economic policy uncertainty

DOI: 10.4236/me.2019.103054 816 Modern Economy Compared to merely using significant events and leader turnover as dummy variables, some scholars measure political uncertainty more comprehensively by preparing uncertainty indicators. Baker S R, Bloom N and Davis S J (2016) [17] constructed the Economic Policy Uncertainty Index (EPU) based on three as- pects: the coverage of economic policy uncertainty in the top ten newspapers, the reports by the Congressional Budget Office (CBO) that compile lists of tem- porary federal tax code provisions and Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters. At the same time, Baker S, Bloom N and Da- vis S J (2013) [18] used the data from the Hong Kong English newspaper SCMP (South China Morning Post) to construct China’s political uncertainty index. They filtered the articles containing specific terms in SCMP, such as including “Fields such as “China”, “Economy”, “Uncertainty”, then counted the number of articles in the South China Morning Post monthly, and standardized the data through the base period of 100 in January 1995. The uncertainty index con- structed by Baker et.al has constructed a new perspective to measure uncertainty through the perspective of media reports, and has been widely used by scholars. Ko and Lee (2015) [19] found a significant negative correlation by studying the impact of the policy uncertainty index on global stock prices. Baker S R, Bloom N and Davis S J (2016) [17] discusses the policy uncertainty mechanism that the United States has proliferated since 1960 through its uncertainty index. Domes- tic scholars conducted empirical research through the China Policy Uncertainty Index. Feng-Yu LI and Mozhu Yang (2015) [20] found that uncertainty has a significant negative relationship with corporate investment. Huili Zhang and Youhong Wu (2014) [21], Feng-Yu LI and Yong-Dong S (2016) [22] proposed a significant positive correlation with corporate cash holdings. Bekiros S (2016) [23] verified the relationship between unemployment rate and policy uncertainty after the US World War II data, and found that in the economic recession envi- ronment, the uncertainty shock will have a greater impact on the unemployment rate. Tsai (2017) [24] studied the impact of policy inaccuracies on China’s stock market and surrounding markets through China, the United States, Europe, and Japan through the EPU index. Demir and Gozgor (2018) [25] used the World Bank’s data on outbound tourism to study the spillover effects of policy uncer- tainty on the demand for outbound tourism in countries, and found that rising policy uncertainty would significantly reduce the demand for outbound tourism in a country.

Households view housing as a hedge during times of economic uncertainties. For instance, during high inflationary periods or macroeconomic uncertainties, savings are moved from liquid money to fixed assets, particularly housing. The relationship between policy uncertainty and housing values appears to be more complicated than originally believed. In their study, El Montasser, Ajmi, Chang, Simo-Kengne, Andre, and Gupta, (2016) found a bi- directional relationship between economic policy uncertainty and real housing prices in France and Spain, and a uni- directional causality from economic policy uncertainty to real housing prices in Canada, Germany and Italy, and a reverse causality between the two variables in the United States and United Kingdom. Cesa-Bianchi, Cespedes, and Rebucci, (2015) also found that housing prices grow faster, and are more volatile in emerging economies than in advanced economies. This indicates the need to study this relationship further in other countries to better understand the relationship between economic policy uncertainty and housing values.

En este documento elaboramos un nuevo índice de incertidumbre sobre las políticas económicas (Economic Policy Uncertainty, EPU) para España, siguiendo la infl uyente metodología de Baker, Bloom y Davis (2016), y lo comparamos con el elaborado por estos autores. El nuevo índice incorpora mejoras metodológicas, entre las que destacan, en primer lugar, la mayor cobertura de periódicos de referencia para el análisis textual (de dos a siete, entre los que se incluye prensa económico-fi nanciera); en segundo lugar, el uso de expresiones de búsqueda (palabras clave) más ricas y ajustadas al uso del español, y, fi nalmente, el uso de una muestra temporal más amplia. El nuevo índice proporciona una medición de la incertidumbre que captura los principales eventos de la historia reciente que se podrían asociar con aumentos de la incertidumbre sobre las políticas económicas. Asimismo, los aumentos inesperados de la incertidumbre de acuerdo con este índice se encuentran asociados a caídas de la actividad económica, del consumo privado y de la inversión empresarial.

The economic uncertainty is a key determinant of the business cycle and its effects on economic activity is mainly propagated either through household consumption decisions and delays in firms’ hiring plans or via delays in the investment activity in physical capital (Visco, 2017). More specifically, households tend to postpone spending and increase their precautionary savings when there is uncertainty surrounding monetary and fiscal policy decisions. Along a similar vein, when economic policy uncertainty is high, firms postpone their investment plans, given the irreversibility of such decisions (Pindyck, 1990), which results in lower productivity and higher levels of unemployment (Bloom, 2009; Bloom et al., 2012; Bloom, 2014). Kang et al. (2014) second these findings, arguing further that when the real sector is faced with uncertainty regarding future decisions in terms of health care costs, tax codes or changes in regulations, then it tends to delay investment plans. Such effects are particularly evident during recession periods. Wang et al. (2015) maintain that economic policy uncertainty could also impact the financial markets and thus financial decisions.

in June 2012. In addition, the financial uncertainty indicator reacts importantly to global and external events, such as Brexit in June 2016 (point L in Figure 1a) or the Dot-com re- cession in the early 2000s, as opposed to local crisis, such as the Catalan one in October 2017 (point M Figure 1a). In turn, the economic disagreement indicator does not react neither to global/external factors nor to local political events, while it mostly captures the Spanish Great Recession. This is understandable, since this indicator is intrinsically linked to reces- sionary periods in which it is harder to reach a consensus on future economic prospects. By contrast, the economic policy uncertainty indicator behave quite differently from the previous two, as expected. For instance, instead of jumping up at the time of the Lehman Brothers collapse, it increases gradually throughout the Great recession, reaching the highest level at the time in which Spain asked for the bank rescue package in June 2012. Afterward, it decreases with a similar gradual pace. In addition, the economic policy uncertainty indicator captures political events which may lead to an increase in economic policy uncertainty (e.g. the Catalan crisis: point M in Figure 1c). For instance, it raises at the time of the request for the Greek bail-out in April 2010 (point H in Figure 1c) and persists at a high level after- ward, possibly reflecting the deadlock situation that occurred before the EU interventions were defined. Another example may refer to periods preceding national elections, which coincide with the electoral campaigns. The latter can increase economic policy uncertainty depending on whether the electorate believes that the announced political stands will be followed coherently after the election’s results. 14

Up until recently measuring the impact of economic policy uncertainty (EPU) on stock market volatility has been difficult due to the lack of a standardized measure of EPU. Now, thanks to the work of Baker et al. (2016) there are standardized measures of EPU. The construction and publication of these indices has created a rapidly growing field investigating the impact of EPU on stock prices and stock price volatility. Baker et al. (2016), Brogard and Detzel (2016), and Yu et al. (2018) find that stock market volatility and EPU are closely correlated. Liu and Zhang (2015) find that higher EPU leads to significantly higher stock market volatility and that forecasting models with EPU provide better out of sample prediction compared to models that do not include EPU. Several other authors have found a significant relationship between economic policy uncertainty and stock prices (Arouri et al., 2016; Bekiros et al., 2016a, 2016b; Chen et al., 2017; Dakhlaoui and Aloui, 2016; Kang et al., 2015; Kang and Ratti, 2013; Li and Peng, 2017; Ozturk and Sheng, 2018; Tsai, 2017).

pected, growth in real housing market returns is associated with increases in industrial production growth and negative changes in the real federal funds rate. Also in line with the literature, house prices show inertia, as measured by autoregressive coefficients. Having set these control variables, we find a significant influence of lagged EPU on housing market returns. Moreover, increases in lagged real housing market returns significantly reduce economic policy uncertainty, when control- ling for implied stock market volatility (VIX) and growth in industrial production, which have the expected signs. In sum, there is a strong feedback loop between EPU and real housing market returns.

Focusing on the US and Canada, Caggiano et al. (2017) examined whether the US economic policy uncertainty affects Canadian business cycle fluctuations. The two countries were being chosen because they are highly interconnected and that shocks (such as total factor productivity, monetary policy, and fiscal policy shocks) originating in the US were shown to contribute to a significant proportion of the economic volatility in Canada. In their paper, they found a strong evidence of US economic policy uncertainty spillovers to Canadian business cycle, mainly in crises periods. Moreover, net exports show a significant, albeit short-lived, fall when the US economic policy uncertainty increases. Thus, they posited that a shock in the US policy uncertainty drives policy uncertainty in Canada, which consequently hurts Canada’s net exports to the US and thus, induces a temporary contraction on Canadian output. This is what they called “trade channel”.

therefore, must be constructed from observable variables. While, there exists no clear-cut consensus in terms of which approach to use in constructing measures of economic policy uncertainty, the news-based measures of uncertainty, as developed by Baker et al. [29], seem to have gained tremendous popularity in various applications in macroeconomics and finance [32]. This EPU measure encapsulates a wide range of policy uncertainty terms that appeared in the newspapers. For China, the South China Morning Post published in Hong Kong is used to collect the information. Articles on the newspaper is searched for the keywords such as ‘uncertainty’, ‘economic/economy’, ‘policy’, ‘tax’, ‘regulation’ and so on. The EPU index is downloaded from the economic policy uncertainty website (www.policy uncertainty.com/). Since only monthly data is available for China's EPU index, the Cubic-match last method is used to convert the monthly data to weekly data.

We investigate whether global Economic Policy Uncertainty (EPU) can predict exchange rates and their volatility in ten ASEAN countries. The foreign exchange market is regarded as the most liquid and largest financial market (Record, 2003). Exchange rate stability is important for building and maintaining a robust economy. Increased exchange rate volatility, for instance, can have negative effects on an economy, including: (1) greater uncertainty on future consumption (Obstfeld and Rogoff, 1998; Devereux, 2004); (2) increased volatility of business profitability (Braun and Larrain, 2005; Aghion, Bacchetta, and Rancière, 2009); (3) increased risk for domestic and foreign direct investment (Campa, 1993; Darby, Hallett, Ireland, and Piscitelli, 1999; Urata and Kawai, 2000; Servén, 2003; Byrne and Davis, 2005); (4) increased inflation uncertainty and higher interest rates along with reduced investment and consumption (Grier and Grier, 2006); and (5) changes in production cost and increased international transaction risk (Baum and Caglayan, 2006). Given these issues, predicting exchange rate and its volatility are of direct interest to central bank policymaking. Therefore, understanding what predicts exchange rate and its volatility is important.

price of oil, next to economic activity. For example, a 1% increase in the global supply of oil could lead to a decrease in the price of oil of approximately 5.3%. Economic policy uncertainty is only found to have some mild explanatory power on the change in the price of oil. Wars is not confirmed to have a direct effect on the price of oil during the sampling time periods, but is confirmed to have a highly significantly negative direct effect on the oil supply. Wars (and political tension) can affect the price of oil indirectly through their effects on the supply of oil. Oil consumption is found to be insignificant in explaining the dynamics of the price of oil during the sampled time periods, which is probably due to the fact that the demand for oil is strongly price inelastic during these time periods (Cooper 2003).

DOI: 10.4236/ajibm.2019.96091 1396 American Journal of Industrial and Business Management [1]. Sum (2012) also indicates that financial stress (FS) and EPU play an impor- tant role in the economic growth and recovery of the overall economy [2]. Cor- porations, consumers, and financial institutions become highly reluctant to make any investment, spending, and lending decision when a high level of FS is observed in the economy [3]. The reason why making those decisions is that the credit costs for economic entities will increase significantly during the situation of financial instability. In addition, when the macro-economy appears to become more uncertain, economic entities are more likely to suspend their investment projects, and to decrease their production capacity, they would reduce personnel scale and stop employing new employees as well. When investors and consumers are exposed to a higher degree of EPU, their willing of investment and consump- tion will be discouraged [4]. Unavoidably, the delay in investment and con- sumption might have a negative impact on economic recovery and growth [5].

Predicting future economic activities is important to all economic agents, such as consumers, firms and governments. In the literature, some forecasting models test the impacts of potential explanatory variables on output growth. Others focus on recession risk and forecast the onset of recession, using a binary variable indicating the periods of recession as a dependent variable. Recession is a more wide-ranging concept describing a country’s economic activities than growth slowdown. According to the National Bureaus of Economic Research (NBER) of the United States, recession is defined as a significant decline in economic activities that spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production and wholesale-retail sales (NBER’s Business Cycle Dating Committee (2010)). The Organization for Economic Co-Operation and Development (OECD) defines the turning points of business cycle in a similar way (OECD (2013)). Binary forecasting models statistically estimate an overall decline in economic variables, not just a slowdown of a single variable.

Typically, most research on corporate investments assumes that a firm’s investment decisions are determined by its investment opportunities, capital structure, cash flow, and information environment. The role of peer firms’ actions or characteristics on influencing corporate investment decisions is often ignored. However, there are recent studies providing evidences that corporate financial policies and investment decisions of a firm are positively correlated to industries (Bustamante and Fresard, 2017; Leary and Roberts, 2014). In other words, firms’ financing and investment decisions respond to the actions and the characteristics of their peer. Graham et al. (2001) survey a significant number of CFOs and find that they adopt their peer firms’ financing decisions as their own. Also, Lieberman (2006) shows that environmental uncertainty promotes imitation behavior between firms, where the mimicking behavior is tacit and complex. The goal of this paper is to empirically explore the effect of economic policy uncertainty on peer effects of firms’ investment decisions.

Next, we estimate Equation 2 in which we test whether firms in countries with stronger insolvency framework would be able to perform better during heightened economic uncertainty. In order to capture large policy uncertainty shocks, we create EP U shock, a dummy variable which takes the value 1 if the EP U index is one standard deviation above its average value and 0 otherwise. We interact this variable with the Insol score. The positive coefficient of the interaction term Insol score × EP U shock, shown in Table 5 column (1), suggests that the likelihood of debt distress of firms is moderated during episodes of heightened economic policy uncertainty in countries with stronger creditor rights. A one standard deviation increase in the creditor rights score (equivalent to 27.9 unit increase) moderates the adverse effect of an EP U shock on firm’s default risk score by about 0.14, which is about 5.5% of the Z-score of the median firm in our sample. As expected, the negative and significant coefficient of the level effect of EP U shock suggests that the default risk of firms is higher (lower Z-score) during episodes of heightened economic policy uncertainty.

This table reports the results of time-series regressions of interest rate-sorted currency portfolios on various explanatory variables akin to equation (7). “pf1” and “pf3” denote the portfolios with the lowest and highest interest rate differential vis-`a-vis the U.S., respectively. “dol” denotes the portfolio that is short the U.S. dollar and long all other currencies. The dependent variable is the portfolios’ excess returns from 4pm to 4pm ET. The announcement dummy takes the value of one on days when the FOMC makes an announcement and zero otherwise. “EPU” is the economic policy uncertainty index of Baker et al. (2016); “DiB FF” is the cross-sectional standard deviation of forecasts constructed from surveys about the future federal funds rate available from Bloomberg; “RV” is realized exchange rate volatility measured over a two-hour window around the time of an FOMC announcement; “VIX” is S&P500 implied volatility; and “CDS” is the average five-year CDS spread of Citibank, JPMorgan, Bank of America, and Goldman Sachs. “EPU”, “RV”, “VIX”, and “CDS” are demeaned and scaled by their respective sample standard deviations. For brevity, the table only reports estimated coefficient for the interaction term. Data runs from January 1, 1994 to December 31, 2013 for all regressions except for the CDS regressions which run from April 1, 2001 to December 31, 2010. Newey and West (1987) t-statistics are in parentheses.

This study attempted to answer the question: “Are there any effects of economic policy uncertainty on real economic activity? If so, how large is the impact of policy uncertainty shocks?” The evidence found that there is an impact of policy uncertainty. Especially if the evidence represented changes in uncertainty felt by households, investors and firms. To determine the impact of these shocks an estimate series of VAR with a new measure of economic policy uncertainty proxied by newspaper coverage for both the US (𝑁𝑒𝑤𝑠𝑈𝑆) and India (𝑁𝑒𝑤𝑠𝐼𝑁𝐷) was utilized. The evidence found that an unanticipated increase in uncertainty, regardless of the measure, results in a sharp, temporary recession. Furthermore, industrial production, consumption, productivity and investment declined, whereas unemployment was likely to rise. The responses to these shocks were rapid and the recovery time thereof was relatively short. Generally, the patterns suggested a role for models of this kind as presented in Bloom (2009). The responses of investment can assist in discriminating between competing theories of how uncertainty changes investment behavior (Alexopoulos & Cohen, 2009). The impulse responses have shown that economic policy uncertainty is a very powerful index. The findings on the economic policy uncertainty suggested that other uncertainty shocks should be added such as technology shocks, oil prices and the news shock as a significant contributor to the short-run business cycle fluctuations. As such, future research should focus on identifying which types of uncertainty shocks are most damaging to the economy, and also to create models that clearly capture these shocks. Even when focusing specifically on India as a Euro area country, this papers findings also answer the question posed by Colombo (2013). Specifically, Colombo (2013) asks, “Are there any spillovers from the US economy to the Euro area due to economy policy uncertainty shocks?” But it was proven that the VARs rendered an undesirable, mild and insignificant response of the Indian macroeconomic variables to an unanticipated surge in the US policy uncertainty.

Equivalence states that government debt has no effect in a frictionless standard representative- agent model (Barro, 1974). However, in the presence of liquidity and safety needs, govern- ment debt plays a special role and has significant effects on macroeconomic quantities and asset prices (Bansal and Coleman, 1996; Krishnamurthy and Vissing-Jorgensen, 2012; Gor- ton and Ordonez, 2013; Drechsler et al., 2014; Greenwood et al., 2015). The impact of government debt is also large in heterogeneous agent incomplete market models (Gomes et al., 2013). These theories are either silent on the new empirical findings or have counter- factual implications that high government debt is related to low equity premium and high risk-free rate. I contribute to the understanding of government debt by proposing a new fiscal policy uncertainty channel which operates through the government discount rate and also affects other risk premia. Because debt-to-GDP ratio encodes the variation of fiscal un- certainty, it explains risk premium variation, which complements the existing explanations of time-varying risk aversion (Campbell and Cochrane, 1999), time-varying consumption volatility (Bansal and Yaron, 2004), and time-varying risk of disasters (Wachter, 2013). My analysis of fiscal uncertainty also relates to the recent literature examining the role of economic uncertainty both in the data and models (Bloom, 2009a; Bansal et al., 2014a; Jurado et al., 2015b, among others). P´ astor and Veronesi (2013) and Baker et al. (2015) study the asset pricing and macroeconomic impacts of general economic policy uncertainty. Fern´ andez-Villaverde et al. (2015) and Born and Pfeifer (2014) show the importance of fiscal uncertainty on economic activities. I propose a new broad-based measure of fiscal policy uncertainty and illustrate its importance for asset prices.

Abstract: Financial risk management is difficult at the best of times, but especially so in the presence of economic policy uncertainty. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic policy uncertainty, specifically the power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk, model-free volatility indexes in the financial literature, robust hedging performance and volatility risk in option markets, price cointegration between sovereign CDS and currency option markets in the GFC, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the GFC, managing financial risk in Chinese stock markets, managing systemic risk in The Netherlands, mean- variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.

While writing this paper, we became aware of a contribution by Bontempi, Golinelli, and Squadrani (2016). They construct an index of economic uncertainty for the United States using Google Trends data, contrast it with alternative measures of uncertainty, and use their measure in a VAR context to analyze the contribution of uncertainty shocks for the dynamics of employment and industrial production. With respect to them, we develop an uncertainty index also for Australia, focus on the unemployment rate as business cycle indicator given its central role for policymakers, and contrasts the role played by uncertainty shocks with that played by monetary policy shocks, which have been shown to have an in‡uence on proxies of uncertainty (Pellegrino, 2017). Our contribution is also close to Baker, Bloom, and Davis (2016), who construct an index of economic policy uncertainty (EPU) for a number of countries including the United States and Australia by searching uncertainty-related keywords conditional on a set of widely read country-speci…c newspapers, and to Moore (2017), who constructs an index of economic uncertainty for Australia based on keywords searches conditional on a set of Australian newspapers. 2 We construct novel indices of uncertainty with a similar