Industry-level determinants of the linkage between credit and stock markets

This paper examines the relationship between US credit default swaps (CDS) and stock returns on an industry-wide basis across a number of investment horizons, with particular focus on the major determinants of such a relationship. Wavelet analysis is first applied to extract the CDS–stock wavelet correlation for each US industry. Then, Bayesian Model Averaging is employed to identify the key driving factors of the industry CDS–stock wavelet correlations at short- and long-term horizons. The empirical results indicate that the wavelet correlations between the industry CDS and stock returns are primarily negative over time and across time scales. Moreover, the CDS–stock correlation at longer horizons exhibits a much more stable pattern than its counterpart at shorter time frames. The results also demonstrate that the volatility of US Treasury and stock markets, as measured by the MOVE and VIX indices, respectively, the volatility of volatility, as captured by the VVIX index, and US economic policy uncertainty, as measured by the EPU index, are the most robust determinants of the correlation between CDS and stock returns at shorter and longer horizons for most US industries. In contrast, the Fama–French systematic equity factors exhibit a practically negligible explanatory power on the CDS–stock link.     

The recent boom–bust cycle: The relative contribution of capital flows, credit supply and asset bubbles

We use an estimated open economy DSGE model with financial frictions for the US and the rest of the world to evaluate various competing explanations about the recent boom–bust cycle. We find that the savings glut hypothesis is insufficient for explaining all aspects of the boom in the US. Relatively strong TFP growth and expansionary monetary policy are also not able to explain fully the volatility of corporate and in particular residential investment. We identify bubbles in the stock and housing market as crucial. Concerning the downturn in 2008/2009, the fall in house prices and residential investment only plays a minor role. Mortgage defaults have more explanatory power, especially in a specification of the model with a segregated equity market. Finally, the bursting of the stock market bubble was at least as important in this recession as in 2001. Because of various negative shocks hitting the economy at the same time in 2008/2009 and continued positive technology growth, not only the real interest rate declined but inflation fell rapidly and left insufficient room for monetary policy to play a similar stabilising role as in previous recessions.     

Predicting Canadian recessions using dynamic probit modelling approaches

Abstract This paper examines the ability of various financial and macroeconomic variables to forecast Canadian recessions. It evaluates four model specifications, including the advanced dynamic, autoregressive, dynamic autoregressive probit models as well as the conventional static probit model. The empirical results highlight several significant recession predictors, notably the government bond yield spread, growth rates of the housing starts, the real money supply and the composite index of leading indicators. Both the in‐sample and out‐of‐sample results suggest that the forecasting performance of the four probit models is mixed. The dynamic and dynamic autoregressive probit models are better in predicting the duration of recessions while the static and autoregressive probit models are better in forecasting the peaks of business cycles. Hence, the advanced dynamic models and the conventional static probit model can complement one another to provide more accurate forecasts for the duration and turning points of business cycles.     

Forecasting US recessions with macro factors

Dynamic factors estimated from panels of macroeconomic indicators are used to predict future recessions using probit models. Three factors are considered: a bond and exchange rates factor, a stock market factor and a real activity factor. Three results emerge. First, models that use only financial indicators exhibit a large deterioration in fit after 2005. Second, models that use factors yield better fit than models that use indicators directly. Out-of-sample forecasting exercises confirm these results for 3-, 6- and 12-month horizons using both ex-post revised data and real-time data. Third, results show evidence that data revisions affect factors less than individual indicators.     

Economic policy uncertainty in the US: Does it matter for the Euro area?

We investigate the effects of a US economic policy uncertainty shock on some Euro area macroeconomic aggregates via Structural VARs. We model the indicators of economic policy uncertainty recently developed by  Baker et al. (2013) jointly with the aggregate price indexes and alternative indicators of the business cycle for the two above indicated economic areas. According to our SVARs, a one standard deviation shock to US economic policy uncertainty leads to a statistically significant fall in the European industrial production and prices of −0.12% and −0.06%, respectively. The contribution of the US uncertainty shock on the European aggregates is shown to be quantitatively larger than the one exerted by an Euro area-specific uncertainty shock.     

Unemployment dynamics in emerging countries: Monetary policy and external shocks

This paper quantifies the impact of three key external shocks – external demand, interest rate, and uncertainty shocks – on emerging market economies (EMEs). We find that external shocks have a sizeable impact on macroeconomic fluctuations in EMEs and that a considerable fraction of this impact is through the domestic stock market. A decrease in external demand and an increase in external interest rate and uncertainty lead to a higher unemployment rate, lower stock market return, and a depreciation of the domestic currency. The EMEs' monetary policy actively responds to external shocks and dampens their impact on domestic activity.     

On the determinants of long-run inflation uncertainty: Evidence from a panel of 17 developed economies

Based on a cross section of 17 advanced economies and data for the period 1975 to 2015, we examine how the interaction between monetary policy and macroeconomic conditions affects inflation uncertainty in the long-term. We construct a proxy for the unobservable inflation uncertainty based on the slowly evolving long-term variance component of inflation from a Spline-GARCH model (Engle and Rangel, 2008). We show that long-run inflation uncertainty is high if an inflation-tolerant central bank governor is in power during a period of high inflation, if the policy rate is below the one that is prescribed by the Taylor rule and during times of heightened stock and exchange rate volatility.     

Persistence of economic uncertainty: a comprehensive analysis

One of the most heavily researched and cited issue in applied economics is the relationship of uncertainty indices with the financial and macroeconomic variables. While the statistical features of financial and macroeconomic variables have been thoroughly examined, virtually nothing has been done to examine uncertainty indices under the statistical perspective. In this paper, we focus on two primary characteristics of uncertainty indices: persistence and chaotic behaviour. In order to evaluate the persistence and the chaotic behaviour we analyse 72 popular uncertainty indices constructed by forecasting models, text mining from news articles and data mining from monetary variables to measure the Hurst and Lyapunov exponents in rolling windows. The examination in rolling windows provides a dynamic evaluation of the specific characteristics revealing significant variations of persistence and chaotic dynamics with time. More specifically, we find that almost all uncertainty indices are persistent, while the chaotic dynamics are detected only sporadically and for certain indices during recessions of economic turbulence. Thus, we suggest that the examination of persistence and chaos should be a prerequisite step before using uncertainty indices in economic policy models.     

Fiscal policy over the real business cycle: A positive theory

This paper explores the implications of the political economy model of Battaglini and Coate (2008) [8] for the behavior of fiscal policy over the business cycle. The model predicts that fiscal policy is counter-cyclical with debt increasing in recessions and decreasing in booms. Public spending increases in booms and decreases during recessions, while tax rates decrease during booms and increase in recessions. In both booms and recessions, fiscal policies are set so that the marginal cost of public funds obeys a submartingale. When calibrated to the US economy, the model broadly matches the empirical distribution of debt and also its negative correlation with output. However, the predictions of pro-cyclical spending and counter-cyclical taxation do not find empirical support. The calibrated model generates the same fit of the data as a benevolent government model in which the government faces an exogenous lower bound on debt. Nonetheless, the two models have very different comparative static implications.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Forecasting unemployment across countries: The ins and outs

This paper evaluates the flow approach to unemployment forecasting proposed by Barnichon and Nekarda (2012) for a set of OECD countries characterized by very different labor markets. We find that the flow approach yields substantial improvements in forecast accuracy over professional forecasts for all countries, with especially large improvements at longer horizons (one-year ahead forecasts) for European countries. Moreover, the flow approach has the highest predictive ability during recessions and turning points, when unemployment forecasts are most valuable.     

Irreversible investment with regime shifts

Under the real options approach to investment under uncertainty, agents formulate optimal policies under the assumption that firms’ growth prospects do not vary over time. This paper proposes and solves a model of investment decisions in which the growth rate and volatility of the decision variable shift between different states at random times. A value-maximizing investment policy is derived such that in each regime the firm's investment policy is optimal and recognizes the possibility of a regime shift. Under this policy, investment is intermittent and increases with marginal q. Moreover, investment typically is very small but, in some states, the capital stock jumps. Implications for marginal q and the user cost of capital are also examined.     

A Note on the (Un)Pleasant Arithmetic of Fiscal Policy: The Case of Italian Public Debt

Using the simple arithmetic of government budget constraint, we perform an illustrative analysis on the Italian case, investigating the consequences on the main public finance aggregates of the adoption of a fiscal policy rule responding to past real debt/GDP ratio. Such a rule, firmly grounded in the economic analysis, would allow the reduction of Italy's outstanding stock of debt without requiring the strict adherence to the 3 per cent criterion for deficit/GDP ratio, as prescribed by SGP (Stability and Growth Pact). We perform a forecasting exercise under five alternative scenarios and analyse the details of a structural debt reduction strategy with alternative yearly step.     

Global macroeconomic uncertainty

We empirically identify global macroeconomic uncertainty using a dynamic factor model, where the conditional variances of all factors are modeled as stochastic volatility processes. Applying this methodology to OECD data, we find the early 1970s and early 1980s recessions as well as the recent Great Recession of the late 2000s to be associated with increases in uncertainty at the global level, but heightened uncertainty during the early 1990s and 2000s slowdowns to be mostly confined to the national levels. We also find that global uncertainty unambiguously lowers national growth rates and raises national inflation rates, and that key macroeconomic variables like oil, commodity and stock prices as well as global liquidity act as drivers of the global dimension of uncertainty.     

Precautionary Principle and Robustness for a Stock Pollutant with Multiplicative Risk

The interest shown by policy makers and economists in the precautionary principle indicates the importance of model uncertainty in global warming policy. I show that through robust control, policy makers can implement the precautionary principle to regulate a stock pollutant, and I analyze its effect on expected steady state pollution taxes, stocks and welfare. The paper is broadly comprised of a theoretical part and an application to global warming policy. I find that: (1) an increase in either uncertainty about the model or risk about abatement cost increases expected steady state pollution taxes; (2) a robust policy is preferred for any level of model uncertainty and this preference increases for either higher model uncertainty or higher multiplicative risk and (3) the effect on expected steady state pollution taxes and stock of introducing model uncertainty is relatively small for high levels of model uncertainty. These results advocate using robust policies for a stock pollutant in the presence of model uncertainty.      

Oil shocks,policy uncertainty and stock returns in China

This paper examines the interdependence of China's policy uncertainty, the global oil market and stock market returns in China. A structural VAR model is estimated that shows that a positive shock to economic policy uncertainty in China has a delayed negative effect on global oil production, real oil prices and real stock market returns. Shocks to oil market‐specific demand significantly raise China's economic policy uncertainty and reduce the real stock market returns. As measured by a spillover index, the interdependence between these variables has been rising since 2003 as China's influence in the oil market has increased. An equivalent spillover index calculated for the US is smaller and has been largely flat over time.     

US economic policy uncertainty and co-movements between Chinese and US stock markets

This paper investigates the impact of innovations in US economic policy uncertainty on the co-movements of China's A/B stock markets with the US stock market. We show that it is the absolute changes in the US economic policy uncertainty index that have a negative impact on the co-movements. The finding is robust to the asymmetric effects of non-policy-uncertainty shocks, to a break in the correlation structure, and to the four Chinese A/B stock markets investigated. Our results provide the first evidence regarding how stock market correlations are driven by policy-related uncertainty shocks in the international context.     

Financial variables and euro area growth: A non-parametric causality analysis

This paper investigates the predictive ability of financial variables for euro area growth through bivariate and multivariate non-parametric Granger causality tests. Apart from assessing the within-country forecasting ability of commonly-employed financial variables, such as the term spread, the stock market returns and the growth of real money supply, we also test for cross-country influences. In this way, we reveal the countries that are more useful in predicting growth in other member countries along with the ones that are more receptive to other countries' financial developments. Our results suggest that financial variables are useful leading indicators for euro area growth at a joint level, albeit at different horizons, ranging from one to six quarters. Our finding of overall increased levels of receptivity among member states provides useful information for policy makers, especially in the case of monetary union such as the euro area.     

A new ICEEMDAN-based transfer entropy quantifying information flow between real estate and policy uncertainty

We quantify the dependence between real estate indices and global economic policy uncertainty for 12 top-advanced countries. Generally, real estate investments are found to be highly risky to information flow from global economic policy uncertainty. amidst policy uncertainty, we find diversification, safe haven, and hedging prospects – based on the market conditions – at short term frequencies only, for the pairs between (a) Japan and the US; (b) Singapore and the US; (c) China and Canada; (d) China and Hong Kong. Our findings underscore market efficiency (inefficiency) at mid-and long-term (short-term) frequencies. In the presence of policy uncertainty, our findings underscore the operability of ingrained market dynamics between real estate investments in the mid-and long-term horizons. It is prudent for investors to combine real estate investments with other asset classes that are less risky to (or are positive recipients of) information flow from global EPU to hedge against adverse market shocks from any asset in the portfolio based on market conditions. Practically, not only should legislations be flexible to the changing market trends in the short term, but they should also be strategically crafted to retain the fundamental market dynamics between real estate investments in the mid-and long-term economic horizons.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.