QUANTILE FORECASTS OF FINANCIAL RETURNS USING REALIZED GARCH MODELS*

This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value‐at‐Risk and expected shortfall. Student's t‐ and skewed Student's t‐distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t‐distribution performs better than that with the normal and Student's t‐distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance.     

THE FORECASTING PERFORMANCE OF SETAR MODELS: AN EMPIRICAL APPLICATION

The aim of this paper is to evaluate the forecasting performance of SETAR models with an application to the Industrial Production Index (IPI) of four major European countries over a period which includes the last Great Recession. Both point and interval forecasts are considered at different horizons against those obtained from two linear models. We follow the approach suggested by Teräsvirta et al. (2005) according to which a dynamic specification may improve the forecast performance of the nonlinear models with respect to the linear models. We re‐specify the models every twelve months and we find that the advantages of this procedure are particularly evident in the forecast rounds immediately following the re‐specification.     

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

A COMPARISON OF CONDITIONAL AND UNCONDITIONAL APPROACHES IN VALUE‐AT‐RISK ESTIMATION*

In this paper, we attempt to find the most important factor causing the differences in the performance of Value‐at‐Risk (VaR) estimation by comparing the performances of conditional and unconditional approaches. For each approach, we use various methods and models with different degrees of flexibility in their distributions including S_U‐normal distribution, which is one of the most flexible distribution functions. Our empirical results underscore the importance of the flexibility‐of‐distribution function in VaR estimation models. Even though it seems to be unclear which approach is better between conditional and unconditional approaches, it seems to be clear that the more flexible distribution we use, the better the performance, regardless of which approach we use.     

FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.     

SECULAR MEAN REVERSION AND LONG‐RUN PREDICTABILITY OF THE STOCK MARKET

The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out‐of‐sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long‐term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in‐sample evidence that past 15‐17 year returns are able to predict the future 15‐17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out‐of‐sample performance of long‐horizon return forecasting based on the mean‐reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean‐reverting model is statistically significantly better than the forecast accuracy provided by the naive historical‐mean model. Moreover, we show that the predictive ability of the mean‐reverting model is economically significant and translates into substantial performance gains.     

NONPARAMETRIC MODELS OF FINANCIAL LEVERAGE DECISIONS

This paper applies nonparametric decision tree models to the analysis of financial leverage decisions. This approach presents three appealing features: (i) the relationship between leverage and explanatory variables is not predetermined but is derived from information provided by the data, (ii) the models respect the fractional nature of leverage ratios, and (iii) each covariate is allowed to influence in different ways the financial leverage decisions of firms automatically assigned to different groups. Based on a data set of Portuguese firms, decision trees are used to tackle both classification (the decision to issue debt) and regression (the decision on the amount of debt to be issued, conditional on using debt) problems. It is found that: (i) two‐part models are the most appropriate specification for explaining the overall amount of debt used by firms, (ii) there are no drastic differences between the results produced by tree and parametric models, although some divergences may arise, and (iii) tree models suggest relationships between covariates and leverage that parametric models fail to capture, especially when the sample size is small.     

POST‐PRIVATIZATION OWNERSHIP AND FIRM PERFORMANCE: A LARGE META‐ANALYSIS OF THE TRANSITION LITERATURE

This paper aims to perform a large‐scale meta‐analysis of the relationship between post‐privatization ownership and firm performance in Central and Eastern Europe and the former Soviet Union. Baseline estimation of a meta‐regression model that employs a total of 2,894 estimates drawn from 121 previous studies indicated the superior impact of foreign ownership on firm performance in comparison with state and domestic private entities. Furthermore, the estimation of an extended meta‐regression model that explicitly controls for the idiosyncrasies of transition economies and privatization policies strongly suggested that differences between countries in location, privatization method, and speed of policy implementation strongly influence the link between post‐privatization ownership structure and firm performance. We also found that these factors not only cause a remarkable gap between countries in terms of ex post improvement in firm performance but also significantly affect the interrelationship between foreign investors, domestic outsider owners, and firm managers, and the relative superiority of various domestic outsiders. Conclusive evidence of the harm caused to ex post firm performance by voucher privatization is one of the most noteworthy empirical findings in this paper.     

ALLOWING FOR JUMP MEASUREMENTS IN VOLATILITY: A HIGH‐FREQUENCY FINANCIAL DATA ANALYSIS OF INDIVIDUAL STOCKS

Following recent advances in the non‐parametric realized volatility approach, we separately measure the discontinuous jump part of the quadratic variation process for individual stocks and incorporate it into heterogeneous autoregressive volatility models. We analyse the distributional properties of the jump measures vis‐à‐vis the corresponding realized volatility ones, and compare them to those of aggregate US market index series. We also demonstrate important gains in the forecasting accuracy of high‐frequency volatility models.     

ROLLING REGRESSION VERSUS TIME‐VARYING COEFFICIENT MODELLING: AN EMPIRICAL INVESTIGATION OF THE OKUN'S LAW IN SOME EURO AREA COUNTRIES

During the last decade, economists have shown that the inverse relationship between economic growth and unemployment rate varies over time. Rolling regression has been the main tool used to quantify such a relationship. This methodology suffers from several well‐known problems which lead to spurious non‐linear patterns in the Okun's coefficient behaviour over time. Here, we take a penalized regression spline approach to estimate the Okun's time‐varying effects. As a result, spurious non‐linearities are suppressed and hence important time‐varying coefficient features revealed. Our empirical results show that the inverse relationship in some Euro area countries is spatially heterogeneous and time‐varying. The findings are complemented by the calculation of the rate of output growth needed for a stable unemployment rate, as proposed by Knotek.     

SOME OBSERVATIONS ON THE HIGH‐FREQUENCY VERSIONS OF A STANDARD NEW‐KEYNESIAN MODEL

In a small‐scale New‐Keynesian model with a hybrid Phillips curve and IS equation, the paper is concerned with an arbitrary frequency of the agents’ synchronized decision making. It investigates the validity of a fundamental methodological precept according to which no substantive prediction or explanation of a well‐defined macroeconomic period model should depend on the real time length of the period. While this principle is basically satisfied as the period goes to zero, the impulse – response functions of the high‐frequency versions can qualitatively as well as quantitatively be fairly dissimilar from their quarterly counterpart. The result proves to be robust under variations of the degree of price stickiness. The main conclusion is that DSGE modelling may be more sensitive to its choice of the agents’ decision interval.     

Trade‐led growth hypothesis in Cuban commercial regimes since 19601,2

This study examines the impact of trade on Cuban growth during different commercial policy regimes spanning the period from 1960 up to 2004, encompassing two essential economic structural transformations: the Cuban revolution and the fall of the Berlin Wall. For this purpose, the Granger causality is used by means of the modified Wald test for augmented‐level vector autoregressive model with integrated and cointegrated processes introduced by Toda and Yamamoto (1995) and Dolado and Lutekepohl (1996) . We show an import‐led growth hypothesis during the Soviet‐oriented pattern that is rejected after 1990, when exports are not only responsive to growth expansion but also to imports’ behaviour.     

SELF‐REPORTED ECONOMIC CONDITION AND HOME PRODUCTION: INTRA‐HOUSEHOLD ALLOCATION IN ITALY

This paper applies a collective model of intra‐household welfare distribution using individual self‐reported data. The model accounts for household production, and self‐reported information on economic condition is used to identify the sharing rule governing the bargaining process in the family. The theoretical framework implies a broad concept of full income, which includes household production as time allocated to domestic activities. We find that self‐reported data on economic status are useful in recovering individual shares of household income and that both wages and non‐strictly‐economic individual variables play an important role in the bargaining process determining the sharing rule in Italy.     

DUAL‐INDEX MEASUREMENT OF INCOME INEQUALITY

This study introduces the concept of unequally distributed income and proposes a dual‐index measurement of income inequality that evaluates the magnitude and dispersion of unequally distributed income. We use the Rawlsian index as a magnitude index and employ current income inequality indices as a dispersion index. We describe the properties of the Rawlsian and dispersion indices, and apply these indices to real income data. Further, we show that the Gini dispersion index is a weighted average of Rawlsian indices for the sub‐distributions of the unequally distributed income distribution.