Economic benefits of using realized covariance forecasts in risk-based portfolios

This article examines the economic benefit of using the realized covariance matrix forecasts, for constructing the risk-based portfolios. We use the two-scale realized covariance estimator (TSC), the jump robust two-scale realized covariance estimator (RTSC) and the realized bipower covariance estimator (BPC), to forecast the daily realized covariance matrix. Using these covariance matrix forecasts, we implement three risk-based portfolios: the global minimum variance portfolio, the equal risk contribution portfolio and the most diversified portfolio. There is evidence that the portfolio performance improves by using TSC or RTSC estimators as compared to the daily-returns-based estimator. The performance gains are robust to the choice of risk-based portfolio strategy, the degree of investor’s relative risk-aversion, the market conditions and the choice of time intervals.     

Estimation of Structural Nonlinear Errors‐in‐Varibles Models by Simulated Least‐Squares Method

This article proposes a simulation approach to obtain least‐squares or generalized least‐squares estimators of structural nonlinear errors‐in‐variables models. The proposed estimators are computationally attractive because they do not need numerical integration nor huge numbers of simulations per observable. In addition, the asymptotic covariance matrix of the estimator has a simple decomposition that may be used to guide selection of appropriate simulation sizes. The method is also useful for models with missing data or imperfect surrogate covariates, where application of conventional least‐squares and maximum‐likelihood methods is restricted by numerical multidimensional integrations.     

Dominance testing for ‘pro-poor’ growth with an application to European growth

This article introduces statistical testing procedures to evaluate ??pro-poor?? growth. Our measure of ??pro-poorness?? follows Kakwani (J Quant Econ 16(1):67?C80, 2000), Kakwani and Pernia (Asian Dev Rev 18(1):1?C16, 2000), and Son (Econ Lett 82:307?C314, 2004), who decompose the generalized Lorenz ordinates into a growth effect and an inequality effect. We derive an asymptotic distribution-free covariance matrix for the decomposed generalized Lorenz curves. Using this decomposition (and our standard errors), we test for pro-poor dominance in the growth process. We illustrate our test for the pro-poor dominance by evaluating the degree of pro-poor growth in five European countries.     

A latent dynamic factor approach to forecasting multivariate stock market volatility

This paper proposes a latent dynamic factor model for high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and combines common latent factors driven by HAR processes and idiosyncratic autoregressive dynamics. The model accounts for positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates are obtained using basic Markov chain Monte Carlo methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices shows remarkably good forecasting results, in-sample and out-of-sample.     

Simultaneously determined,time-varying hedge ratios in the soybean complex

Time-varying hedge ratios are derived which account for the dynamic characteristics of prices in the soybean complex. A multivariate generalized autogressive heteroskedastic (MGARCH) model, along with other conditional models, is used to specify the relevant covariance matrix. While the time-varying representations of the variance matrix are statistically appropriateex anteand ex posthedging effectiveness indicate that they provide minimal gain to hedging in terms of mean return and reduction in variance over a constant conditional procedure. Whether similar findings arise from other applications of GARCH models to optimal hedging is a question for further research.     

Finite-sample size distortion of the AESTAR unit root test: GARCH,corrected variance–covariance matrix estimators and adjusted critical values

The behaviour of the asymmetric exponential smooth transition autoregressive (AESTAR) unit root test, which allows for asymmetric and nonlinear reversion to equilibrium, is examined in the presence of generalized autoregressive conditional heteroscedasticity (GARCH). It is found that while the test is relatively robust in the presence of ‘low volatility’ GARCH processes, it exhibits substantial size distortion when large values of the volatility parameter are considered. Attempted resolution via the routine application of heteroscedasticity consistent (or ‘corrected’) covariance matrix estimators (HCCMEs) is shown to result in overwhelming size distortion due to their impact upon the finite-sample distribution of the underlying test statistic. However, application of a corrected HCCME, in combination with critical values derived specifically under its use, results in the control of test size. Analogous results for the Dickey–Fuller (1979) test are presented to permit comparison with a test considering linear, symmetric adjustment. It is found that the AESTAR test is subject to far greater distortion than its linear, symmetric alternative. In summary, the results indicate that caution must be exercised when applying the AESTAR test to macroeconomic and financial time series, particularly if routine application of corrected covariance matrix estimators occurs.     

A general method for comparative dynamics in continuous-time models

This paper develops a general and applicable method for the computation of comparative dynamics in continuous-time perfect foresight models. The key technique in our method is the Jordan decomposition of the Jacobian matrix. This enables us to derive analytical solutions when dealing with high-dimensional systems with repeated eigenvalues. In an application, we compute comparative dynamics of an unanticipated expansionary monetary policy in scenarios with and without repeated eigenvalues. We find that the short-run effects on the social welfare are opposite in the two scenarios, while the long-run effects are similar.     

A matrix approach to the Gini index decomposition by subgroup and by income source

The literature offers two main ways of decomposing the Gini index: decomposition by population subgroup and by income source. This article proposes merging the two decomposition dimensions by suggesting a matrix formula for the Gini index which permits the simultaneous decomposition by subgroup and by income source. Using this multi‐decomposition, one can investigate the role of the interaction between the subgroup and the source components in determining the overall inequality. We apply the methodology to sample data on Italian household incomes collected in 2008.     

Principal Component Models for Generating Large GARCH Covariance Matrices

The implementation of multivariate GARCH models in more than a few dimensions is extremely difficult: because the model has many parameters, the likelihood function becomes very flat, and consequently the optimization of the likelihood becomes practicably impossible. There is simply no way that full multivariate GARCH models can be used to estimate directly the very large covariance matrices that are required to net all the risks in a large trading book. This paper begins by describing the principal component GARCH or 'orthogonal GARCH' (O-GARCH) model for generating large GARCH covariance matrices that was first introduced in Alexander and Chibumba (1996) and subsequently developed in Alexander (2000, 2001b). The O-GARCH model is an accurate and efficient method for generating large covariance matrices that only requires the estimation of univariate GARCH models. Hence, it has many practical advantages, for example in value–at–risk models. It works best in highly correlated systems, such as term structures. The purpose of this paper is to show that, if sufficient care is taken with the initial calibration of the model, equities and foreign exchange rates can also be included in one large covariance matrix. Simple conditions for the final covariance matrix to be positive semi-definite are derived.
(J.E.L.: C32, C53, G19, G21, G28).     

The properties of some covariance matrix estimators in linear models with AR(1) errors

A Monte Carlo study is used to demonstrate how poor some traditional covariance matrix estimators can be in linear models with AR(1) errors. It is also shown that a second-order approximation suggested recently by Ullah et al. (1983) does not lead to a more accurate covariance matrix estimator.     

The limitations of negative incomes in the Gini coefficient decomposition by source

Lerman and Yitzhaki (1985) developed a decomposition of the Gini coefficient by income source that has been extensively used in the literature. This method has strong limitations in the presence of negative incomes, which were not discussed by the original authors and have been widely overlooked in successive studies. Through theoretical argumentation and practical examples, this article shows that, when using negative incomes, (1) the original decomposition formulae become inappropriate, (2) the marginal effects analysis may yield erroneous results and (3) the Pigou–Dalton ‘principle of transfers’ is not always met. This has critical implications for policy development, given that strategies based upon incorrect analyses could actually result in undesired greater income inequalities. The Gini source decomposition should be carefully applied by researchers and policymakers, especially in rural developing areas, where negative incomes are common due to financial losses from agricultural activities.     

The maximum entropy moment matrix with missing values

The occurrence of missing values for one or several variables has the effect of adding a ridge along the diagonal of their maximum entropy (ME) covariance matrix. This is a second ridge in addition to the usual ridge of the ME covariance matrix.     

互联网使用与工资收入差距*——基于CHNS数据的经验分析

基于2004—2011年的中国营养与健康调查数据（CHNS）,本文使用最小二乘法和控制自选择偏差的倾向性得分匹配法详细估计了互联网使用对工资收入的影响。结果表明,互联网使用对个体工资收入有积极的影响,尤其对于拥有高中学历的个体而言,这种影响更大。本文进一步使用基于RIF回归的FFL分解方法考察了互联网使用对工资不平等的影响。本文发现,2004—2011年间互联网使用比例的增加降低了整体工资不平等,且使用过互联网的高中学历人员比例增加有助于降低高工资与中等工资收入人群的工资差距。同时,互联网使用收益率的下降也有助于降低工资不平等,尤其有助于降低高工资与中等工资收入人群之间的工资差距。因此,普及互联网使用,尤其是提高低收入群体的互联网使用比例和普及该群体的高中阶段教育对于抑制不平等的扩大有重要的意义。     

Central Bank Independence, Inflation Variability, and the Revenue Smoothing Hypothesis

This paper examines the revenue-smoothing hypothesis, which posits that an optimizing government will adjust both taxes and inflation to meet shocks to government spending. Our contribution is to examine this through the lens of a new methodology that relates both the first and second moments of inflation rates to central bank independence (CBI) measures. Unlike existing least-squares-based CBI papers, this study uses a maximum likelihood framework that facilitates the direct inclusion of CBI parameters in the residual covariance matrix. This new approach allows for a more intensive use of information contained in the CBI indexes and the estimates obtained are better reflective of CBI influences. Our results provide stronger evidence confirming the revenue-smoothing hypothesis, in particular for those countries with more independent central banks.     

Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Finnish stock market over the period 1996 to 2012. It contributes to the existing technical analysis literature by comparing for the first time the performance of DMAC trading portfolios of individual stocks to the performance of index trading strategies based on trading on an index that consists of the same stocks. The results show that their relative performance varies over time, whereas previous studies have documented outperformance of index trading strategies over trading strategies of stock portfolios. Moreover, the great majority of 3020 DMAC strategies examined in this article outperform the corresponding buy-and-hold (B and H) strategy for both trading targets (i.e., OMX Helsinki 25 index and individual stocks included in the index) in out-of-sample tests. In addition, the decomposition of the full-sample-period performance into separate bull- and bear-period performance shows clearly that the outperformance of DMAC strategies over B and H strategy is mostly attributable to their better performance during bearish periods.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

离散分布收入数据基尼系数的矩阵向量形式及相关问题

本文旨在给出离散分布收入数据基尼系数的很一般的矩阵向量形式,并以此为基础,在一般的意义上讨论基尼系数的按组分解及按类分解问题。本文的结论是,借助矩阵向量形式,我们可以确切地说明基尼系数"完全分解"的含义,更直观地表述目前已有的各种并组或并类基尼系数按组或按类分解的意义,简洁地揭示它们内在的数理结构;通过矩阵向量的运算法则,我们可以直接发现,不可能一般性地实现并组或并类基尼系数按组或按类"完全分解",且使分解后各项均有明确的经济意义;除非进一步给出特殊假定,各种分解的运算量与直接计算并组或并类基尼系数的运算量是相当的。     

Sources of employment fluctuations in Taiwan's industries and regions

This empirical study explores the sources of employment fluctuations in Taiwan's industries and regions over the period 1978 to 2004. The quarterly growth rates of employment in nine industries and four regions are modelled with a structural vector autoregression (VAR), and the employment shocks are measured by VAR residuals. The covariance matrix of the VAR residuals is decomposed using system estimation method that selects the parameters to make the error model close to the covariance matrix and, in turn, to estimate the relative importance of national as well as industry-specific and region-specific shocks. The empirical results show that industry-specific shocks account for the major fluctuations in industries and regions. On average, about 83.95% of an industry's cyclical variations and 56.28% of the volatility in a region may be attributed to industry-specific shocks. National shocks account for little employment volatility in industries. Only the finance and personal service industries are highly sensitive to national shocks.     

Can the Consumption Capital Asset Pricing Model Account for Traders' Expected Currency Returns?

This study uses survey data on traders' exchange rate forecasts to test whether their expected excess returns are related to the covariance between the exchange rate and consumption; as predicted by the consumption capital asset pricing model (CCAPM). The covariance is measured through the novel use of rolling windows of the realized covariance (both forward and backward looking) and testing is conducted with the cointegrated VAR. The model is able to account for expected returns with more plausible degrees of risk aversion, but only when using sufficiently long, backward‐looking measures of the covariance. This suggests that market participants assess risk, in part, based upon the pro‐cyclicality of returns, and infer it from experience in the recent past. There is also evidence that inclusion of the real exchange rate improves the plausibility of the estimates and the model fit.     

On factor decomposition of cross-country income inequality: some extensions and qualifications

In a recent paper in this journal Duro and Esteban [Econom. Lett. 60 (1998) 269] have proposed a factor decomposition of the Theil [Economics and Information Theory, Amsterdam, North-Holland, 1967] index of inequality over per capita incomes into the (unweighted) sum of the inequality indexes of the factors in order to measure the contribution of each individual factor to the overall inequality. The purpose of this little note is to extend and qualify the meaning of such a decomposition, to show that the decomposition also holds for another Theil [Economics and Information Theory, Amsterdam, North-Holland, 1967], index of inequality and that both decompositions offer qualitatively the same general results about the contribution of the factors to aggregate inequality when applied to a particular data set, the OECD countries, although quantitative results differ.