ON THE (INTRADAILY) SEASONALITY AND DYNAMICS OF A FINANCIAL POINT PROCESS: A SEMIPARAMETRIC APPROACH.

A component model for the analysis of financial durations is proposed. The components are the long-run dynamics and the seasonality. The later is left unspecified and the former is assumed to fall within the class of certain family of parametric functions. The joint model is estimated by maximizing a (local) quasi-likelihood function, and the resulting nonparametric estimator of the seasonal curve has an explicit form that turns out to be a transformation of the Nadaraya-Watson estimator. The estimators of the parameters of interest are shown to be root-N consistent and asymptotically efficient. Furthermore, the seasonal curve is also estimated consistently. The methodology is applied to the trade duration process of Bankinter, a medium size Spanish bank traded in Bolsa de Madrid. We show that adjusting data by seasonality produces important misspecifications.

Financial markets as a complex system: A short time scale perspective

In this paper we want to discuss macroscopic and microscopic properties of financial markets. By analyzing quantitatively a database consisting of 13 minute per minute recorded financial time series, we identify some macroscopic statistical properties of the corresponding markets, with a special emphasize on temporal correlations. These analysis are performed by using both linear and nonlinear tools. Multivariate correlations are also tested for, which leads to the identification of a global coupling mechanism between the considered stock markets. The application of a new formalism, called transfer entropy, allows to measure the information flow between some financial time series. We then discuss some key aspects of recent attemps to model financial markets from a microscopic point of view. One model, that is based on the simulation of the order book, is described more in detail, and the results of its practical implementation are presented. We finally address some general aspects of forecasting and modeling, in particular the role of stochastic and nonlinear deterministic processes. --time series analysis,econophysics,simulated markets,temporal correlations,high-frequency data

Modeling the Impacts of Market Activity on Bid-Ask Spreads in the Option Market

In this paper, we examine the impact of market activity on the percentage bid-ask spreads of S&P 100 index options using transactions data. We propose a new market microstructure theory which we call derivative hedge theory, in which option market percentage spreads will be inversely related to the option market maker's ability to hedge his positions in the underlying market, as measured by the liquidity of the latter market. In a perfect hedge world, spreads arise from the illiquidity of the underlying market, rather than from inventory risk or informed trading in the option market itself. We find option market volume is not a significant determinant of option market spreads. This finding leads us to question the use of volume as a measure of liquidity and supports the derivative hedge theory. Option market spreads are positively related to spreads in the underlying market, again supporting our theory. However, option market duration does affect option market spreads, with very slow and very fast option markets both leading to bigger spreads. The fast market result would be predicted by the asymmetric information theory. Inventory model predicts big spreads in slow markets. Neither result would be observed if the underlying securities market provided a perfect hedge. We interpret these mixed results as meaning that the option market maker is able to only imperfectly hedge his positions in the underlying securities market. Our result of insignificant options volume casts doubt on the price discovery argument between stock and option market (Easley, O'Hara, and Srinivas (1998)). Asymmetric information costs in either market are naturally passed to the other market maker's hedgeing and therefore it is unimportant where the informed traders trade.

Optimal trading with frictions

This thesis studies the optimal trading problem with particular attention to frictions, taking alpha signals as given in several practical settings in modern financial markets. Chapter 2 provides a reduced-form model for price impact of market orders. As a scaling limit of the econo-physics propagator model, it has both tractability for optimization and good empirical fit. The nonlinearity in propagator model is explained as a effect of intraday stochasticity of the market activity. Optimal trading strategies are given for the case of stochastic alpha signal and volume signals in closed-form solutions. Moreoever, concrete bounds for the absence of price manipulation strategies are provided. Chapter 3 derives an actionable derivatives hedging strategy with both market and limit orders from the perspective of a central risk book. It is found that limit order is only beneficial for delta-hedging when the gamma of the risky position is negative. Additionally, the interaction between transaction cost, adverse selection and risk aversion can be characterized by a nonlinear PDE that describes the option price. According to empirical analysis, tactical liquidity provision is beneficial for non-competitive market makers for reasonable trading frequencies. Chapter 4 studies the usage of display and nondisplay limit orders for order execution. A price impact model is postulated and the corresponding scheduling algorithm is derived. In the case where nondisplay limit order (hidden order) is used, there is a time which separates the trading horizon into two regimes: the former only uses hidden order, and the latter uses the mixture of limit and hidden orders. The effectiveness and robustness of the algorithm is shown via numerical testing in both simulated data and NASDAQ 100 Index data.Open Acces

The Augmented ACD Models: High Frequency Modelling and Applications to BVMT Stocks

We propose in this paper, a new work to model the durations between successive transactions of the Stock Exchange of Tunis (BVMT). For this purpose, the autoregressive approach of the ACD model will be extended to the class of augmented ACD models to model the data that arrive at irregularly spaced intervals in time called high-frequency data or Ultra-high frequency data. The choice of the interval remains crucial since the daily exchanges are too small

Structure Learning and Break Detection in High-Frequency Data

The accurate learning of the underlying structure in high-frequency data has become critical in the analysis of time series for capturing valuable information that facilitates decision-making. The time series data in finance often is large, dynamic, heterogeneous and even structural unstable. Each aspect of these characteristics will add a degree of difficulty in efficient analysis. The goal of this dissertation is to discover the latent structure of dynamic high-frequency data that may have structural breaks, from both univariate and network perspective. We focus our analysis on durations between user-defined events in transaction-by-transaction stock prices from the Trade and Quotes (TAQ) data base at Wharton Research Data Services (WRDS). Our proposed approach can be easily adapted to other models. The dissertation has three main contributions. First, we propose a fast and accurate distribution-free approach using penalized martingale estimating functions on logarithmic autoregressive conditional duration (Log ACD) models. We discuss three approaches for parameter estimation. Our approach employs effective starting values from an approximating time series model and provides investigators accurate fits and predictions that can assist in trading decisions. Second, we propose a sequential monitoring scheme to detect structural breaks in the estimated parameters of a univariate piecewise Log ACD model. Based on martingale estimating function, this scheme does not require any distributional assumption. This monitoring scheme can detect structural breaks and choose model orders at the same time. Assuming data is given, we compare the performance of our scheme with that of a state-of-the-art offline scheme via simulation studies. Third, we propose a framework for detecting structural breaks in dynamic networks of a large number of stocks. In particular, we discover unobserved dynamic network structure from nodal observations governed by both the latent network and time. Our empirical analysis on the 30 most liquid stocks in S&P100 is an exploratory study. Such an analysis would be useful to economists studying the structural breaks in financial networks

Modelling bid-ask spreads in competitive dealership markets

pricing;estimation;asset valuation