Jeffrey R. Russell

Measuring and Modeling Execution Cost and Risk
Date Posted:Tue, 21 May 2019 13:28:18 -0500
Financial markets are considered to be liquid if a large quantity can be traded quickly and with minimal price impact. Although the idea of a liquid market involves both a cost as well as a time component, most measures of execution costs tend to focus on only a single number reflecting average costs and do not explicitly account for the temporal dimension of liquidity. In reality, trading takes time since larger orders are often broken up into smaller transactions or when limit orders are used. Recent work shows that the time taken to transact introduces a risk component in execution costs. In this setting, the decision can be viewed as a risk/reward tradeoff faced by the investor who can solve for a mean variance utility maximizing trading strategy. We introduce an econometric method to jointly model the expected cost and the risk of the trade thereby characterizing the mean variance tradeoffs associated of different trading approaches given market and order characteristics. We apply our methodology to a novel data set and show that the risk component is a non-trivial part of the transaction decision. The conditional distribution of transaction costs is also used to construct a new measure of liquidation risk that we refer to as liquidation value at risk (LVaR).

New: Measuring and Modeling Execution Cost and Risk
Date Posted:Mon, 29 Dec 2008 02:18:21 -0600
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 ...

New: Measuring and Modeling Execution Cost and Risk
Date Posted:Mon, 29 Dec 2008 02:17:44 -0600
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 ...

Measuring and Modeling Execution Cost and Risk
Date Posted:Mon, 03 Nov 2008 00:00:00 -0600
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 model the expected cost and risk associated with different order execution approaches. The model yields a risk/cost tradeoff that depends upon the state of the market and characteristics of the order. We show how to assess liquidation risk using the notion of liquidation value at risk (LVAR).

Measuring and Modeling Execution Cost and Risk
Date Posted:Mon, 03 Nov 2008 00:00:00 -0600
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 model the expected cost and risk associated with different order execution approaches. The model yields a risk/cost tradeoff that depends upon the state of the market and characteristics of the order. We show how to assess liquidation risk using the notion of liquidation value at risk (LVAR).

Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Condi...
Date Posted:Tue, 29 Apr 2008 10:50:50 -0500
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 ...

Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
Date Posted:Tue, 29 Apr 2008 10:49:51 -0500
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 ...

Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New A...
Date Posted:Tue, 29 Apr 2008 10:45:50 -0500
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 ...

Forecasting Transaction Rates: The Autoregressive Conditional Duration Model
Date Posted:Tue, 22 Apr 2008 07:54:04 -0500
This paper will propose a new statistical model for the analysis of data that does 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 observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and ...

Separating Microstructure Noise from Volatility
Date Posted:Sun, 02 Jan 2005 22:01:01 -0600
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 methodology to a sample of S&P100 stocks.

Separating Microstructure Noise from Volatility
Date Posted:Sun, 02 Jan 2005 17:01:01 -0600
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 ...

Forecasting Transaction Rates: The Autoregressive Conditional Duration Model
Date Posted:Wed, 30 Aug 2000 00:00:00 -0500
This paper will propose a new statistical model for the analysis of data that does 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 observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and more recently discussed by Stock (1988), Lamoureux and Lastrapes (1992), Muller et al. (1990) and Ghysels and Jasiak (1994) but does not require auxiliary data or assumptions on the causes of time flow. Strong evidence is provided for duration clustering beyond a deterministic component for the financial transactions data analyzed. We will show that a very simple version of the model can successfully account for the significant autocorrelations in the observed durations between trades of IBM stock on the consolidated market. A simple transformation of the duration data allows us to include volume in the model.

Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Conditional Duration Model
Date Posted:Sat, 22 Aug 1998 00:00:00 -0500
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 selectively thinning the sample, we develop a measure and forecasts for the intensity of price changes. This measure is related to standard measures of volatility but is formulated in a way that better captures the irregular sampling intervals that are inherent to high frequency financial data. Continuous-stochastic-process theorems for crossing times are used to derive an exact relationship between the intensity of price changes and standard volatility measures. The model might be useful for traders and allows tests that other variables are useful in forecasting the intensity of price changes. Generally, little support is found for price leadership, but other variables influence the intensity of price changes.

Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model
Date Posted:Fri, 14 Aug 1998 00:00:00 -0500
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 model proposed in Engle and Russell (1998) allows for joint modeling of the price transition probabilities and the arrival times of the transactions. The transition probabilities are formulated to allow general types of duration dependence. Estimation and testing are based on Maximum Likelihood methods. The data are IBM transactions from the TORQ dataset. Variations of the model allow for volume and spreads to impact the conditional distribution of price changes. Impulse response studies show the long run price impact of a transaction can be very sensitive to volume but is less sensitive to the spread and transaction rate.

Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
Date Posted:Tue, 21 Apr 1998 00:00:00 -0500
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 the Autoregressive Conditional Duration (ACD) model. Strong evidence is provided for transaction clustering for the financial transactions dataanalyzed, even after time-of-day effects are removed. Although the model is most naturally applied to the arrival of transactions, we suggest a thinning algorithm to model characteristics associated with the arrival times, allowing the investigator to model processes that are observed in irregular time intervals, not just the arrival times of the data. Models for transaction events, the flow of volume, and the rate of change for prices are estimated.