臺灣博碩士論文加值系統

本篇論文使用了Easley et al. (2012a)所提出的VPIN指標來預測市場上因為流動性造成的波動。VPIN指標是根據訂單不均衡(order imbalance)和交易的強度(trade intensity)下去做估計，所以在現在高頻交易的市場上，VPIN可以更準確的預測市場上的波動。因為在高頻交易的環境下，訂單所傳達的資訊遠比時間所傳達的資訊來的多。我們利用了臺灣指數期貨的資料來估計VPIN指標，樣本期間從2007年1月1日至2008年12月31日，去研究VPIN指標是否可以預測臺指期的波動。接者我們定義了一些市場的極端特性，例如:總體經濟宣告、極端波動以及極端VIX。我們發現如果前一個bucket是極端波動和極端VIX，它的VPIN會顯著高於極它bucket的VPIN，但是這個結果沒有發生在總體經濟宣告的情況下。我們也發現VPIN指標能夠預測臺指期的波動，這種情況在金融海嘯時期更為明顯，但沒有任何結果顯示VPIN指標能夠預測VIX。

This research use the new procedure presented by Easley et al. (2012a) to estimate flow toxicity, which named volume-synchronized probability of informed trading, or the VPIN flow-toxicity metric. The VPIN metric is based on volume imbalance and trade intensity which can more accurate to predict the volatility in the high frequency trading (HFT) environment. Because in HFT, trade volume convey more information than clock-time. We use the Taiwan index futures’ (TX) data from 2007/01/01 to 2008/12/31 to estimate the VPIN metric to see whether the VPIN metric can predict TX’s volatility. Then, we define some extreme market conditions, including macroeconomics announcements, extreme volatility, and extreme VIX. We find that if it is the one bucket before extreme volatility and extreme VIX, it VPIN are more higher than the others buckets. But this results not happened in the pre-macroeconomics announcements. We also find the evidence the VPIN metric can forecast TX’s volatility, especially in the financial crisis period this phenomenon is more significant, but we find a little evidence that the VPIN metric can forecast the VIX.

Title Page…………………………………………………………………i
Letter of Approval………………………………………………………ii
Letter of Authority……………………………………………………iii
Abstract in Chinese……………………………………………………vi
Abstract in English……………………………………………………vii
Acknowledgement…………………………………………………………viii
Contents……………………………………………………………………ix
Table Contents……………………………………………………………x
Figure Contents……………………………………………………………x
1. Introduction………………………………………………………1
2. Literature review………………………………………………4
2.1 High frequency trading…………………………………………4
2.2 The characteristics of VPIN…………………………………5
2.3 Extreme market condition and VPIN…………………………7
3. Methodology………………………………………………………8
3.1 Data…………………………………………………………………8
3.2 Extreme market conditions……………………………………9
3.3 The VPIN toxicity metric……………………………………10
3.3.1 Time bars………………………………………………………10
3.3.2 Volume buckets…………………………………………………11
3.3.3 Buy volume and sell volume classification………………11
3.3.4 Order imbalance....................................12
3.3.5 Sample length……………………………………………………12
3.4 Empirical model…………………………………………………13
4. Empirical results………………………………………………14
4.1 Descriptive statistics and correlation…………………14
4.2 Extreme market conditions and VPIN………………………15
4.3 VPIN and Volatility/VIX………………………………………16
5. Conclusion………………………………………………………18
Reference……………………………………………………………………20


Table Contents
Table 1. Extreme market condition days……………………………22
Table 2. Taiwan macroeconomics announcements……………………22
Table 3. Macroeconomics announcement dates………………………23
Table 4. The descriptive statistics…………………………………24
Table 5. Pearson correlation…………………………………………25
Table 6. Extreme market conditions…………………………………26
Table 7. Volatility and VPIN…………………………………………28
Table 8. VIX and VPIN……………………………………………………31



Figure Contents
Figure 1. The VPIN metric innovations………………………………34
Figure 2. The VPIN metric………………………………………………34

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