Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

PORTFOLIO SELECTION BETWEEN A MATURE MARKET AND SELECTED EMERGING MARKETS INDICES IN THE PRESENCE OF STRUCTURAL BREAKS

The paper examines the influence of the structural breaks on the optimal weights, hedge ratios and hedge effectiveness index (HEI) of risk‐minimizing portfolios composed of S&P500 and selected emerging markets’ indices from East Europe, Asia and South America. We employ a bivariate DCC‐EGARCH models without and with structural breaks and we find better estimation features when structural breaks are included in the model. However, we do not find evidence that insertion of structural breaks increases portfolio hedging performances. The differences that exist between optimal weights, hedge ratios and HEI values are so small that tangible economic benefit for international investors do not exist.     

Global and local determinacy in a one-step forward looking New Keynesian model

In this paper we consider a New Keynesian model for optimal monetary policy in a staggered fashion. We provide the relations of a non linear model of general economic equilibrium, implementing a suitable Taylor-type interest rate rule. We characterize the conditions that guarantee local determinacy and explore conditions under which local bifurcations of the target equilibrium may occur. Afterwards, we argue how local determinacy might be associated with global indeterminacy, providing some numerical examples.     

Output Variability and Economic Growth: the Japanese Case

We examine the empirical relationship between output variability and output growth using quarterly data for the 1961–2000 period for the Japanese economy. Using three different specifications of GARCH models, namely, Bollerslev's model, Taylor/Schwert's model, and Nelson's EGARCH model, we obtain two important results. First, we find robust evidence that the “in‐mean” coefficient is not statistically significant. This evidence is consistent with Speight's (1999) analysis of UK data and implies that output variability does not affect output growth. In other words, this finding supports several real business cycle theories of economic fluctuations. Second, we find no evidence of asymmetry between output variability and growth, a result consistent with Hamori (2000) .     

Asymmetry, realised volatility and stock return risk estimates

In this paper we estimate minimum capital risk requirements for short and long positions with three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes. We also address the problem of the extremely high estimated persistence of the GARCH model to generate observed volatility patterns by including realised volatility as an explanatory variable into the model??s variance equation. The results suggest that the inclusion of realised volatility improves the GARCH forecastability as well as its ability to calculate accurate minimum capital risk requirements and makes it quite competitive when compared with asymmetric conditional heteroscedastic models such as the GJR and the EGARCH.     

Pricing and hedging options with GARCH-stable proxy volatilities

This article considers modelling nonnormality in return with stable Paretian (SP) innovations in generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH) volatility dynamics. The forecasted volatilities from these dynamics have been used as a proxy to the volatility parameter of the Black–Scholes (BS) model. The performance of these proxy-BS models has been compared with the performance of the BS model of constant volatility. Using a cross section of S&P500 options data, we find that EGARCH volatility forecast with SP innovations is an excellent proxy to BS constant volatility in terms of pricing. We find improved performance of hedging for an illustrative option portfolio. We also find better performance of spectral risk measure (SRM) than value-at-risk (VaR) and expected shortfall (ES) in estimating option portfolio risk in case of the proxy-BS models under SP innovations.

Abbreviation: generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH)     

中国贸易开放度、贸易结构与通货膨胀不确定性关系

完全预期到的通货膨胀在理论上并不会对真实经济活动产生实质性的影响,通货膨胀的真正危害在于它的不确定性。随着贸易开放的不断扩大和深化,影响中国通货膨胀的不确定性的因素变得更加复杂。本文利用EGARCH模型估计了中国的通货膨胀不确定性,并在此基础上利用协整分析方法检验了贸易开放度、贸易结构与通货膨胀不确定性的关系。研究表明:贸易开放度的提高对通货膨胀不确定性具有负向的影响;贸易结构的变化对通货膨胀不确定性具有正向的影响。     

股指期货对股票现货市场波动性影响的实证研究

本文选取1984年1月13日至2008年9月10日香港恒生股指期货的日收盘价作为原始数据,建立了GARCH模型与EGARCH模型就股指期货对股票现货市场波动性的影响进行实证研究,得出香港恒生股指期货的引入在一定程度上降低了香港股票现货市场的波动性等结论。     

Artificial neural network regression models in a panel setting: Predicting economic growth

Economic time series often feature non-linear structures such as non-linear time trends, non-linear autoregressive effects, and non-linear interaction effects. In this paper, it is shown that artificial neural network regression models are suitable tools for the analysis of economic panel data because they allow for a compromise between the ability to model these features and the model size. As model specification is a concern in artificial neural network models, previous approaches are discussed critically. It is shown that the growth rates of the gross domestic product of 24 industrialized economies in the period 1992–2016 follow a non-linear time trend which cannot be explained by autoregressive features or polynomial time variables. The unrestricted functional form of the time trend in the artificial neural network model is also the main reason for the superior statistical performance compared to conventional panel models. This is confirmed by out-of-sample predictions for 2017.     

Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach

Researchers have suggested that the relationship between the emission of carbon dioxide per capita and the real gross domestic product per capita follows an inverted-U-shaped (so-called environmental Kuznets) curve. Studies have generally used polynomial regression (quadratic or cubic form) to investigate this relationship. It has been recognised that polynomials are not that flexible and that, by choosing the degree of the polynomial, researchers make a priori assumptions. In this paper, we investigate the environmental Kuznets curve hypothesis using a flexible approach from additive mixed models. Such models are well-suited to handle nonlinear covariate effects flexibly and to simultaneously deal with temporal error structure. We consider the following countries: Australia, Austria, Canada, Denmark, Finland, France, Italy, Spain and Switzerland. Our results show the existence of the classic environmental Kuznets curve for France and Switzerland, and of a nonlinear (increasing) relationship for Australia, Italy and Spain. For Austria, the evidence reveals a weak N-shaped relationship. New nonlinear shapes are found for Finland (inverted-L-shape relationship), Canada (a special case of the inverted-L-shape relationship), and Denmark (M-shape relationship). Our findings are complemented by the calculation of the elasticity of the carbon dioxide emission per capita as a percentage of each level of real gross domestic product per capita.     

Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.     

基于多项式回归模型的货币供应与通货膨胀的实证分析

通货膨胀是当前中国国民经济发展中遇到的热点问题,从探讨与通货膨胀率关系最直接的变量货币供应量出发,首先定性的讨论它们之间的有关理论和关系,然后通过实际数据,采用R软件,用非参数LPE回归模型对货币供应与通货膨胀的因果关系进行实证分析,并将此结果与线性回归和多项式回归模型比较,得出多项式回归模型优良的结论,最后,通过实证结果分析,得出相应的结论。     

On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

Monetary shocks and asymmetric effects in an emerging stock market: The case of China

In this paper, we study the effect of monetary shocks on the Chinese stock market over the period of 2005 to 2011 with the MSVAR–EGARCH model. The evidence suggests that Chinese monetary policies have significantly asymmetric effects on the stock market in different time periods and market cycles. The effects of shocks from interest rate and reserve rate vary across market cycles but effects from money supply and exchange rate do not. Empirical evidence from the non-linear model shows that monetary policy changes increase stock market volatility, even though these monetary policies are often aimed at stabilizing macro-economic activities. The evidence suggests that both the market conditions and the effects on stock markets should be taken into consideration in monetary policy design and implementation.     

中国银行间同业拆借市场信息国际化的检验

本文运用EGARCH模型，检验伦敦银行间同业拆借市场和中国银行间同业拆借市场之间拆借利率波动溢出的流星雨假定。结果表明，来自伦敦银行间同业拆借市场的流星雨对中国银行间同业拆借市场利率波动具有显著性影响。     

Spatial contagion in the subprime crisis context: Adjusted correlation versus local correlation approaches

This paper investigates the influence of the spatial dimension on financial contagion in the subprime crisis based on adjusted and local correlation measures. Daily series of stock indexes of American and Asian countries are used from January 1, 2003, to December 30, 2011. We consider two groups of countries: the first group includes the United States and countries that are geographically close: Brazil, Argentina, Mexico, and Canada. The second group includes countries that are geographically distant from the United States: Hong Kong, India, Australia, Indonesia, Malaysia, South Korea, China, and Singapore. The results show that simple and adjusted correlations are not enough to explain the spatial effect of contagion. Using local correlations and polynomial regressions, the results show the existence of spatial contagion between the United States and all countries in the American region. As for countries that are geographically distant from the United States, we prove the existence of spatial contagion between only some groups of countries (United States/India, United States/Australia, United States/Indonesia, United States/Malaysia, United States/China). These results have international diversification, and within-industry implications.     

Financial market integration: Theory and empirical results

In this article, we introduce a new theoretical international asset pricing model which accounts for partial financial market segmentation. We show that if some investors do not hold all international assets because of implicit and/or explicit segmentation factors, the world market portfolio is not efficient and the classic ICAPM must be augmented by a new factor reflecting the local risk undiversifiable internationally. We test this model empirically for a sample of emerging markets. Our findings show that the degree of market integration is time-varying and that the premium associated with the domestic risk factors is the most important component of the total risk premium. However, our results also show that most of the emerging markets we study have become more integrated in the end of our sample period as a result of liberalization and reforms.     

Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia

This paper analyses a second-order polynomial spatial structure in the residues of a regression model. We propose a new specification that captures spatial dependence on two different levels, adding a new autoregressive cycle to the errors of the classical spatial error model (SEM). The inference problems of the parameters are solved by means of maximum likelihood estimation. The model is confirmed to identify two spatial structures of spatial dependence, global and local, by an empirical application in the analysis of municipal unemployment in the Spanish region of Andalusia. Finally, Monte Carlo is implemented to evaluate the performance of this strategy in a context of finite size samples.     

Competitive equilibria in semi-algebraic economies

This paper develops a method to compute the equilibrium correspondence for exchange economies with semi-algebraic preferences. Given a class of semi-algebraic exchange economies parameterized by individual endowments and possibly other exogenous variables such as preference parameters or asset payoffs, there exists a semi-algebraic correspondence that maps parameters to positive numbers such that for generic parameters each competitive equilibrium can be associated with an element of the correspondence and each endogenous variable (i.e. prices and consumptions) is a rational function of that value of the correspondence and the parameters.This correspondence can be characterized as zeros of a univariate polynomial equation that satisfy additional polynomial inequalities. This polynomial as well as the rational functions that determine equilibrium can be computed using versions of Buchberger's algorithm which is part of most computer algebra systems. The computation is exact whenever the input data (i.e. preference parameters etc.) are rational. Therefore, the result provides theoretical foundations for a systematic analysis of multiplicity in applied general equilibrium.