Periodically collapsing Evans bubbles and stock-price volatility

This paper analyzes stock-price volatility in the presence of periodically collapsing Evans bubbles. We derive a volatility formula that establishes a link between the bubble component and stock-price volatility. We demonstrate how to fit the volatility equation to stock-market data.     

Some cautions on the use of nonlinear panel unit root tests: Evidence from a modified series-specific non-linear panel unit-root test

The purpose of this paper is to examine the relevance of applying nonlinear panel unit root test to examine the non-linear mean reversion behaviors of real exchange rates. We find that nonlinear panel unit root test may achieve lower power performance as compared to its alternative of linear panel unit test when the data generating process does not contain significant non-linear components. This finding post cautions to researchers in modeling and testing real exchanges behavior. We also develop a modified series-specific nonlinear panel unit root test and find evidence in favor of purchasing power parity hypothesis for China's four ASEAN trading partners in the period of February 1997 to August 2009.     

Asset bubbles in an overlapping generations model with endogenous labor supply

This paper examines the effects of asset bubbles in an overlapping generations model with endogenous labor supply. We show analytically that asset bubbles can lead to an expansion in steady-state capital, investment, employment and output under certain conditions.     

Estimating GARCH volatility in the presence of outliers

GARCH volatilities depend on the unconditional variance, which is a non-linear function of the parameters. Consequently, they can have larger biases than estimated parameters. Using robust methods to estimate both parameters and volatilities is shown to outperform Maximum Likelihood procedures.     

Inflation in South Africa. A long memory approach

This paper deals with the analysis of the inflation rate in South Africa for the time period 1970M1-2008M12. We use long range dependence techniques and the results show that inflation in this country is a covariance stationary process with long range dependence, with an order of integration ranging in the interval (0, 0.5). Policy implications are derived.     

A CUSUM test for a long memory heterogeneous autoregressive model

A CUSUM test is proposed for testing structural breaks in a long-memory heterogeneous autoregressive model. The limiting distribution of the CUSUM test is shown to be a simple function of a standard Brownian bridge, contrasting with the nuisance parameter dependent asymptotics of other CUSUM tests based on fractional integration models. A Monte-Carlo experiment investigates finite sample size and power of the test. The proposed test is applied to a set of daily realized volatilities of the log-return of the Korean Won US Dollar exchange rate to reveal some evidence of a break in addition to a long-memory.     

Comovements among U.S. state housing prices: Evidence from fractional cointegration

This study investigates the relationship between U.S. state housing prices and overall U.S. housing prices as well as the relationship among state housing prices using fractional integration and cointegration techniques. The results based on parametric and semiparametric estimators reveal that some states contain unit roots though we fail to find cointegrating relations between U.S. states housing prices and the overall U.S. housing prices as well as among state housing prices. The results raise doubts regarding the long-run convergence in U.S. state housing prices and the presence of the ripple effect.     

A bootstrap test for jumps in financial economics

An i.i.d. bootstrap is applied for the ratio test of Barndorff-Nielsen and Shephard (2006) for jumps in jump diffusion processes. Asymptotic validity is established for the bootstrap test both under the null of no jump and under the alternative of jumps. Finite sample simulation shows that the bootstrap test has more stable size than the ratio test of Barndorff-Nielsen and Shephard (2006).     

DO PERMANENT SHOCKS EXPLAIN INCOME LEVELS? A COMMON CYCLE–COMMON TREND ANALYSIS OF REGIONAL INCOME LEVELS FOR CHINA

Abstract. In this paper, we analyse per capita income levels of China's three main regions: the western region, the eastern region and the central region using common cycle and common trend tests. Our main contribution is that we impose the common cycle and common trend restrictions in decomposing shocks into permanent and transitory components. We find that: (i) there is evidence for two cointegrating relationships and one common cycle; and (ii) the variance decomposition analysis of shocks provides evidence that over short horizons, permanent shocks play a large role in explaining variations in regional per capita incomes.     

The energy consumption-real GDP nexus revisited: Empirical evidence from 93 countries

In this paper, we analyse the long-run relationship between energy consumption and real GDP for 93 countries. We find mixed results on the impact of energy consumption on real GDP, with greater evidence at the country level supporting energy consumption having a negative causal effect on real GDP. For the G6 panel of countries, we find significant evidence that energy consumption negatively Granger causes real GDP. This means that for countries where energy consumption has a negative long-run causal effect on real GDP, energy conversation policies should not retard economic growth. We identify these countries and regional panels. We argue that these countries/regions should play a greater role in reducing carbon dioxide emissions.     

EXAMINING THE ASYMMETRIC BEHAVIOUR OF MACROECONOMIC AGGREGATES IN ASIAN ECONOMIES

Abstract. The goal of this paper is to test for asymmetric behaviour of macroeconomic aggregates for three Asian economies; namely, Malaysia, Hong Kong and Korea. Whether macroeconomic aggregates can be characterised as asymmetric has important implications for policy‐making and econometric modelling including forecasting. We examine two forms of asymmetries; specifically deepness, which arises when a detrended time series contains an asymmetric distribution, and steepness, which arises when the first difference of a series contains an asymmetric distribution. Overall, our findings suggest that for all three countries, the bulk of the series display asymmetry behaviour.     

On the origin of high persistence in GARCH-models

We show that the (Baillie and Chung, 2001) minimum distance estimates of the GARCH (1,1) model induce spurious persistence in the volatility when there are structural changes in the mean of the process.     

Testing of the mean reversion parameter in continuous time models

In this paper we use the approximate bias expressions developed in Yu (2012) and Bao et al. (2013) to improve the testing of the ordinary least squares or quasi-maximum likelihood estimator of the mean reversion parameter in continuous time models. We follow the approach given in Iglesias and Phillips (2005) and Chambers (2013), where if we bias correct the estimated mean reversion parameter, we can improve on the small sample properties of the testing procedure. Simulation results confirm the usefulness of this approach using a t$t$-statistic in this setting in the near unit root situation when the mean reversion parameter is approaching its lower bound. Therefore we always recommend bias correcting when applying a t$t$-statistic in practice in this context.     

What drives the nonlinearity of time series: A frequency perspective

It is well-known that economic and financial time series are characterized by nonlinearities. The literature does not agree, however, on the actual causes of such nonlinearities. In this paper, I investigate whether dynamics at different frequencies present different degree of nonlinearity, and how much they may influence any nonlinearity in the aggregate original series. This paper finds strong evidence in support of the idea that nonlinearities are mostly found at high frequencies.     

Enhancing the local power of IVX-based tests in predictive regressions

IVX estimation is used increasingly often in predictive regressions with regressors of unknown persistence. While not exhibiting the second-order bias the OLS estimator has in this setup, IVX estimators have reduced rates of convergence when the regressors are highly persistent. The reduced convergence rates may sometimes lead to power losses in finite samples when testing for no predictability, for instance. The note discusses a simple way of improving the local power of IVX-based tests, consisting of augmenting the predictive regression with the lagged dependent variable. This implies a feed-back loop which strengthens the signal of the IVX instrument without changing its dynamic properties. The proposed augmentation works best when the power loss of IVX would have been maximal compared to the infeasible OLS-based test.     

Asymptotic behaviour of tests for a unit root against an explosive alternative

We compare the asymptotic local power of upper-tail unit root tests against an explosive alternative based on ordinary least squares (OLS) and quasi-differenced (QD) demeaning/detrending. We find that under an asymptotically negligible initialisation, the QD-based tests are near asymptotically efficient and generally offer superior power to OLS-based approaches; however, the power gains are much more modest than in the lower-tail testing context. We also find that asymptotically non-negligible initial conditions do not affect the power ranking in the same way as they do for lower-tail tests, with the QD-based tests retaining a power advantage in such cases.     

Persistence under temporal aggregation and differencing

Temporal aggregation is known to affect the persistence of time series. We study the aggregation of flow variables as well as stock data, and difference-stationarity is allowed for. Moreover, moving averages encountered when computing annual growth rates (seasonal differences) are investigated. Using a relative persistence measure (long-run variance ratio), it is clarified when persistence is increased or decreased, and by how much. Our results are exact for a finite aggregation level. They are illustrated with monthly time series. Approximate results for the growing aggregation level are provided, too.     

Nonparametric lag selection for nonlinear additive autoregressive models

The lag selection procedure based on the final prediction error (FPE) is investigated when the additive structure is a priori known in the nonparametric autoregression. The consistency of the lag selection is proved, followed by the finite sample simulation results.     

Efficient realized variance, regression coefficient, and correlation coefficient under different sampling frequencies

Efficiency of the realized variance of an asset is improved by taking advantage of another asset whose return is cross-sectionally correlated with that of the asset and is less sensitive to market microstructure noises permitting higher frequency sampling than the original asset.     

Long memory and changing persistence

We study the empirical behaviour of semi-parametric estimation for long-memory models when the true data generating process exhibits a change in persistence. Evidence for long memory is likely to be found. Procedures for discrimination between different models are proposed.