High Frequency最新文献

We apply the change-of-measure arguments of Shepp and Shiryaev (Theory of Probability and its Applications, 1994, 39, 103–119) to study the dual Russian option pricing problem proposed by Shepp and Shiryaev (Probability Theory and Mathematical Statistics: Lectures presented at the semester held in St. Peterburg, Russia, March 2 April 23, 1993, Amsterdam, the Netherlands: Gordon and Breach, 1996, pp. 209–218) as an optimal stopping problem for a one-dimensional diffusion process with reflection. We recall the solution to the associated free-boundary problem and give a solution to the resulting one-dimensional optimal stopping problem by using the martingale approach of Beibel and Lerche (Statistica Sinica, 1997, 7, 93–108) and (Theory of Probability and its Applications, 2000, 45, 657–669).

This study replicates previous studies in the context of China by determining whether exchange rate changes have a symmetric or asymmetric effect on stock prices. Moreover, it extends previous studies by examining whether the relationship between the underlying variables changes as a result of the global financial crisis. For this purpose, we use both linear and nonlinear ARDL models during the full sample, pre-crisis, and the post-crisis periods. The findings of this study suggest that exchange rate asymmetrically affects the stock prices in the long run only when the whole sample period is selected, whereas it symmetrically affects both in the long run and in the short run during the pre-crisis period and asymmetrically affects both in the long run and in the short run when the post-crisis period is selected. These findings indicate that the financial crisis causes asymmetric relationship between exchange rate changes and stock prices and may, therefore, be taken into consideration while making investment or policy decisions.

This research examines the use of the intensity-curvature functional (ICF) as filter in image space and in k-space. The novelty of this study is three-folded: (a) The evidence that the ICF calculated from three additional (International Journal of Imaging Systems and Technology, 28, 2018, 54) two-dimensional model polynomial functions is an image space filter; (b) An additional (The use of the intensity-curvature functional as k-space filter: Applications in magnetic resonance imaging of the human brain, 2018) ICF-based k-space filtering technique applicable to two-dimensional magnetic resonance images; (c) Results obtained through the calculation of the ICF of the trivariate cubic Lagrange model polynomial function (LGR3D). Although ICF-based k-space filtering delivers clear and well-defined images, ICF-based image space filtering remains superior when reconstructing vessel images in T2 MRI. The ICF of the LGR3D function provides sharp images too.

Using extreme value analysis, we investigate the tail risk behavior of the high-frequency (hourly) log returns of four most popular cryptocurrencies. The analysis is conducted on high-frequency returns data, estimating value at risk and expected shortfall with varying thresholds. We find that Ripple is the most risky cryptocurrency exhibiting the largest potential gain or loss for both positive and negative (hourly) log returns at every percentile and threshold. Bitcoin is the least risky cryptocurrency.

We study shot noise processes with Poisson arrivals and nonstationary noises. The noises are conditionally independent given the arrival times, but the distribution of each noise does depend on its arrival time. We establish scaling limits for such shot noise processes in two situations: (a) the conditional variance functions of the noises have a power law and (b) the conditional noise distributions are piecewise. In both cases, the limit processes are self-similar Gaussian with nonstationary increments. Motivated by these processes, we introduce new classes of self-similar Gaussian processes with nonstationary increments, via the time-domain integral representation, which are natural generalizations of fractional Brownian motions.

In this research, we develop a set of new measures to evaluate the data flow in the U.S. equity exchanges using Level I order book data. The quantities we develop and use to summarize trading activity are as follows: the activity-weighted spread and the activity-weighted return. We study the distribution of these two quantities and observe that there are significant changes in their behavior when equity markets are impacted by an external event. We focus the study on three exchanges: New York Stock Exchange (NYS), NASDAQ InterMarket (THM), and NYSE Arca (PSE) since they exhibit the largest impact to the proposed measures. We also study two different financial events which happened suddenly without any prior warning. These events are as follows: May 6, 2010, the “Flash Crash,” and April 23, 2013, the “Hoax tweet” event. Based on the results we obtain, the order flow dynamic is disturbed during these rare events. We quantify this change by measuring the number of detected “anomalies” for each exchange and equity studied. We believe the methodology we propose may be capable of detecting a potential unforecasted event as it is impacting the equity markets.

This paper solves the infinite-horizon optimal liquidation problem in a market with float-dependent, nonlinear temporary price impact. The investor's value function and optimal strategy are identified as the unique classical solutions of nonlinear parabolic partial differential equations. Depending on the price impact parameters, liquidation may require finite or infinite time.

In this work, a methodology is proposed to detect and predict the intention of cancelation of a large order at an optimal event-time horizon by analyzing real order flow market data. We achieve this by reconstructing the full history of the limit order book and formulate the case as a binary classification supervised learning problem. The results presented in this study suggest that using the information at the microstructure level of the order book is highly efficient for predicting and detecting the cancelation of the large order than using information at the macrostructure level, and that predicting the cancelation is marginally outperformed by the detection case. With this, we make a step forward in identifying potential orders related to price manipulation but the results can be used by institutional traders to anticipate adversary market impact produced by large orders.

We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In particular, we show that, in the presence of a negative drift, there exists a critical time t₀ > 0 such that, for any time horizon t > t₀, there exists an optimal placement, which, contrary to earlier work, is different from one that is placed “infinitesimally” close to the best ask, such as the best bid and second best bid. We also propose a simple method to approximate the critical time t₀ and the optimal order placement.