Three essays in financial econometrics

Abstract

This thesis consists of three essays on applying state space models to tackle interesting problems in finance and economics. Simulation-based model estimation techniques are used extensively to draw statistical inference on latent state vari- ables. In the first essay, I develop a new type of Bivariate Mixture model to describe the empirical dynamics between return volatility and trading volume. The pro- posed semi-structural model allows the common and idiosyncratic components in traders’ reservation price to interact in a multiplicative way rather than an addi- tive way which is typically adopted by previous researches. The resulting Revised Bivariate Mixture (RBM) model has desirable properties that are fully consistent with empirical stylized facts, and the model also provides additional insights on price discovery process from a behavioural perspective. A multi-block Bayesian MCMC algorithm is proposed to estimate the model. The empirical results based on a sample of 8 stocks listed in the US stock market are summarized as follows. First, I find the existence of a common latent information flow process that drives the bivariate dynamics of return volatility and trading volume simultaneously, thus the empirical evidence is in favour of the Mixture of Distribution Hypothesis (MDH) of Clark [1973]. Second, the investors’ sentiment process is near unit root but the information flow process is much less persistent; this embedded two-factor structure is able to replicate the empirically observed autocorrelation functions of absolute return and trading volume. Third, the proportion of liquidity-driven trading volume is much higher in large-cap stocks than in small-cap tickers. Fourth, no statistical evidence is found to support the self-referential hypothesis in behaviour finance literature. Finally, there is strong evidence suggesting that the investors’ sentiment process might be a market-wide factor as the estimated latent sentiment processes are highly correlated within the sample of 8 stocks. In the second essay, I use the Stochastic Vector Multiplicative Error model (S-VMEM) of Hautsch [2008] to investigate on genuine multivariate intraday high-frequency dynamics between bid-ask spread, average dollar volume per trade, trade intensity and return volatility by taking into account the presence of serially correlated latent information flow. The simulation-based Maximum Likelihood with Efficient Importance Sampling (ML-EIS) technique is used to estimate the model. The main findings based on a sample of six heavily traded stocks listed in the US stock market are summarized as follows. First, the empirical evi- dence supports the Mixture of Distribution Hypothesis (MDH) even at 5-min fre- quency by revealing the existence of unobserved serially correlated information flow. Second, a strong contemporaneous genuine dependence between return volatility and the other three transaction variables is found. Third, the impact of information flow is most significant for return volatility and trade intensity. This finding is in sharp contrast with previous studies like Blume et al. [1994], Xu and Wu [1999], Huang and Masulis [2003] and Hautsch [2008], where the authors find that it is the average trade size instead of trade intensity that is most infor- mative about the quality of news. This changing behaviour reflects that market impact becomes an increasing important concern when investors execute their trades, and consequently, they tend to break large order into many small child orders. Thus the number of trades carries more informative content about hidden market event than the average trade size does. Finally, impulse response analysis shows that the dynamics of bid-ask spread is little affected by a positive shock in the underlying news arrival process, and thus provides no evidence to support the asymmetric information market microstructure theory. In the third essay, motivated by the fact that inflation swap provides a cleaner source than government-issued inflation-linked bond to analyse inflation dynamics, I fit the no-arbitrage joint term structure of nominal interest rate and breakeven inflation rate to zero coupon inflation swap data in US, UK and EU markets. The model is estimated using the three-step regression technique outlined in Abrahams et al. [2013]. The empirical evidence suggests that the no-arbitrage joint term structure is able to describe the dynamics of breakeven inflation rate very well in all three eveloped markets, indicated by small pricing errors observed in nominal yield curve and inflation swap curve. What’s more, most variation in long-term forward BEI is attributed to the time-varying risk premium whereas the forward in- flation expectation remains stable over time. Finally, the model-implied inflation expectation outperforms the unadjusted BEI in terms of forecasting short-term realized inflation. Thus the no-arbitrage joint term structure model is potentially of considerable interest to investors and policy markers to help them make more informative macro decisions.