Economic policy uncertainty and stock market returns: New evidence

This paper investigates the predictive performance of the Chinese economic policy uncertainty (EPU) index constructed by Davis, Liu, and Sheng (2019) in forecasting the returns of China’s stock market. Using the univariate and bivariate predictive regression model, we confirm that the monthly EPU index can significantly and negatively impact the next month’s stock returns, and has better out-of-sample predictability than the existing EPU index and several macroeconomic variables. By comparing the forecasting effect of the EPU index before and during special events with sharply increased uncertainty, we find that the EPU’s forecasting power decline rapidly when an event of sharply increased uncertainty occurs. Finally, our conclusions are consistent through a batch of robustness tests.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility

This paper constructs an aligned global economic policy uncertainty (GEPU) index based on a modified machine learning approach. We find that the aligned GEPU index is an informative predictor for forecasting crude oil market volatility both in- and out-of-sample. Compared to general GEPU indices without supervised learning, well-recognized economic variables, and other popular uncertainty indicators, the aligned GEPU index is rather powerful and can provide preponderant or complementary information. The trading strategy based on the aligned GEPU index can also generate sizable economic gains. The statistical source of the aligned GEPU index’s predictive power is that it can learn both the magnitude and sign of national EPU variables’ predictive ability and thus yields reasonable and informative loadings. On the other hand, the economic driving force probably stems from the ability for forecasting the shocks of oil-related fundamentals.     

VIX forecasting based on GARCH-type model with observable dynamic jumps: A new perspective

This paper proposes to study VIX forecasting based on discrete time GARCH-type model with observable dynamic jump intensity by incorporating high frequency information (DJI-GARCH model). The analytical expression is obtained by deducing the forward iteration relations of vector composed of conditional variance and jump intensity, and parameters are estimated via maximum likelihood functions. To compare the pricing ability, we also present VIX forecasting under four simple GARCH-type models. Results find that DJI-GARCH model outperforms other GARCH-type models for the whole sample and stable period in terms of both in-sample and out-of-sample forecasting, and for the in-sample forecasting during crisis period. This indicates that incorporating both realized bipower and jump variations, and combining VIX information in the estimation can obtain more accuracy forecasting. However, the out-of-sample forecasting using parameters estimated from crisis period shows that GARCH and GJR-GARCH models performs relatively better, which reminds us to be cautious when making out-of-sample prediction.     

What does money and credit tell us about real activity in the United States?

We analyse the forecasting power of different monetary aggregates and credit variables for US GDP. Special attention is paid to the influence of the recent financial market crisis. For that purpose, in the first step we use a three-variable single-equation framework with real GDP, an interest rate spread and a monetary or credit variable, in forecasting horizons of one to eight quarters. This first stage thus serves to pre-select the variables with the highest forecasting content. In a second step, we use the selected monetary and credit variables within different VAR models, and compare their forecasting properties against a benchmark VAR model with GDP and the term spread (and univariate AR models). Our findings suggest that narrow monetary aggregates, as well as different credit variables, comprise useful predictive information for economic dynamics beyond that contained in the term spread. However, this finding only holds true in a sample that includes the most recent financial crisis. Looking forward, an open question is whether this change in the relationship between money, credit, the term spread and economic activity has been the result of a permanent structural break or whether we might return to the previous relationships.     

美国金融危机对我国贸易顺差的影响及对策研究

美国金融危机影响已由虚拟经济扩散到实体经济，由发达经济体扩展到新兴经济体。持续多年的贸易顺差是当前我国经济增长的主要推动力，为我国积累了巨额的外汇储备。然而，金融危机导致我国贸易顺差额大幅收窄，FDI连续十个月同比负增长及经济增速放缓。为了应对美国金融危机给我国对外贸易、经济带来的消极影响。为此，建议从加大创新力度，提高出口企业产品附加值，转变出口市场结构等三方面来寻求应对危机的有效办法。     

Economic indicators and stock market volatility in an emerging economy

By analyzing the daily realized volatility series calculated from intraday stock price observations, this study examines the direct causality between one-day-ahead aggregate stock market volatility and several economic and financial indicators in the Korean market, a leading emerging market. Using the predictive regression and superior predictive ability tests, we find that the model-free implied volatility index (VKOSPI) and stock market indicators both lead the daily market volatility. However, daily economic indicators provide no predictive information beyond that contained in historical volatility. Though in-sample causality does not guarantee a better out-of-sample forecasting performance, the VKOSPI and combinations of predictors exhibit significant predictive ability regardless of the time period. Our study verifies the information role of the VKOSPI as an indicator of daily market risk.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Real Estate and the Asian Crisis

This paper suggests that activities in the real estate markets in Southeast and East Asian economies were an important contributing force to the financial crises of 1997 in the Asian economies. The analysis relies upon unpublished data reported contemporaneously by financial institutions and market watchers to document the extent of the imbalances in the real property market that were evident to informed observers at the time of the financial collapse. The analysis argues that a series of reforms in the regulation of the property market and the treatment of real property loans by financial institutions are necessary to prevent the recurrence of the kind of speculative bubble that contributed to the financial crises in Asia. Given the recentness of the crisis, the nature of the data, and the absence of definitive statistical sources, the results are tentative, but they are certainly consistent with a financial collapse whose proximate cause was unchecked activity in the property market.     

Belgian economic policy uncertainty index: Improvement through text mining

Recently, the literature has measured economic policy uncertainty using news references, resulting in the frequently-mentioned ‘Economic Policy Uncertainty index’ (EPU). In the original setup, a news article is assumed to address policy uncertainty if it contains certain predefined keywords. We argue that the original setup is prone to measurement error, and propose an alternative methodology using text mining techniques. We compare the original method to modality annotation and support vector machines (SVM) classification in order to create an EPU index for Belgium. Validation on an out-of-sample test set speaks in favour of using an SVM classification model for constructing a news-based policy uncertainty indicator. The indicators are then used to forecast 10 macroeconomic and financial variables. The original method of measuring EPU does not have predictive power for any of these 10 variables. The SVM indicator has a higher predictive power and, notably, changes in the level of policy uncertainty during tumultuous periods of high uncertainty and risk can predict changes in the sovereign bond yield and spread, the credit default swap spread, and consumer confidence.     

Stress-testing US bank holding companies: A dynamic panel quantile regression approach

We propose an econometric framework for estimating capital shortfalls of bank holding companies (BHCs) under pre-specified macroeconomic scenarios. To capture the nonlinear dynamics of bank losses and revenues during periods of financial stress, we use a fixed effects quantile autoregressive (FE-QAR) model with exogenous macroeconomic covariates, an approach that delivers a superior out-of-sample forecasting performance relative to the standard linear framework. According to the out-of-sample forecasts, the realized net charge-offs during the 2007–09 crisis fall within the multi-step-ahead density forecasts implied by the FE-QAR model, but are frequently outside the density forecasts generated using the corresponding linear model. This difference reflects the fact that the linear specification substantially underestimates loan losses, especially for real estate loan portfolios. Employing the macroeconomic stress scenario used in CCAR 2012, we use the density forecasts generated by the FE-QAR model to simulate capital shortfalls for a panel of large BHCs. For almost all institutions in the sample, the FE-QAR model generates capital shortfalls that are considerably higher than those implied by its linear counterpart, which suggests that our approach has the potential to detect emerging vulnerabilities in the financial system.     

Forecasting the US housing market

The recent housing market boom and bust in the United States illustrates that real estate returns are characterized by short-term positive serial correlation and long-term mean reversion to fundamental values. We develop an econometric model that includes these two components, but with weights that vary dynamically through time depending on recent forecasting performances. The smooth transition weighting mechanism can assign more weight to positive serial correlation in boom times, and more weight to reversal to fundamental values during downturns. We estimate the model with US national house price index data. In-sample, the switching mechanism significantly improves the fit of the model. In an out-of-sample forecasting assessment the model performs better than competing benchmark models.     

Navigating the New Normal: Political Affinity and Multinationals’ Post-Acquisition Performance

The New Normal in the international business landscape reflects a world challenged by economic volatility and political hostilities. This suggests increased political risk, even for MNEs operating in developed markets. We use the legitimacy-based view of political risk to examine how political affinity between host and home markets may contribute to an MNE’s post-acquisition performance in a developed market. A high degree of political affinity signifies aligned national interests thus reducing legitimacy concerns faced by MNEs during post-acquisition integration. Based on cross-border M&A deals focused on U.S. targets completed by MNEs representing 45 countries between 2004 and 2012, we find that MNEs from countries with greater political affinity to the U.S. experience better post-acquisition performance. We also investigate two country-level factors that intensify the threat to legitimacy; the MNEs’ home market economic status and the presence of a financial crisis in the host market. Our findings indicate that political affinity mitigates risk for MNEs originated from emerging economies much more than for MNEs originated from developed economies, whereas a financial crisis reduces the benefit of political affinity.     

Capital flows and the boom–bust cycle: The case of Estonia

During 2000–2007, Estonia was among the fastest growing emerging market economies, but in late-2008 entered a deep recession. This paper examines shocks, institutions, and policies that have made Estonia's boom–bust cycle so severe. It finds that an open capital account, the prospect for EU entry, and the currency board facilitated massive capital inflows, which led to credit and real estate booms. In late-2008 a domestic slowdown was greatly amplified by the global financial and economic crisis. To resume sustainable growth, the country will need to regain competitiveness and rebalance resources to exports. Estonia's experience underscores the importance for other emerging market economies to retain some flexibility in their macroeconomic frameworks and approach capital account liberalization cautiously.     

How does the economic policy uncertainty affect the relationship between financial slack and firm performance in emerging countries?

This study investigates the moderating effect of economic policy uncertainty (EPU) on the relationship between slack resources and firm performance in emerging countries. By using 47,523 firm-year observations from seven emerging market economies through the period of 2010–2018, the results show an inverse U-shaped relationship between slack resources and firm performance. The findings also demonstrate that EPU acts as a negative moderator for the slack–performance relationship. In high EPU environments, the marginal benefit from one additional unit of slack is smaller relative to low EPU environments. This study contributes to the slack resources-firm performance literature by emphasizing the role of the broader external environment as a moderator.     

Equity market and money supply spillovers and economic growth in BRICS economies: A global vector autoregressive approach

Previous studies report that private credit as a proxy of financial development contributes to economic growth in BRICS economies. This paper employs three additional measures of financial development, namely equity market, money supply and market capitalization, and further investigates cross-country evidence on the impact of equity market and money supply spillovers on economic growth in BRICS economies. Utilizing a Global Vector Autoregressive (GVAR) framework and quarterly data from 1989Q1 and to 2012Q4 from BRICS economies, we find that equity market and money supply variables do not predict the contributions of financial development in each BRICS member in boosting economic growth in the other member countries. However, market capitalization significantly influences economic growth. These results suggest that, besides private credit, market capitalization is another key channel of promoting growth in individual economies and the region. Policy implications of the findings are discussed.     

The Paradoxes and Pitfalls of Revived Fiscal Activism

This paper critiques the revival of fiscal activism by the G20 in response to the global financial crisis of 2008–9. It first re‐examines the international macroeconomic conditions leading up to that crisis, before highlighting the paradoxes and pitfalls of revived fiscal activism for advanced economies. It argues that the harmful legacy of budget deficits and escalated public debt levels spawned further financial crises, most notably in southern Europe, and generally damaged household and business confidence to the detriment of private consumption and investment spending, which has delayed economic recovery. To undertake the repair necessary to the fiscal accounts of most advanced economies, the paper proposes that government spending programmes need to be thoroughly reviewed against the key principles of public finance.     

Forecasting volatility via stock return,range, trading volume and spillover effects: The case of Brazil

For the purpose of developing alternative approach for forecasting volatility, we consider heterogeneous VAR (HVAR) model which accommodates the market effects of different horizons, namely, daily, weekly and monthly effects, and examine the interdependence of stock markets in Brazil and the US, based on information of daily return, range and trading volume. To compare with the new approach, we also work with the univariate and multivariate GARCH models with asymmetric effects, trading volumes and fat-tails. The heteroskedasticity-corrected Granger causality tests based on the HVAR show the strong evidence of such spillover effects. We assess the value-at-risk thresholds for Brazil, based on the out-of-sample forecasts of the HVAR model, finding the new approach works satisfactory for the periods including the global financial crisis, without assuming heavy-tailed conditional distributions.     

Time-frequency connectedness of crude oil,economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis

This article investigates the time–frequency connectedness of economic policy uncertainty (EPU), WTI crude oil and Chinese commodity markets during the period between 2004 and 2020. Rolling window wavelet vector autoregression and connectedness networks are developed to evaluate the time-varying characteristics of the connectedness. The empirical results are as follows: First, the total connectedness between EPU, oil and commodities becomes stronger as the time scale increases. Second, the net connectedness of EPU and WTI in the system is positive, indicating that EPU and WTI are contributors to information and will affect financial markets across time scales. Third, the connectedness remains at a high level during financial crises across all scales, and the contribution of EPU and crude oil to commodities increases significantly. Specifically, compared with other commodity sectors, grains are greatly affected by EPU under the condition that the energy sector is seriously affected by crude oil. Overall, investors and policy makers should consider connectedness in terms of time and frequency when making a decision.     

Asymmetries between Rich and Poor Countries in Financial Crisis Responses: The Need for a Flow-of-Funds Approach

This paper focuses on the manner in which interest rates have been raised to, and sustained at, extremely high levels in developing and emerging market economies as a consequence of recent financial crises. By contrast rich market economies have typically lowered interest rates and injected liquidity in response to incipient financial crises. The paper first sketches the logic that lies behind extremely high interest rates (nominal and real) as an element of crisis resolution. It suggests that this reflects a money-phobic view of financial markets and also conflicts with some well-established economics. It then reviews the conventional wisdom about why richer economies have enjoyed sustained price stability in recent years and why this in turn has allowed their monetary authorities to be relaxed about injecting additional liquidity in response to LTCM (1998) and September 11 (2001)-type crises. It is pointed out that this conventional wisdom is also money-phobic in that it neglects the build up of corporate and government debt in bond and financial derivative form that has been associated with recent financial developments. This analysis helps to contest the common view that emerging market economies pay a higher price merely because their polices are "bad'. Finally, the paper reviews the manner in which the financial systems of developing and emerging market economies respond to the destabilization created by corrosively high real rates of interest. Even when bankruptcy arrangements are well established, certain new forms of financial flows and instruments are implicit in this response, but are invariably ignored in formal modelling.