New: Measuring and Modeling Execution Cost and Risk
Date Posted: Dec 29, 2008
We introduce a new analysis of transaction costs that explicitly recognizes the importance of the timing of execution in assessing transaction costs. Time induces a risk/cost tradeoff. The price of immediacy results in higher costs for quickly executed orders while more gradual trading results in higher risk since the value of the asset can vary more over longer periods of time. We use a novel data set that allows a sequence of transactions to be associated with individual orders and measure and
New: Measuring and Modeling Execution Cost and Risk
Date Posted: Dec 29, 2008
We introduce a new analysis of transaction costs that explicitly recognizes the importance of the timing of execution in assessing transaction costs. Time induces a risk/cost tradeoff. The price of immediacy results in higher costs for quickly executed orders while more gradual trading results in higher risk since the value of the asset can vary more over longer periods of time. We use a novel data set that allows a sequence of transactions to be associated with individual orders and measure and
New: Microstructure Noise, Realized Variance, and Optimal Sampling
Date Posted: May 29, 2008
A recent and extensive literature has pioneered the summing of squared observed intra-daily returns, realized variance, to estimate the daily integrated variance of financial asset prices, a traditional object of economic interest. We show that, in the presence of market micro-structure noise, realized variance does not identify the daily integrated variance of the frictionless equilibrium price. However, we demonstrate that the noise-induced bias at very high sampling frequencies can be appropr
Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Condi...
Date Posted: Apr 29, 2008
This paper applies the Autoregressive Conditional Duration model to Foreign Exchange quotes arriving on Reuter's screens. The Autoregressive Conditional Duration model, proposed in Engle and Russell (1995), is a new statistical model for the analysis of data that do not arrive in equal time intervals. When Dollar/Deutschmark data are examined, it is clear that many of the price quotes carry little information about the price process, as they are simply repeats of the previous quote. By selective
Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
Date Posted: Apr 29, 2008
This paper proposes a new statistical model for the analysis of data that do not arrive in equal time intervals, such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between events as a stochastic time varying process. We propose a new model for point processes with intertemporal correlation. Because the model focuses on the time interval between events it is called th
Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New A...
Date Posted: Apr 29, 2008
This paper proposes a new approach to modeling financial transactions data. A model for discrete valued time series is introduced in the context of generalized linear models. Since the model specifies probabilities of return outcomes conditional on both the previous state and the historic distribution, we call the it the Autoregressive Conditional Multinomial (ACM) model. Recognizing that prices are observed only at transactions, the process is interpreted as a marked point process. The ACD m
Separating Microstructure Noise from Volatility
Date Posted: Jan 03, 2005
There are two volatility components embedded in the returns constructed using recorded stock prices: the genuine time-varying volatility of the unobservable returns that would prevail (in equilibrium) in a frictionless, full-information, economy and the variance of the equally unobservable microstructure noise. Using straightforward sample averages of high-frequency return data recorded at different frequencies, we provide a simple technique to identify both volatility features. We apply our met