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on Market Microstructure |
| By: | Lorenzo Camponovo (University of Surrey); Yukitoshi Matsushita (Tokyo Institute of Technology); Taisuke Otsu (London School of Economics) |
| Abstract: | We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson’s x^2 as special cases, is not asymptotically pivotal under the so-called infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. We show that multiplying a correction term recovers the x^2 limiting distribution. Furthermore, we establish Bartlett correction for our modified nonparametric likelihood statistic under the constant and general non-constant volatility cases. In contrast to the existing literature, the empirical likelihood statistic is not Bartlett correctable under the infill asymptotics. However, by choosing adequate tuning constants for the power divergence family, we show that the second order refinement to the order O(n^{-2}) can be achieved. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:sur:surrec:0318&r=mst |
| By: | Mohammad Davoodalhosseini |
| Abstract: | A model of over-the-counter markets is proposed. Some asset buyers are informed in that they can identify high quality assets. Heterogeneous sellers with private information choose what type of buyers they want to trade with. When the measure of informed buyers is low, there exists a unique and stable equilibrium, and interestingly, price, trading volume and welfare typically decrease with more informed buyers. When the measure of informed buyers is intermediate, multiple equilibria arise, and price, trading volume and welfare may decrease or increase with more informed buyers, depending on the equilibrium being played. A switch from one equilibrium to another can lead to large drops in liquidity, price, trading volume and welfare, like a financial crisis. The measure of informed buyers is then endogenized by allowing buyers to invest in a technology that enables them to identify high quality assets. In this case, the model features endogenous strategic complementarity in acquiring the information technology. Multiple equilibria still exist, with different measures of informed buyers, but a scheme of tax/subsidy on information acquisition sometimes leads to the unique equilibrium. |
| Keywords: | Economic models, Financial markets, Financial stability, Financial system regulation and policies, Market structure and pricing |
| JEL: | D40 D82 D83 G01 G10 G20 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:18-7&r=mst |
| By: | Kenneth R. Ahern |
| Abstract: | This paper exploits hand-collected data on illegal insider trades to test whether standard illiquidity measures can detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that only absolute order imbalance and the negative autocorrelation of order flows are statistically and economically robust predictors of insider trading. However, this result only holds for short-lived information. When information is long-lived, none of the measures of illiquidity I consider detect informed trading, including bid-ask spreads, Kyle's lambda, and Amihud illiquidity. These results suggest that standard measures of illiquidity have limited applications. |
| JEL: | D53 D83 D85 G12 G14 K42 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24297&r=mst |
| By: | Marie Chen; Corey Garriott |
| Abstract: | Using bond futures data, we test whether high-frequency trading (HFT) is engaging in back running, a trading strategy that can create costs for financial institutions. We reject the hypothesis of back running and find instead that HFT mildly improves trading costs for institutions. After a rapid increase in the number of HFTs, trading costs as measured by implementation shortfall decrease by 27 basis points for smaller-sized positions ($2–$10 million notional). For larger-sized positions there is no significant effect. We explain the improvement as being the consequence of HFT reducing effective spreads and per-trade price impacts. |
| Keywords: | Financial markets, Financial system regulation and policies, Market structure and pricing |
| JEL: | G20 G14 L10 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:18-8&r=mst |
| By: | Davide Pettenuzzo (Brandeis University); Riccardo Sabbatucci (Stockholm School of Economics); Allan Timmermann (University of California San Diego) |
| Abstract: | We develop a new approach to modeling high-frequency dynamics in cash flows extracted from daily firm-level dividend announcements. Daily cash flow news follows a noisy process that is dominated by outliers so our approach decomposes this series into a persistent component, large but infrequent jumps, and temporary shocks with time-varying volatility. Empirically, we find that the persistent cash flow growth component is a better predictor of future dividend growth than alternative predictors from the literature. We also find strong evidence that news about the persistent cash flow component has a significantly positive effect on same-day stock market returns, while news about the temporary cash flow components has little effect on returns. Negative jumps in the cash flow process and higher cash flow volatility are associated with elevated stock market volatility and a higher probability of observing a jump in daily stock returns. These findings suggest that high-frequency news about the underlying cash flow growth process is an important driver not only of average stock market performance but also of the volatility and jump probability of stock prices. |
| Keywords: | High-frequency cash flow news; predictability of dividend growth; jump risk; dynamics in stock returns; Bayesian modeling |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:120&r=mst |
| By: | Georgios Bampinas (Department of Economics, University of Macedonia, Greece); Theodore Panagiotidis (Department of Economics, University of Macedonia, Greece; Rimini Centre for Economic Analysis); Christina Rouska (Department of Economics, University of Macedonia, Greece) |
| Abstract: | This study explores the relationship between Google search activity and the conditional volatility of oil and gold spot market returns. By aggregating the volume of queries related to the two commodity markets in the spirit of Da et al. (2015), we construct a weekly Searching Volume Index (SVI) for each market as proxy of households and investors information demand. We employ a rolling EGARCH framework to reveal how the significance of information demand has evolved through time. We find that higher information demand increases conditional volatility in gold and oil spot market returns. Information flows from Google SVIs reduce the proportion of the significant volatility asymmetry produced by negative shocks in both commodity markets. The latter is more profound in the gold market. |
| Keywords: | Gold, Oil, Google Trends, Volatility, Asymmetry, EGARCH |
| JEL: | C01 C32 C38 C51 C81 D81 G02 G11 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:18-13&r=mst |