Theo Nijman

Temporal aggregation of Garch processes

The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high frequency model can be derived from low frequency data. The common assumption in applications that rescaled innovations are independent is disputable, since it depends upon the available data frequency. Copyright 1993 by The Econometric Society.

Hedging Pressure Effects in Futures Markets

We present a simple model implying that futures risk premia depend on both own-market and cross-market hedging pressures. Empirical evidence from 20 futures markets, divided into four groups (financial, agricultural, mineral, and currency) indicates that, after controlling for systematic risk, both the futures own hedging pressure and cross-hedging pressures from within the group significantly affect futures returns. These effects remain significant after controlling for a measure of price pressure. Finally, we show that hedging pressure also contains explanatory power for returns on the underlying asset, as predicted by the model. Copyright The American Finance Association 2000.

Can cohort data be treated as genuine panel data

If repeated observations on the same individuals are not available it is not possible to capture unobserved individual characteristics in a linear model by using the standard fixed effects estimator. If large numbers of observations are available in each period one can use cohorts of individuals with common characteristics to achieve the same goal, as shown by Deaton (1985). It is tempting to analyze the observations on cohort averages as if they are observations on individuals which are observed in consecutive time periods. In this paper we analyze under which conditions this is a valid approach. Moreover, we consider the impact of the construction of the cohorts on the bias in the standard fixed effects estimator. Our results show that the effects of ignoring the fact that only a synthetic panel is available will be small if the cohort sizes are sufficiently large (100, 200 individuals) and if the true means within each cohort exhibit sufficient time variation.

Testing for Mean‐Variance Spanning with Short Sales Constraints and Transaction Costs: The Case of Emerging Markets

We propose regression-based tests for mean-variance spanning in the case where investors face market frictions such as short sales constraints and transaction costs. We test whether U.S. investors can extend their efficient set by investing in emerging markets when accounting for such frictions. For the period after the major liberalizations in the emerging markets, we find strong evidence for diversification benefits when market frictions are excluded, but this evidence disappears when investors face short sales constraints or small transaction costs. Although simulations suggest that there is a possible small-sample bias, this bias appears to be too small to affect our conclusions. Copyright The American Finance Association 2001.

Testing for mean-variance spanning: a survey

Abstract In this paper, we present a survey on the various approaches that can be used to test whether the mean-variance frontier of a set of assets spans or intersects the frontier of a larger set of assets. We analyze the restrictions on the return distribution that are needed to have mean-variance spanning or intersection. The paper explores the duality between mean-variance frontiers and volatility bounds, analyzes regression-based test procedures for spanning and intersection, and shows how these regression-based tests are related to tests for mean-variance efficiency, performance measurement, optimal portfolio choice and specification error bounds.

Incomplete Panels and Selection Bias

In this chapter attention will be paid to selection bias in panel data. In case of selection bias a rule other than simple random sampling determines how sampling from the underlying population takes place. This selection rule may distort the representation of the true population and consequently distort inferences based on the observed data using standard methods. Distorting selection rules may be the outcome of self—selection decisions of agents, non-response decisions of agents or decisions of sample survey statisticians. Many existing panel data sets suffer from missing observations due to nonresponse of agents or design decisions of survey statisticians. Both sources of missing observations may imply a non—random selection rule. Additionally, in many economic applications decisions of individual agents imply a distorting selection rule. Examples of these types of self—selection are the endogenous decisions to join the labor force or to participate in some social program.

A comparison of the cost of trading French shares on the Paris Bourse and on SEAQ International

Abstract This paper analyses the cost of trading French shares on two exchanges, the Paris Bourse and Londons SEAQ International. Using a large data set consisting of all quotes, limit orders and transactions for a two month period, it is shown that for small transactions the Paris Bourse has lower implicit transaction costs, measured by both the effective and quoted bid-ask spread. The market in London, however, is deeper and provides immediacy for much larger trades. Moreover, we find that the cost of trading is decreasing in trade size, rather than increasing over the range of trade sizes that we examine. This suggests that order processing costs are an important determinant of bid-ask spreads, since competing market microstructure theories (adverse selection, inventory control) predict bid-ask spreads increasing in trade size.

An Anatomy of Commodity Futures Risk Premia

We identify two types of risk premia in commodity futures returns: spot premia related to the risk in the underlying commodity, and term premia related to changes in the basis. Sorting on forecasting variables such as the futures basis, return momentum, volatility, inflation, hedging pressure, and liquidity, results in sizable spot premia in the high-minus-low sorted portfolios between 5% and 14% per annum and term premia between 1% and 3% per annum. We show that a single factor, the high-minus-low portfolio from basis sorts, explains the cross-section of spot premia. Two additional basis factors are needed to explain the term premia.

High frequency analysis of lead-lag relationships between financial markets☆

High frequency data are often observed at irregular intervals, which complicates the analysis of lead-lag relationships between financial markets. Frequently, estimators have been used that are based on observations at regular intervals, which are adapted to the irregular observations case by ignoring some observations and imputing others. In this paper we propose an estimator that avoids imputation and uses all available transactions to calculate (cross) covariances. This creates the possibility to analyze lead-lag relationships at arbitrarily high frequencies without additional imputation bias, as long as weak identifiability conditions are satisfied. We also provide an empirical application to the lead-lag relationship between the SP500 index and futures written on it.

Evaluating Style Analysis

In this paper we evaluate applications of (return based) style analysis. The portfolio and positivity constraints imposed by style analysis are useful in constructing mimicking portfolios without short positions. Such mimicking portfolios can be used e.g. to construct efficient portfolios of mutual funds with desired factor loadings if the factor loadings in the underlying factor model are positively weighted portfolios. Under these conditions style analysis may also be used to determine a benchmark portfolio for performance measurement. Attribution of the returns on portfolios of which the actual composition is unobserved to specific asset classes on the basis of return based style analysis is attractive if moreover there are no additional cross exposures between the asset classes and if fund managers hold securities that on average have a beta of one relative to their own asset class. If such restrictions are not met, and in particular if the factor loadings do not generate a positively weighted portfolio, the restrictions inherent in return based style analysis distort the outcomes of standard regression approaches rather than that the analysis is improved. The size of the distortions is illustrated by considering empirical results on style analysis of US mutual funds.