Journal of Financial Econometrics - current issue

Time-Varying Arrival Rates of Informed and Uninformed Trades

Easley, D., Engle, R. F., O'Hara, M., Wu, L. — 2008-03-10

We propose a dynamic econometric microstructure model of trading, and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity, market depth, and order flow. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for 16 actively traded stocks over 15 years of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades, which we use to construct forecasts of the probability of information-based trade (PIN). These forecasts are used in turn to forecast market liquidity as measured by bid-ask spreads and the price impact of orders. We observe that PINs vary across assets and over time, and most importantly that they are correlated across assets. Our analysis shows that one principal component explains much of the daily variation in PINs and that this systemic liquidity factor may be important for asset pricing. We also find that PINs tend to rise before earnings announcement days and decline afterwards.

Parameterizing Unconditional Skewness in Models for Financial Time Series

He, C., Silvennoinen, A., Terasvirta, T. — 2008-03-10

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate unconditional skewness. We consider modeling the unconditional mean and variance using models that respond nonlinearly or asymmetrically to shocks. We investigate the implications of these models on the third-moment structure of the marginal distribution as well as conditions under which the unconditional distribution exhibits skewness and nonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible, explicit analytical expressions provided for all third-order moments and cross-moments. Finally, we introduce a new tool, the shock impact curve, for investigating the impact of shocks on the conditional mean squared error of return series.

Estimating Value at Risk and Expected Shortfall Using Expectiles

Taylor, J. W. — 2008-03-10

Expectile models are derived using asymmetric least squares. A simple formula has been presented that relates the expectile to the expectation of exceedances beyond the expectile. We use this as the basis for estimating the expected shortfall. It has been proposed that the quantile be estimated by the expectile for which the proportion of observations below the expectile is . In this way, an expectile can be used to estimate value at risk. Using expectiles has the appeal of avoiding distributional assumptions. For univariate modeling, we introduce conditional autoregressive expectiles (CARE). Empirical results for the new approach are competitive with established benchmarks methods.

Kernel Conditional Quantile Estimation for Stationary Processes with Application to Conditional Value-at-Risk

Wu, W. B., Yu, K., Mitra, G. — 2008-03-10

The paper considers kernel estimation of conditional quantiles for both short-range and long-range-dependent processes. Under mild regularity conditions, we obtain Bahadur representations and central limit theorems for kernel quantile estimates of those processes. Our theory is applicable to many price processes of assets in finance. In particular, we present an asymptotic theory for kernel estimates of the value-at-risk (VaR) of the market value of an asset conditional on the historical information or a state process. The results are assessed based on a small simulation and are applied to AT&T monthly returns.

Detecting ARCH Effects in Non-Gaussian Time Series

Raunig, B. — 2008-03-10

Engles ARCH test has become the standard test for ARCH effects in applied work. Under non-normality the true rejection probability of this test can differ substantially from the nominal level, however. Bootstrap and Monte Carlo versions of the test may then be used instead. This paper proposes an alternative test procedure. The new test exploits the empirical distribution of the data and an extended probability integral transformation. The test is compared with the former tests in Monte Carlo experiments. Under normality, the new test works as well as the conventional Monte Carlo test and the bootstrap. Under non-normality, the test tends to be more accurate and more powerful than the bootstrapped ARCH test. The procedure is then used to test for ARCH effects in S&P 500 returns sampled at different frequencies. In contrast to the standard and the bootstrapped ARCH tests, the new test detects ARCH effects in the transformed low-frequency returns.