The predictability of stock market volatility in emerging economies: Relative roles of local,regional, and global business cycles

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.     

Monetary policy,composite leading economic indicators and predicting the 2001 recession

On 26 November 2001, the National Bureau of Economic Research announced that the US economy had officially entered into a recession in March 2001. This decision was a surprise and did not end all the conflicting opinions expressed by economists. This matter was finally settled in July 2002 after a revision to the 2001 real gross domestic product showed negative growth rates for its first three quarters. A series of political and economic events in the years 2000–01 have increased the amount of uncertainty in the state of the economy, which in turn has resulted in the production of less reliable economic indicators and forecasts. This paper evaluates the performance of two very reliable methodologies for predicting a downturn in the US economy using composite leading economic indicators (CLI) for the years 2000–01. It explores the impact of the monetary policy on CLI and on the overall economy and shows how the gradualness and uncertainty of this impact on the overall economy have affected the forecasts of these methodologies. It suggests that the overexposure of the CLI to the monetary policy tools and a strong, but less effective, expansionary money policy have been the major factors in deteriorating the predictions of these methodologies. To improve these forecasts, it has explored the inclusion of the CLI diffusion index as a prior in the Bayesian methodology. Copyright © 2004 John Wiley & Sons, Ltd.     

Risk factor beta conditional value‐at‐risk

We propose a new approach to the estimation of the portfolio Value‐at‐Risk. Based on the assumption that the same macroeconomic factors affect returns of all assets in a portfolio, this methodology allows the generation of the sequence of hypothetical future equilibrium portfolio returns given the historical values of the underlying macroeconomic factors and the asset betas with respect to these factors. Value‐at‐Risk is then found as an appropriate percentile of the corresponding hypothetical distribution of the portfolio profits and losses. The backtesting results for the six Fama–French benchmark portfolios and the S&P500 index show that this approach yields reasonably accurate estimates of the portfolio Value‐at‐Risk. Copyright © 2008 John Wiley & Sons, Ltd.     

Portfolio managers' and novices' forecasts of risk and return: are there predictable forecast errors?

This study aims to investigate the individual behaviour that underlies the overreaction hypothesis by conducting a controlled experiment. Two areas that were not captured by previous research on the validity of the overreaction hypothesis are investigated. First, actual portfolio managers are employed as forecasters. Second a real‐world assessment task is given in the form of predicting the prices of stocks traded on the exchange on a real time basis. The purpose is to explore return expectations and risk perceptions of portfolio managers as well as financially unsophisticated investors by using point and interval forecasts provided for different forecast horizons in bull and bear markets. Contributions stem from three sources. (1) The use of financially sophisticated subjects for the first time in an experimental framework testing the overreaction hypothesis makes possible to control for the effect of expertise. (2) The use of different forecast horizons controls for the effect of forecast period. (3) The use of real‐time forecasts of specific stocks traded at the stock exchange, for the first time in an experimental framework testing the overreaction hypothesis enables to control for ecological validity. Discussions will be given as to the portfolio managers' versus naive investors' interpolating asset prices from past trends and hedging behaviour, due to their caution in projections of ranges for future prices. Copyright © 2002 John Wiley & Sons, Ltd.     

Monetary Aggregates to Improve Early Output Gap Estimates in the Euro Area: An Empirical Assessment

Output gap estimates at the current edge are subject to severe revisions. This study analyzes whether monetary aggregates can be used to improve the reliability of early output gap estimates as proposed by several theoretical models. A real‐time experiment shows that real M1 can improve output gap estimates for euro area data. For many periods the cyclical component of real M1 shows good results, while a forecasting strategy based on projecting GDP series seems to be more robust and provides superior results during the Great Recession. Broader monetary aggregates provide no superior information for output gap estimates. Copyright © 2015 John Wiley & Sons, Ltd.     

A decision rule to minimize daily capital charges in forecasting value‐at‐risk

Under the Basel II Accord, banks and other authorized deposit‐taking institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of value‐at‐risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realized losses are above the estimated risk. In this paper we analyze the profit‐maximizing problem of an ADI subject to capital requirements under the Basel II Accord as ADIs have to choose an optimal VaR reporting strategy that minimizes daily capital charges. Accordingly, we suggest a dynamic communication and forecasting strategy that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor's 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.     

RNA-dependent RNA polymerase encoded by hepatitis C virus: biomedical applications

The hepatitis C viruses (HCVs) are a group of small enveloped RNA viruses that have been viewed as a leading cause of chronic hepatitis in humans. Infections by HCV represent a serious global health problem, because millions of people worldwide are infected and no efficient treatment is available at the present time. Since HCV was identified in 1989, considerable effort has been devoted to the discovery and development of novel molecules to treat HCV-related diseases. One of the approaches is the development of novel inhibitors that interrupt the normal functions of HCV NS5B, an RNA-dependent RNA polymerase essential to HCV replication. This review summarizes recent advances in the biochemical and structural understanding of HCV NS5B polymerase as well as in the development of antiviral agents targeting this important enzyme. Received 19 March 2002; received after revision 23 April 2002; accepted 23 April 2002     

Domestic and international macro-economic effects of german economic and monetary union simulations on QUEST

This paper addresses the impact of German economic and monetary union on the world economy. The basic question is if positive growth effects of increased German imports dominate the negative forces caused by rising interest rates and varying exchange rates. From simulations on the multi-country QUEST model which incorporates the major bilateral trade selections one can draw the conclusion that the former impact is stronger than the latter. Hence, in general, the world economy benefits from German unification.     

Nowcasting quarterly GDP growth in a monthly coincident indicator model

This paper presents an extension of the Stock and Watson coincident indicator model that allows one to include variables available at different frequencies while taking care of missing observations at any time period. The proposed procedure provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. An application to a coincident indicator model for the Portuguese economy is presented. We use monthly indicators from business surveys whose results are published with a very short delay. By using the available data for the monthly indicators and for quarterly real GDP, it becomes possible to produce simultaneously a monthly composite index of coincident indicators and an estimate of the latest quarter real GDP growth well ahead of the release of the first official figures. Copyright © 2005 John Wiley & Son, Ltd.     

Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub‐optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out‐of‐sample forecasting performance of various linear and GARCH‐type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decision making. Copyright © 2002 John Wiley & Sons, Ltd.     

Vector smooth transition regression models for US GDP and the composite index of leading indicators

In this paper, I extend to a multiple‐equation context the linearity, model selection and model adequacy tests recently proposed for univariate smooth transition regression models. Using this result, I examine the nonlinear forecasting power of the Conference Board composite index of leading indicators to predict both output growth and the business‐cycle phases of the US economy in real time. Copyright © 2004 John Wiley & Sons, Ltd.     

Analyzing Macroeconomic Forecastability

This paper estimates, using stochastic simulation and a multi‐country macroeconometric model, the fraction of the forecast error variance of output changes and the fraction of the forecast error variance of inflation that are due to unpredictable asset price changes. The results suggest that between about 25% and 37% of the forecast error variance of output growth over eight quarters is due to asset price changes and between about 33% and 60% of the forecast error variance of inflation over eight quarters is due to asset price changes. These estimates provide limits to the accuracy that can be expected from macroeconomic forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Insights or forecasts? An evaluation of a computable general equilibrium model of spain

Computable general equilibrium (CGE) models are widely used as an advanced tool to evaluate alternative economic strategies and policy measures. These models are well rooted in solid economic theory, yet a crucial question is hardly asked: how well do these models perform? We address this question by comparing the economic performance of the Spanish economy in 1988 with the simulation results drawn from a CGE model calibrated with a 1987 Social Accounting Matrix. The values of endogenous variables used in the comparison are the equilibrium values provided by the model after updating the values of exogenous variables such as labour and capital endowments, real exports and effective nominal exchange rates with the European Community and the rest of the world, real government expenditures, and various tax rates, government subsidies, and transfers. The comparison shows that the model captures adequately the major developments that occurred in the Spanish economy in 1988. This result increases our confidence in the quantitative estimates derived from the model in the usual simulation exercises.     

A threshold stochastic volatility model

This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.     

Market risk management of banks: implications from the accuracy of Value‐at‐Risk forecasts

This paper adopts the backtesting criteria of the Basle Committee to compare the performance of a number of simple Value‐at‐Risk (VaR) models. These criteria provide a new standard on forecasting accuracy. Currently central banks in major money centres, under the auspices of the Basle Committee of the Bank of International settlement, adopt the VaR system to evaluate the market risk of their supervised banks. Banks are required to report VaRs to bank regulators with their internal models. These models must comply with Basle's backtesting criteria. If a bank fails the VaR backtesting, higher capital requirements will be imposed. VaR is a function of volatility forecasts. Past studies mostly conclude that ARCH and GARCH models provide better volatility forecasts. However, this paper finds that ARCH‐ and GARCH‐based VaR models consistently fail to meet Basle's backtesting criteria. These findings suggest that the use of ARCH‐ and GARCH‐based models to forecast their VaRs is not a reliable way to manage a bank's market risk. Copyright © 2002 John Wiley & Sons, Ltd.     

Testing for Granger (non‐)causality in a time‐varying coefficient VAR model

In this paper we propose Granger (non‐)causality tests based on a VAR model allowing for time‐varying coefficients. The functional form of the time‐varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non‐causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money–output relationship using quarterly US data for the period 1952:2–2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out‐of‐sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd.     

On a family of finite moving‐average trend filters for the ends of series

A family of finite end filters is constructed using a minimum revisions criterion and based on a local dynamic model operating within the span of a given finite central filter. These end filters are equivalent to evaluating the central filter with unavailable future observations replaced by constrained optimal linear predictions. Two prediction methods are considered: best linear unbiased prediction and best linear biased prediction where the bias is time invariant. The properties of these end filters are determined. In particular, they are compared to X‐11 end filters and to the case where the central filter is evaluated with unavailable future observations predicted by global ARIMA models as in X‐11‐ARIMA or X‐12‐ARIMA. Copyright © 2002 John Wiley & Sons, Ltd.     

浅议新加坡科技发展态势

在知识经济背景下，新加坡日益重视科技对于经济的推动作用。本文尝试从5个方面分析了新加坡科技发展的态势：科技投入和产出持续高增长，研发人力不断加强，研发基础设施日益完善，企业的研发和创新备受重视，相对竞争优势逐渐形成。     

Geopolitical risk and global financial cycle: Some forecasting experiments

In this study, we investigate the connection between geopolitical risk (GPR) and global financial cycle (GFCy) as well as whether the former has predictive value for the out-of-sample predictability of the latter. We utilize both the historical and recent GPR data and their variants, namely, GPR act covering all “acts” that constitute GPR such as war, nuclear invasion and terrorism, and GPR threat, which represents threats of these acts. We construct a predictive model that accommodates the salient features of the predicted and predictor series while the forecast evaluation is conducted for both in-sample and out-of-sample periods. Our findings reveal that a rise in GPR discourages investments in risky assets and by implication worsens GFCy. The impact is more severe after the global financial crisis (gfc), and the GPR threat exerts more adverse effect on GFCy compared with GPR act regardless of whether historical GPR or recent GPR is used. Meanwhile, the predictive model of GFCy that accommodates the GPR data outperforms the benchmark model that ignores it both in the in-sample and out-of-sample estimates albeit with improved forecast performance during the post-gfc period and at a longer forecast horizon. However, the recent GPR data, which are broader in scope, offer better forecast accuracy than the historical GPR data. Additional analyses involving the vulnerability of global economic conditions reveal similar outcomes as GFCy.