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- Title
- High Frequency Trading and the Impact of Volume-Duration on Market Quality in the U.S. Futures Markets
- Creator
- Xu, Xiaoruo
- Date
- 2023
- Description
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This paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted...
Show moreThis paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted Volume-Durations (AVD). By using the unique nanosecond level TAQ CME datasets of commodities futures in 2018, which include Crude Oil, E-mini S&P 500, Eurodollar, Gold, Corn and Soybean, I create the AVDs of each dataset, then conduct the regression analysis on market quality variables with the independent variables including AVDs and other key variables, and the results show that as AVD decreases, the market quality deteriorates, thus HFT positively affects market quality in the U.S. futures market. In order to explore the main driver of AVD on market quality in the futures market, I use the Autoregressive Conditional Duration Model to decompose AVDs into expected AVDs (AEVD), which is the component of AVD that is influenced by past AVDs and unexpected AVDs (AUVD), which is the component of AVD that is not captured by past AVDs but by unanticipated events, and then conduct the regression analysis on market quality variables with the independent variables including AEVD, AUVD and other key variables. The result shows that AEVD has a higher impact on liquidity than AUVD, but the impact of AEVD and AUVD on volatility is mixed in the U.S. futures market. However, except for the conclusions get from the based multivariate regression results, I also explain why there are some outliers for the Eurodollar, soybean and gold, and why HFT has more explicit impact on agricultural futures market.
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- Title
- Essays on Clean Energy Finance and Cryptocurrency Market
- Creator
- Xie, Yao
- Date
- 2021
- Description
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This dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the...
Show moreThis dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the first essay, I investigate the heterogeneous relationship between various determinants of the clean energy market across all subsectors of the clean energy stock market. My findings reveal that VIX is the most significant predictor of all clean energy subsectors conditional volatility. During the COVID-19 stress period, economic uncertainty measures become more significant measures. The heterogeneity of clean energy market persists in the out-of-sample results. These results suggest that portfolio diversification for different clean energy subsector is necessary. In the second essay, I study the safe haven property of several volatility indexes on clean energy subsectors. I compare the current COVID-19 stress period and the time before. The results show that market volatility and commodity volatility are good safe haven assets during the COVID-19 period. But they are not safe haven assets against the clean energy subsector before the pandemic period. Among all volatility indexes, gold volatility index is the most effective safe haven assets. In the third essay, I investigate the characteristics of Bitcoin as a financial asset. A comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other markets, stress level, and investor sentiment. The empirical results show that blockchain technology, stress level and investor sentiment have strong predicting power on Bitcoin returns. In the fourth essay, I aim to study how extreme sentiment measures from Google Trend and Wikipedia Pageviews affect both traditional cryptocurrency, such as Bitcoin and stablecoin, like Tether. Our results show that Tether’s return is not affected by the extreme sentiment measures during the COVID-19 stress period which suggests that stablecoin can offer price stability.
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