Volume 16, Issue 3

There has been an increasing interest in the financial econometrics literature on the properties of non-linear transformations of returns. In this paper, we focus on power transformations and analyze which powers of returns provide stronger autocorrelations in the context of stochastic volatility (SV) models. We show that the sample and theoretical autocorrelations implied by short and long memory SV models peak at the same power transformation, which is close to one for realistic values of the model parameters. Consequently, we suggest that the power that maximizes the sample autocorrelations could be used as an additional descriptive tool for the adequacy of a SV model. The results are illustrated with three real series of financial returns.

We study how household borrowing constraints shape house price dynamics. We consider the fully non-linear dynamics following large aggregate shocks in a calibrated OLG model with standard preferences. We find that the main effect of a down payment constraint is to make house price dynamics asymmetric between large positive and large negative income shocks: prices increase rapidly following the impact effect of a large adverse income shock but decline slowly following the impact effect of a positive income shock. This asymmetry stems from the fact that the share of borrowing constrained households changes over time. However, the down payment constraint does not substantially magnify the impact effect of adverse income or interest rate shocks.

We propose a TAR(3,1)-GARCH(1,1) model able to describe two different types of extreme events: a first type generated by large uncertainty regimes and a second type where extremes come from isolated dread/joy events. The novelty of this model resides on the definition of the regimes, motivated by the occurrence of extreme values, and of the threshold variable, defined by the shock affecting the process one period lagged. The model is able to uncover dependence and clustering of extremes in high and low volatility periods. A Wald type test to detect nonlinearities on the conditional mean process defined by an unobservable threshold variable is introduced. In the empirical application, we find evidence of predictability for extreme returns on SPDR S&P500 fund during the recent crisis period, July 2008 to March 2011. This finding seems to support the presence of some persistence and mean reversion in the dynamics of returns after the occurrence of extreme shocks.

This paper models the return series of USD/CNY exchange rate by considering the conditional mean and conditional volatility simultaneously. An index type functional-coefficient model is adopted to model the conditional mean part and a GARCH type model with a policy dummy variable is applied to the conditional volatility model. We show that the government policy indeed has an impact on the exchange rate dynamic. To evaluate the out-of-sample forecasting ability, a prediction interval is computed by employing nonparametric conditional quantile regression. Our method outperforms other popular models in terms of various criteria.

The increased availability of high-frequency financial data has imposed new challenges for its denoising analysis since the data exhibits heavy tails and long-memory effects that render the application of traditional methods difficult. In this paper, we introduce the local linear scaling approximation (in short, LLSA), which is a nonlinear filtering algorithm based on the linear maximal overlap discrete wavelet transform (MODWT). We show the unique properties of LLSA and compare its performance with MODWT. We empirically show the superior performance of LLSA in smoothing analysis (i.e., trend extraction) of high- frequency data from German equity market. Based on our results we conclude that LLSA is reliable and suitable for high-frequency data denoising analysis.

In this paper, we study capital and labor adjustment costs by allowing interrelation among adjustments when estimating a dynamic model of factor demand for Greek manufacturing firms. A balanced panel dataset of 1299 firms on 9093 observations for the period 1998-2004 has pointed to strong evidence of (a) inaction, (b) lumpiness, and (c) interrelation of factor adjustment. On account of these empirical observations, we set up and estimate models with alternative factor adjustment costs, using the Method of Simulated Moments. We show that a model incorporating convex, non convex, and interrelation adjustment processes fits the data best. Moreover, the results indicate that (i) labor and capital adjustment should be analyzed together, (ii) capital and labor’s slow adjustment is generated and can be explained by costs associated with changing input demand, and (iii) the firm has an incentive to adjust input factors at the same time period, since adjustment costs decrease when the firm decides to adjust the two factors, namely capital and labor, simultaneously.