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What is the relationship between financial development, political instability, trade openness and economic growth and how does it change over time? This paper examines these links using a new econometric approach and unique data set. In this paper, we apply the logistic smooth transition model (LST) to annual data for Brazil from 1890 to 2003. The main finding is that financial development has a time-varying effect on economic growth, which depends significantly on (jointly estimated) trade openness thresholds. In addition, political instability displays a negative effect on growth whereas trade openness a positive one. Finally, our estimates show that in 56% of the years in which financial development has a ‘below the mean’ effect, we find that trade openness experiences a substantial ‘above the mean’ change.

Copyright © 2021 The Author(s). The research debate on the informational content embedded in option prices mostly approves the incremental predictive power of implied volatility estimates for financial volatility forecasting beyond that contained in GARCH and realized variance models. Contributing to this ongoing debate, we introduce the novel AIM-HEAVY model, a tetravariate system with asymmetries, option-implied volatility, and economic uncertainty variables beyond daily and intra-daily dispersion measures included in the benchmark HEAVY specification. We associate financial with macroeconomic uncertainties to explore the macro-financial linkages in the high-frequency domain. In this vein, we further focus on economic factors that exacerbate stock market volatility and represent major threats to financial stability. Hence, our findings are directly connected to the current world-wide Coronavirus outbreak. Financial volatilities are already close to their crisis-peaks amid the generalized fear about controversial economic policies to support societies and the financial system, especially in the case of the heavily criticized UK authorities’ delayed and limited response.

Supplementary material: Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejor.2022.04.017. Appendix B. Supplementary materials: Download Acrobat PDF file (https://ars.els-cdn.com/content/image/1-s2.0-S0377221722003150-mmc1.pdf - 1MB) Supplementary Data S1. Supplementary Raw Research Data. This is open data under the CC BY license https://creativecommons.org/licenses/by/4.0/

© 2020 The Authors. This study examines the impact of investors’ buy and sell trades on Korean stock market volatility across two crisis events, the Asian crisis of 1997 and the 2008 global financial crash. We investigate the trading behaviour of domestic vs. foreign and institutional vs. individual investors. Our results suggest that the buy and sell trades have an asymmetric effect on volatility that depends on the type of investor trading and on the phase of the business cycle. Buy orders appear to be more informative than sell orders since they mostly lower volatility in the pre-crisis periods, while sell and post-crisis buy trades affect volatility positively regardless of who trades (institutional or individual investors) and on what information (member, non-member). Most importantly, decomposing total buy and sell trades into trader-type categories reveals that some institutional investors are more informed traders that stabilize the market compared to individuals that always increase volatility. Foreign investors reduce volatility with their purchases and total trading activity in the whole Asian crisis sample, but only in the pre-crisis period before the recent global financial turmoil.

This is an Accepted Manuscript of an article published by Taylor & Francis in The European Journal of Finance on 28 Feb 2020, available online: https://www.tandfonline.com/doi/full/10.1080/1351847X.2020.1732437

This paper studies the bivariate HEAVY system of volatility regression equations and its various extensions that are directly applicable to the day-to-day business treasury operations of trading in foreign exchange and commodities, investing in bond and stock markets, hedging out market risk, and capital budgeting. We enrich the HEAVY framework with powers, asymmetries, and long memory that improve its forecasting accuracy significantly. Other findings are as follows. First, hyperbolic memory fits the realized measure better, whereas fractional integration is more suitable for the powered returns. Second, the structural breaks applied to the bivariate system capture the time-varying behavior of the parameters, in particular during and after the global financial crisis of 2007/2008.

For the large family of ARMA models with variable coefficients we provide an explicit and computationally tractable solution representation, which yields the fundamental properties of such processes, including the Wold-Cramer decomposition and the covariance structure. These results are founded on a banded Hessenbergian representation of the Green's function, built up solely of the auto-regressive coefficients of the model. A generic condition, also expressed in terms of the Green's function,guarantees the convergence of the above mentioned properties. This condition is in line with the asymptotic stability and efficiency of such processes, while, their invertibility is guaranteed by an analogous condition, but now the Green's function is built up of the moving average coefficients. As a consequence, we illustrate mathematically a structural asymmetry between constant and 'time dependent' coefficient models, that is in the latter ones the backward and forward asymptotic efficiency differ structurally from one another. An alternative approach to the Hessenbergian solution representation is described by a simple procedure for manipulating polynomials with variable coefficients. The practical significance of the theoretical results in this work is illustrated with an application to U.S. inflation data. The main finding is that inflation persistence increased after 1976, whereas from 1986 on wards the persistence declines and stabilizes to even lower levels than the pre-1976 period.

We use intraday aggregate stock market data and an event‐study framework to assess the UK's equity market reaction to the unexpected element of the Bank of England Monetary Policy Committee's (MPC) asset purchase announcements for the 2009–2017 period. We assess the reactions of equity returns and their volatility over various time frames, both preceding and following the MPC announcements. Our results show that the UK unconventional monetary policy shocks have a significant impact on domestic equity returns and volatilities. The strength of this impact depends on the Bank's information dissemination through inflation reports and the publication of the MPC's voting records. (JEL G14, E44, E52)

This paper examines how the most prevalent stochastic properties of key metal futures returns have been affected by the recent financial crisis using both mapped and unmapped data. Our results suggest that copper and gold futures returns exhibit time-varying persistence in their corresponding conditional volatilities over the crisis period; in particular, such persistence increases during periods of high volatility compared with low volatility. The estimation of a bivariate GARCH model further shows the existence of time-varying volatility spillovers between these returns during the different stages of such a crisis. Our results, which are broadly the same in relation to the use of mapped or unmapped data, suggest that the volatilities of copper and gold are inherently linked, although these metals have very different applications.

We reveal the macroeconomic determinants of the dynamic correlations between three global asset markets: equities, real estate, and commodities. Conditional equicorrelations, computed by the GJR-GARCH-DECO model, are explained by the macro-financial proxies of economic policy and financial uncertainty, credit conditions, economic activity, business and consumer confidence, and geopolitical risk. Our results suggest that elevated cross-asset correlations are associated with higher uncertainty, tighter credit conditions, and lower geopolitical risk, while lower correlations are related to stronger economic activity, business, and consumer confidence. We further focus on economic policy uncertainty (EPU) as a potent catalyst of the asset markets integration process and conclude that EPU magnifies all macro-effects across all correlations. Lastly, we investigate the global financial crisis effect on the time-varying impact of the correlations’ macro-drivers. The crisis structural break amplifies the influence that all determinants exert on the evolution of correlations apart from the geopolitical risk upshot, which is alleviated after the crisis advent.

We study the convergence properties of inflation rates among the countries of the European Monetary Union over the period 1980–2013. Recently developed panel unit root/stationarity tests cannot reject the stationarity hypothesis. This implies that some countries have been in the process of converging absolutely or relatively. By using a clustering algorithm we statistically detect three absolute convergence clubs in the pre-euro period, which comprise early accession countries. In particular, Luxembourg clusters with Austria and Belgium, while a second sub-group includes Germany and France and the third The Netherlands and Finland. We also detect two separate clusters of early accession countries in the post-1997 period: a sub-group with Germany, Austria, Belgium and Luxembourg, and one with France and Finland. For the rest of the countries/cases we find evidence of divergent behavior. Robustness is checked by testing pairwise convergence in a Bayesian framework. The outcome broadly confirms our findings.

In this paper we investigate the behavior of inflation persistence in the United States. To model inflation we estimate an autoregressive GARCH-in-mean model with variable coefficients and we propose a new measure of second-order time varying persistence, which not only distinguishes between changes in the dynamics of inflation and its volatility, but it also allows for feedback from nominal uncertainty to inflation. Our empirical results suggest that inflation persistence in the United States is best described as unchanged. Another important result relates to the Monte Carlo experiment evidence which reveal that if the model is misspecified, then commonly used unit root tests will misclassify inflation of being a nonstationary, rather than a stationary process.

This paper investigates the information content of trading volume and its relationship with range-based volatility in the Indian stock market for the period 1995–2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is consistent with the argument that the activity of informed traders is inversely related to volatility when the marketplace has increased liquidity, an increasing number of active investors and high consensus among investors when new information is released. We also find that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and (iii) there are significant expiration day effects on both the value of shares traded and volatility series.

This paper addresses neoliberal origins of the acute geoeconomic and social crisis that was inflicted on Greece since 2010 with the unleashing of the 3 consecutive bailout plans and the implementation of fierce austerity policies. We further scrutinize the composition of the soaring Greek debt and most importantly, the unsettling utilization of the troika loans for the 2010–15 period. For the first time in the literature, we provide evidence that the vast bulk of the loans went overwhelmingly not to benefiting a “profligate” Greek state but to avoiding the write-downs of bad loans made by reckless creditors (mainly, German and French banks) to the Greek government and private banks. We propose the temporary adoption of a parallel currency in the form of government IOUs, together with other drastic measures to reboot the ailing Greek economy inside the Eurozone.

This paper employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework we examine the twelve potential intertemporal relationships between inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight that macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extend indirectly via the

In this paper we summarize the opinion of three renowned economists, namely Paul De Grauwe, Paul Krugman and Joseph Stiglitz, on the eurozone crisis as well as the Greek case. In particular all three expressed in one way or another their reservations about the single currency. On one side De Grauwe and Stiglitz highlighted the design failures of the eurozone and on the other Krugman argued that the creation of the common currency was a terrible mistake. In support of their claims we provide evidence of the negative consequences of the austerity measures that were implemented by the troika on the Greek economy for a period covering 2010–2014. After five years of austerity, Greece among others experienced significant deflationary dynamics, deep recession, high unemployment rates, that are among the highest in Europe and an increase of the percentage of the people at risk of poverty or social exclusion. More specifically, GDP per capita growth shrank on average by 5.85% in the period 2010–2013 while the unemployment rate reached 25.5% in 2015. Even more remarkable is the fact that the youth unemployment rate reached 52.4% in 2014. Finally, 14% of the population cannot meet its medical needs due to the high cost of treatment.

Argentina is the only country in the world that in 1900 was ‘developed’ and in 2000 was ‘developing’. Although economic historians have identified and explored various possible explanations (chiefly institutions, political instability, financial development, inflation, trade openness and international financial integration), no study so far has attempted a comprehensive quantitative assessment of their relative importance. This article tries to fill this gap using the power-ARCH framework and annual data since 1896 to study the effects of these factors in terms of both growth and growth volatility. The results highlight two main factors to understand the remarkable growth trajectory of Argentina over the very long run, financial development and institutions (formal and informal political instability) and stress the importance of differences in their short vis-à-vis long-run behaviour.

This paper studies the impact of financial liberalization on economic growth. It contributes to this literature by using an innovative econometric methodology and a unique data set of historical series. It presents power ARCH estimates for Argentina for the period from 1896 to 2000. The main results show that the long-run effect of financial liberalization on economic growth is positive while the short-run effect is negative, albeit substantially smaller. Interestingly, we find that financial development affects growth only directly, that is, not through growth volatility.

This paper examines the link between inflation, output growth and their respective variabilities. We employ a bivariate GARCH model, which incorporates mean and level effects, to investigate in a unified empirical framework all the possible interactions between the four variables. We show that not only does variability affect performance but the latter influences the former as well. Specifically, inflation has a positive impact on both variabilities.

This paper investigates the issue of temporal ordering of the range-based volatility and turnover volume in the Korean market for the period 1995–2005. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish volume trading before the Asia financial crisis from trading after the crisis. We find that the apparent long-memory in the variables is quite resistant to the presence of breaks. However, when we take into account structural breaks the order of integration of the conditional variance series decreases considerably. Moreover, the impact of foreign volume on volatility is negative in the pre-crisis period but turns to positive after the crisis. This result is consistent with the view that foreign purchases tend to lower volatility in emerging markets—especially in the first few years after market liberalization when foreigners are buying into local markets—whereas foreign sales increase volatility. Before the crisis there is no causal effect for domestic volume on volatility whereas in the post-crisis period total and domestic volumes affect volatility positively. The former result is in line with the theoretical underpinnings that predict that trading within domestic investor groups does not affect volatility. The latter result is consistent with the theoretical argument that the positive relation between the two variables is driven by the uninformed general public.

Copyright @ 2010 Cambridge University Press. This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories.

We use a long series of annual data that span over 100 years to examine the relationship between output growth and its uncertainty in five European countries. Using the GARCH methodology to proxy uncertainty, we obtain two important results. First, more uncertainty about output leads to a higher rate of growth in three of the five countries. Second, output growth reduces its uncertainty in all countries except one. Our results are robust to alternative specifications and provide strong support to the recent emphasis by macroeconomists on the joint examination of economic growth and the variability of the business cycle.

We use univariate GARCH models of inflation and output growth and monthly data for the G7 covering the 1957–2000 period to test for the causal effect of real and nominal macroeconomic uncertainty on inflation and output growth, and the effect of inflation on inflation uncertainty. Our evidence supports a number of important conclusions. First, inflation is a positive determinant of uncertainty about inflation. Second, output growth uncertainty is a positive determinant of the output growth rate. Third, there is mixed evidence regarding the effect of inflation uncertainty on inflation and output growth. Hence, uncertainty about the inflation rate is not necessarily detrimental to economic growth. Finally, there is not much evidence supporting the hypothesis that output uncertainty raises inflation.

In this article, we show that in times series models with in-mean and level effects, persistence will be transmitted from the conditional variance to the conditional mean and vice versa. Hence, by studying the conditional mean/variance independently, one will obtain a biased estimate of the true degree of persistence. For the specific example of an AR(1)-APARCH(1,1)-in-mean-level process, we derive the autocorrelation function, the impulse response function and the optimal predictor. Under reasonable assumptions, the AR(1)-APARCH(1,1)-in-mean-level process will be observationally equivalent to an autoregressive moving average (ARMA)(2,1) process with the largest autoregressive root being close to one. We illustrate the empirical relevance of our results with applications to S&P 500 return and US inflation data.

This paper investigates the stock volatility–volume relation in the Korean market for the period 1995–2001. Previous research examined the impact of liberalization on the Korean stock market up to the period before the financial turmoil in 1997 although the crucial measures of the liberalization were introduced after the crisis under the International Monetary Fund program. One of the major features of the reformation was the financial opening to foreign investors. In this study the ‘total’ trading volume is separated into the domestic investors’ and the foreign investors’ volume. By doing this the information used by two different groups of traders can be separated. Further, in addition to the absolute value of the returns and their squares we use the conditional volatility from a GARCH-type model as an alternative measure of stock volatility. The following observations, among other things, are noted about the volume–volatility causal relationship. First, for the entire period there is a strong bidirectional feedback between volume and volatility. In most cases this causal relationship is robust to the measures of volume and volatility used. Second, volatility is related only to ‘domestic’ volume before the crisis whereas after the crisis a bidirectional feedback relation between ‘foreign’ volume and volatility begins to exist. In other words, ‘foreign’ volume tends to have more information about volatility in recent years, which suggests the increased importance of ‘foreign’ volume as an information variable.

We use a long series of annual output data that span about one and a half centuries to examine the relationship between output growth and output growth uncertainty in the G3. Our econometric methodology employs GARCH models and proxies output uncertainty by the conditional variance of shocks to output growth. We find that first, more uncertainty about output growth leads to a higher rate of output growth in two of the three countries and second, output growth reduces its uncertainty in two of the three countries. Our results are robust to the choice of the distribution of the error term and the form in which the time varying variance enters the specification of the mean.

We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth.

This study presents additional evidence on the convergence speeds of real exchange rates. Using median unbiased estimation, impulse response analysis and long horizon data sampled annually and monthly, we estimate the speeds at which deviations from purchasing power parity (PPP) die out. Both monthly and annual data have been used since temporal aggregation has been proposed as a possible cause of the implausibly large half-lives reported in the literature. Moreover, since reporting only point estimates provides an incomplete picture of the speed of convergence towards PPP, median unbiased confidence intervals are also estimated. The results show that the confidence intervals for the half-lives are typically very wide. Interestingly, however, the intervals estimated using monthly data are tighter than those estimated with annual data, though, they do not help solve the PPP puzzle. In fact, it appears that the point estimates of the half-lives obtained with monthly data are much larger. Therefore, on the b...

In this article we derive convenient representations for the cumulative impulse response function of the long memory GARCH(p, d, q) (LMGARCH) process. Our results extend the results in Baillie et al. (1996) [Baillie, R.T., Bollerslev, T., Mikkelsen, H.O. 1996. Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 74, 3–30.] on the first order LMGARCH. Using the derived impulse response functions we compare the persistence of shocks to the conditional variance in various GARCH models of interest such as stable, integrated and LMGARCH.

The purpose of this paper is to provide a comprehensive methodology for the analysis of the Asymmetric Power ARCH model. First, it gives the ARMA representations of a power transformation of the conditional variance and the absolute returns. Second, it derives a certain fractional moment of the absolute observations. Third, it obtains the autocorrelation function of the power-transformed absolute returns. Finally, the practical implications of the results are illustrated empirically using daily data on five East Asia stock indices. D 2005 Elsevier B.V. All rights reserved.

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying modelwhich provides the platformupon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure. Open Access funded by European Research Council under a Creative Commons license.

We use parametric models of long memory in both the conditional mean and the conditional variance of inflation and monthly data in the USA, Japan and the UK for the period 1962–2001 to examine the relationship between inflation and inflation-uncertainty. In all countries, inflation significantly raises inflation-uncertainty as predicted by Friedman. Increased nominal uncertainty affects inflation in Japan and the UK but not in the same manner. The results from Japan support the Cukierman–Meltzer hypothesis. In the UK uncertainty surrounding the future inflation appears to have a mixed impact on inflation.

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).

This paper derives the autocorrelation function of the squared values of long-memory GARCH processes. Such processes are of much interest as they can produce the long-memory conditional heteroskedasticity that many high-frequency financial time series exhibit. An empirical application illustrating the practical use of our results is also discussed.

Using a bivariate GARCH model of inflation and output growth we find evidence that higher inflation and more inflation uncertainty lead to lower output growth in the Japanese economy. These results support the argument of a price stability objective for the monetary authority.

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the mean and volatility dynamics, including the underlying volatility persistence and volatility spillovers structure. Using daily data from several key stock market indices we find that stock market returns exhibit time varying persistence in their corresponding conditional variances. Furthermore, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for low order time varying specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.

The standard approach for studying the periodic ARMA model with coefficients that vary over the seasons is to express it in a vector form. In this paper we introduce an alternative method which views the periodic formulation as a time varying univariate process and obviates the need for vector analysis. The specification, interpretation, and solution of a periodic ARMA process enable us to formulate a forecasting method which avoids recursion and allows us to obtain analytic expressions of the optimal predictors. Our results on periodic models are general, analogous to those for stationary specifications, and place the former on the same computational basis as the latter., 26 pages, no figures. arXiv admin note: text overlap with arXiv:1403.3359

The paper examines the problem of representing the dynamics of low order autoregressive (AR) models with time varying (TV) coefficients. The existing literature computes the forecasts of the series from a recursion relation. Instead, we provide the linearly independent solutions to TV-AR models. Our solution formulas enable us to derive the fundamental properties of these processes, and obtain explicit expressions for the optimal predictors. We illustrate our methodology and results with a few classic examples amenable to time varying treatment, e.g, periodic, cyclical, and AR models subject to multiple structural breaks., 31 pages

This paper considers forecasting the conditional mean and variance from an ARMA model with GARCH in mean effects. Expressions for the optimal predictors and their conditional and unconditional MSEs are presented. We also derive the formula for the covariance structure of the process and its conditional variance. JEL. C22.

Since Bollerslev and Taylor indepedently introduced the GARCH model almost a decade ago many questions have remained unanswered. This paper addresses two of them. First, ‘What is the autocovariance structure of the squared errors?’ and second, ‘What is the condition on the parameters of the GARCH (p, q) model in order for the

In this article we present a new method for computing the theoretical autocovariance function of an autoregressive moving average model. The importance of our theorem is that it yields two interesting results: First, a closed-form solution is derived in terms of the roots of the autoregressive polynomial and the parameters of the moving average part. Second, a sufficient condition for the lack of model redundancy is obtained.

This paper employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework we examine the twelve potential intertemporal relationships between inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight that macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extend indirectly via the uncertainty channel.

Karanasos (1998) presented a new method for computing the theoretical autocovariance function (acf) of the following univariate autoregressive moving average (ARMA) model:Φ(L)yt = Θ(L)εtwhereΦ(L) = 1 − φ1L − … − φpLp, Θ(L) = 1 − θ1L − … − θqLq