Modeling Firm-Size Distribution Using Box-Cox Heteroscedastic Regression

Using the Box-Cox regression model with heteroscedasticity, we examine the size distribution of firms. Analyzing the data set of Portuguese manufacturing firms as in Machado and Mata (2000), we show that our approach compares favorably against the Box-Cox quantile regression method. In particular, we are able to answer the key questions addressed by Machado and Mata, with the additional advantage that our empirical quantile functions are monotonic. Furthermore, confidence intervals of the regression quantiles are easy to compute, and the estimation of the Box-Cox heteroscedastic regression model is straightforward.Box-Cox transformation, Firm-size distribution, Quantile regression.

A Multivariate GARCH Model with Time-Varying Correlations

In this paper we propose a new multivariate GARCH model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By imposing some suitable restrictions on the conditional-correlation-matrix equation, we manage to construct a MGARCH model in which the conditional-correlation matrix is guaranteed to be positive definite during the optimisation. Thus, our new model retains the intuition and interpretation of the univariate GARCH model and yet satisfies the positive-definite condition as found in the constant-correlation and BEKK models. We report some Monte Carlo results on the finite-sample distributions of the QMLE of the varying-correlation MGARCH model. The new model is applied to some real data sets. It is found that extending the constant-correlation model to allow for time-varying correlations provides some interesting time histories that are not available in a constant-correlation model.

Expectations Formation and Forecasting of Vehicle Demand: An Empirical Study of the Vehicle Quota Auctions in Singapore

This paper studies the expectations formation and forecasting of vehicle demand in Singapore under the vehicle quota system. Under the system, a car buyer must first bid for a vehicle license in monthly auctions in order to purchase a new car. We construct an econometric model to test the hypothesis that past bid distributions of the license auctions contain information that car buyers can use to update their expectations about the intensity of market demand, forecast the license premiums and formulate their bidding strategies in future auctions. Our empirical analysis indicates that past bid distributions have a good degree of predictive power for the license premiums.quota licenses, vehicle demand, learning, expectations formation

Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading

This paper implements the Asymmetric Autoregressive Conditional Duration (AACD) model of Bauwens and Giot (2003) to analyze irregularly spaced transaction data of trade direction, namely buy versus sell orders. We examine the influence of lagged transaction duration, lagged volume and lagged trade direction on transaction duration and direction. Our results are applied to estimate the probability of informed trading (PIN) based on the Easley, Hvidkjaer and OHara (2002) framework. Unlike the Easley- Hvidkjaer-OHara model, which uses the daily aggregate number of buy and sell orders, the AACD model makes full use of transaction data and allows for interactions between buy and sell orders.Autoregressive Conditional Duration, Market microstructure, Probability of Informed Trading, Transaction Data, Weibull Distribution

A Survey on Physical Delivery Versus Cash Settlement in Futures Contracts

Published in International Review of Economics and Finance, 2006, https://doi.org/10.1016/j.iref.2004.08.001</p

Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Models Approach

Published in Journal of Business and Economic Statistics doi:10.1080/07350015.2012.707582</p

Forecasting large covariance matrix with high-frequency data: A factor correlation matrix approach

Published in Economics Letters, 2020, 195, 109465. DOI: 10.1016/j.econlet.2020.109465</p