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on Market Microstructure |
| By: | Viet Hoang Nguyen (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Yongcheol Shin (Leeds University Business School, The University of Leeds) |
| Abstract: | We generalize the portfolio shifts model advanced by Evans and Lyons (2002a; b), and develop the dynamic asymmetric portfolio shifts (DAPS) model by explicitly allowing for possible market under- and overreactions and for asymmetric pricing impacts of order flows. Using the Reuters D2000-1 daily trading data for eight currency markets over a four-month period from 1 May to 31 August 1996, we find strong evidence of a nonlinear cointegrating relationship between exchange rates and (cumulative) order flows: The price impact of negative order flows (selling pressure) is overwhelmingly stronger than that of the positive ones (buying pressure). Through the dynamic multiplier analysis, we find two typical patterns of the price discovery process. The markets following overreactions tend to display a delayed overshooting and a volatile but faster adjustment towards equilibrium whereas the markets following underreactions are generally characterized by a gradual but persistent adjustment. In our model, these heterogeneous adjustment patterns reflect different liquidity provisions associated with different market conditions following under- and overreactions. In addition, the larger is the mispricing, the faster is the overall adjustment speed, a finding consistent with Abreu and Brunnermeier (2002) and Cai et al. (2011). We also find that underreactions are followed mostly by positive feedback trading while overreactions are characterized by delayed overshooting in the short run but corrected by negative feedback trading at longer horizons, the finding is consistent with Barberis et al. (1998) who show that positive short-run autocorrelations (momentum) signal underreaction while negative long-run autocorrelations (reversal) signal overreaction. |
| Keywords: | Exchange rate, order flow, under- and overreaction, asymmetric pricing impacts, asymmetric cointegrating relationship and dynamic multipliers |
| JEL: | C22 F31 G15 |
| Date: | 2011–06 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2011n14&r=mst |
| By: | Toshiaki Watanabe |
| Abstract: | This article applies the realized GARCH model, which incorporates the GARCH model with realized volatility (RV), to quantile forecasts of financial returns such as Value-at-Risk and expected shortfall. This model has certain advantages in the application to quantile forecasts because it can adjust the bias of RV casued by microstructure noise and non-trading hours and enables us to estimate the parameters in the return distribution jointly with the other parameters. Student's t- and skewed strudent's t-distributions as well as normal distribution are used for the return distribution. The EGARCH model is used for comparison. Main results for the S&P 500 stock index are: (1) the realized GARCH model with the skewed student's t-distribution performs better than that with the normal and student's t-distributions and the EGARCH model using the daily returns only, and (2) the performance does not improve if the realized kernel, which takes account of microstructure noise, is used instead of the plain realized volatility, implying that the realized GARCH model can adjust the bias of RV caused by microstructure noise. |
| Keywords: | Expected shortfall, GARCH, Realized volatility, Skewed student's t-distribution, Value-at-Risk |
| JEL: | C52 C53 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:hst:ghsdps:gd11-195&r=mst |
| By: | Chew Lian Chua (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Sandy Suardi (School of Economics and Finance, La Trobe University); Sarantis Tsiaplias (KPMG, Australia) |
| Abstract: | This paper examines the forecasting qualities of Bayesian Model Averaging (BMA) over a set of single factor models of short-term interest rates. Using weekly and high frequency data for the one-month Eurodollar rate, BMA produces predictive likelihoods that are considerably better than the majority of the short-rate models, but marginally worse off than the best model in each dataset. We observe preference for models incorporating volatility clustering for weekly data and simpler short rate models for high frequency data. This is contrary to the popular belief that a diffusion process with volatility clustering best characterizes the short rate. |
| Keywords: | Bayesian model averaging, out-of-sample forecasts |
| JEL: | C11 C53 |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2011n01&r=mst |