Nor Aishah Hamzah

Real interest rate parity in the ASEAN-5 countries: a nonlinear perspective

This article aims at testing real interest parity (RIP) by using nonlinear unit root tests. The results from Kapetanios et al. (2003) demonstrated that the adjustment of ASEAN-5 real interest rates towards real interest rates in Japan and the US follows a nonlinear (stationary) process. Overall, the evidence is in favour of RIP.

Inflation uncertainty and economic growth: evidence from the LAD ARCH model

In this paper, we combined the panel data and least absolute deviation autoregressive conditional heteroscedastic (ARCH) (L 1-ARCH) model to infer on the relationship between inflation uncertainty and economic growth in five emerging market economies. Two interesting findings emerged from the analysis; first, we confirmed that the inflation uncertainty has a significant and negative effect on economic growth. Second, inflation is also an important variable and it is detrimental to economic prospects in the fast-growing Association of Southeast Asian Nations (ASEAN) economies. All in all, the empirical findings suggest that greater stability in the economy may be desirable in order to stimulate economic growth in the region.

Feasible robust estimator in restricted semiparametric regression models based on the LTS approach

ABSTRACT Under some nonstochastic linear restrictions based on either additional information or prior knowledge in a semiparametric regression model, a family of feasible generalized robust estimators for the regression parameter is proposed. The least trimmed squares (LTS) method proposed by Rousseeuw as a highly robust regression estimator is a statistical technique for fitting a regression model based on the subset of h observations (out of n) whose least-square fit possesses the smallest sum of squared residuals. The coverage h may be set between n/2 and n. The LTS estimator involves computing the hyperplane that minimizes the sum of the smallest h squared residuals. For practical purpose, it is assumed that the covariance matrix of the error term is unknown and thus feasible estimators are replaced. Then, we develop an algorithm for the LTS estimator based on feasible methods. Through the Monte Carlo simulation studies and a real data example, performance of the feasible type of robust estimators is compared with the classical ones in restricted semiparametric regression models.

Entropy-based portfolio models: Practical issues

Entropy is a nonparametric alternative of variance and has been used as a measure of risk in portfolio analysis. In this paper, the computation of entropy risk for a given set of data is discussed with illustration. A comparison between entropy-based portfolio models is made. We propose a natural extension of the mean entropy portfolio to make it more general and diversified. In terms of performance, this new model is similar to the mean-entropy portfolio when applied to real and simulated data, and offers higher return if no constraint is set for the desired return; also it is found to be the most diversified portfolio model.

Robust estimation procedure in panel data model

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.

A nonparametric and diversified portfolio model

Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.

Locating shifts in the unconditional variance of time-dependent processes using multiscale wavelet periodogram

In this paper, we offer a proxy volatility measure based on maximum overlap discrete wavelet transform (MODWT) called multiscale wavelet periodogram (MSWP). We show that the multiscale wavelet periodogram (MSWP) is well equipped to detect multiple changes in the unconditional variance of a time-varying volatile process. Our simulation results demonstrate that use of MSWP as an input instead of the squared variable can overcome the size distortion problem associated with the CUSUM-type test for the detection of shifts in the unconditional variance. The power of the test based on wavelet periodograms is in fact comparable to that of the test based on squared variable. Our new approach is found successful for analyzing real data.