U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

This paper examines the market efficiency of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Futures after the announcement of Quantitative Easing (QE) policy. Order imbalance is used to explore the relationship between return and order imbalance. We find that under the unconditional OLS model, lagged order imbalances almost have no significantly positive predictive power for current return. Nonetheless, on the trading day after the announcement of QE 1 policy, one-minute interval data show that the lagged order imbalance has predictive power for current return. Under the conditional OLS model, the reversed relation between current return and lagged order imbalance is not universal; on the other hand, after the announcement of QE 2 policy, the reversed relation between current return and lagged order imbalance is more common. Moreover, under volatility-GARCH (1, 1), one-minute interval data shows significantly positive relation between order imbalance and volatility

Financial Policy Announcement Efficiency in Financial Crisis

In 2008 financial crisis, stock market turned highly volatile while U.S. government had proposed a series of policies rescuing the economy. This study examines convergence to market efficiency from government financial policies. We find a significant impact of contemporaneous order imbalance on return, while the relation between return and lagged imbalances is insignificant, implying that lagged order imbalances have no predictability on return. From a time-varying GARCH model, we find that explaining power of order imbalance on return declining, implying that volatility plays an important role in return-order imbalance relation. We take a further step to find that there is no strong direct relationship between order imbalances and stock volatility. The story casts on market maker behaviors. Market makers accommodate high inventory levels to mitigate stock volatility on financial policies announcements. An imbalance based trading strategy we develop fails to beat the market. It supports financial policy announcement efficiency

The Impact of Liquidity on GARCH Option Pricing Error during Financial Crisis

In this paper, we explore the valuation performance of Heston and Nandi GARCH (HN GARCH) model on the pricing of options of financial stocks listed for AMEX during pre and post financial crisis periods. We find that the GARCH pricing model presents better performance than the traditional Black-Scholes model for the out-of-sample option pricing, no matter what the moneyness and the time-to-maturity. Specifically, the models show the effects of liquidity is not significant. Intuitionally, smaller liquidity tends to exhibit more pricing errors, especially for longer mature options. Unfortunately, we cannot get the expected outcomes, which is that the period of post financial crisis tend to have larger pricing errors. In sum, except more computational convenience, the HN GARCH model offers another vision of the relationship between liquidity and its effect on pricing errors

Market Efficiency of Commercial Bank in Financial Crisis

This study investigates commercial bank market efficiency in financial crisis. We employ a time-varying GARCH model because volatility matters in financial crisis. The empirical results show a significant positive relation between contemporaneous order imbalances and returns in convergence process toward efficiency. A direct linkage between volatility and order imbalances is examined by GARCH model. Surprisingly, a low connection exists between order imbalance and price volatility, implying that market makers are capable of mitigating commercial bank prices volatility in financial crisis. We develop an imbalance based trading strategy but fail to beat the market. A nested causality approach, which examines the dynamic return-order imbalance relationship during the price formation process, confirms the results. Keywords: order imbalance; market efficiency; commercial bank; financial crisis. JEL classification: G01; G14; G2