior for price trend chasing.展开更多
文摘Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.
基金supported by the National Natural Science Foundation under Grant Nos.71431008,71532013,71501170Zhejiang Provincial National Science Foundation under Grant No.LQ16G010001the fund provided by Zhejiang Provincial Key Research Base for Humanities and Social Science Research(Applied Economics in Zhejiang Gongshang University)
文摘Using 4128 single jumps detected from high frequency data of 220 individual stocks in SZ300 P index, this paper investigates the liquidity dynamics around price jumps in Chinese market.Some interesting empirical results are obtained and the corresponding explanations are given. The frequency of positive jumps is quite higher than that of negative jumps. The trading volumes and average trade sizes are all in a high level around positive jumps. The relatively low liquidities around negative jumps show that negative jumps may be generated and enlarged by poor liquidity provision.The price reversal after price jumps is significant, and price reversal lasts longer after positive jumps.Moreover, the size and direction of jumps are significantly correlated with the returns and trades in the post-jump trading time. These findings are believed to be associated with the high proportion of retail investors and their herding behavior for price trend chasing.
