We explore the relationship between liquidity of a firm's equity and its capital structure. Firms with more liquid stocks benefit from lower costs of equity issuance. Therefore, it is hypothesized that such firms are likely to have a preference for equity in their capital structure. This article empirically investigates the relationship between liquidity and capital structure decisions on a sample of Indian firms. Contrary to the existing literature, we find no empirical evidence for an inverse relationship between liquidity and leverage among Indian firms. The results are indicative of the fact that due to distinctive features of emerging markets, namely, less sophisticated capital markets, higher information asymmetry, concentrated ownership, constrained access to debt and prevalence of family owned businesses, there are other more significant determinants of capital structure that subsume the explanatory power of liquidity variables.
Purpose
– The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.
Design/methodology/approach
– Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.
Findings
– It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.
Originality/value
– This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.
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