The financial content of inflation risks in the euro area

Recent studies have emphasized that survey-based inflation risk measures are informative about future inflation, and thus are useful for monetary authorities. However, these data are typically only available at a quarterly frequency, whereas monetary policy decisions require a more frequent monitoring of such risks. Using the ECB Survey of Professional Forecasters, we show that high-frequency financial market data have predictive power for the low-frequency survey-based inflation risk indicators observed at the end of a quarter. We rely on MIDAS regressions for handling the problem of mixing data with different frequencies that such an analysis implies. We also illustrate that upside and downside risks react differently to financial indicators.     

Money demand and the role of monetary indicators in forecasting euro area inflation

This paper examines the stability of money demand and the forecasting performances of a broad monetary aggregate (M3), excess liquidity and excess inflation in predicting euro area inflation. The out-of sample forecasting performances are compared to a widely used alternative, the spread of interest rates. The results indicate that the evolution of M3 is still in line with money demand, even when observations from the economic and financial crisis are included. Both excess measures and the spread are useful for predicting inflation.     

Do labor market institutions matter for business cycles?

Using panel data of 19 OECD countries observed over 40 years and data on specific labor market reform episodes we conclude that labor market institutions matter for business cycle fluctuations. Spearman partial rank correlations reveal that more flexible institutions are associated with lower business cycle volatility. Turning to the analysis of reform episodes, wage bargaining reforms increase the correlation of the real wage with labor productivity and the volatility of unemployment. Employment protection reforms increase the volatility of employment and decrease the correlation of the real wage with labor productivity. Reforms reducing replacement rates make labor productivity more procyclical.     

Does the Phillips curve help to forecast euro area inflation?

We find that it does, but choosing the right specification is not trivial. Based on an extensive forecast evaluation we document notable forecast instabilities for most simple Phillips curves. Euro area inflation was particularly hard to forecast in the run-up to the Economic and Monetary Union and after the sovereign debt crisis, when the trends—and, for the latter period, also the amount of slack—were harder to pin down. Yet, some specifications outperform a univariate benchmark and point to the following lessons: (i) the key type of time variation to consider is an inflation trend; (ii) a simple filter-based output gap works well, but after the Great Recession it is outperformed by endogenously estimated slack or by “institutional” estimates; (iii) external variables do not bring forecast gains; (iv) newer-generation Phillips curve models with several time-varying features are a promising avenue for forecasting; and (v) averaging over a wide range of modelling choices helps.     

Openness and inflation volatility: Panel data evidence

Trade openness can affect inflation volatility via the incentives faced by policy-makers or the structure of production and consumption, but the sign of this effect, as predicted from economic theory, is ambiguous. This paper provides evidence for a negative effect of openness on inflation volatility using a dynamic panel model that controls for the endogeneity of openness and the effects of both average inflation and the exchange rate regime. Our results offer one explanation for the recent decline in inflation volatility observed in many countries. The relationship is shown to be strongest amongst developing and emerging market economies, and we argue that the mechanisms linking openness and inflation volatility are likely to be strongest amongst this group of countries.     

Decomposing the declining volatility of long-term inflation expectations

The level and volatility of survey-based measures of long-term inflation expectations have come down dramatically over the past several decades. To capture these changes in inflation dynamics, we embed both short- and long-term expectations into a medium-scale VAR model with stochastic volatility. The model estimates attribute most of the marked decline in the volatility of expectations to smaller shocks to long-run inflation expectations. According to our estimates, the volatility of shocks plummeted in the early to mid-1980s, moved to a somewhat higher level that prevailed for much of the 1990s, and then declined to and remained at very low levels.     

Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence, which results in standard Kalman filter techniques not being directly applicable. To overcome this hurdle, we develop an efficient posterior simulator that builds on recently developed precision-based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy, and the U.S.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Leading indicators of inflation in market economies

Inflation rates are cyclical in major market-oriented economies. Recently Geoffrey H. Moore and Stanley Kaish applied the well-known leading indicator approach to the development of a leading index of inflation cycles for the United States. Their index was based on measures of tightness in the labor market, and a measure of tightness in total credit markets, along with a measure of changes in industrial commodity prices. They found that this composite index reflects changes in inflation rate cycles reasonably well, and that it was more reliable than any of the three components taken alone. The present study broadens their study by attempting to duplicate the leading inflation index for forecasting changes in inflation rates in Canada, the United Kingdom, West Germany, France, Italy, and Japan. In general we find that the leading index is useful in anticipating changes in inflation rates in all these countries with the exception of France and Italy. As such we find that the forecasting properties of this index are often as promising in other countries as they have been in the U.S. Where they are not we conclude that there is a need for further research.     

News on inflation and the epidemiology of inflation expectations in China

Inflation expectations are important elements in monetary policy analysis. This paper examines how inflation expectations of Chinese consumers and professional forecasters are affected by media sentiments based on the epidemiological foundations of the sticky information model. Rather than assuming professional forecasts are identical to newspaper forecasts, we assume news media are a common source for the transmission of typical people's inflation expectations. We collect media data from 30 leading newspapers and magazines in China and code news reports into three types of inflation: rising, falling, and unchanged. More importantly, we categorize the media pool into comprehensive, economic, and politically oriented media sources. We find a fundamental connection between news media and inflation expectations. However, there are significantly different impacts of news reports in different media sources on expectations. The difference is mainly concentrated in politically oriented media sources, and may be a reflection of China's unique media administration system.     

Learning and time-varying macroeconomic volatility

This paper presents a DSGE model in which agents׳ learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors.The model is estimated using likelihood-based Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system.The estimation results show that private agents appear to have often switched to constant-gain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the so-called “Great Moderation” in the 1984–2007 period. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find time-varying volatility in the exogenous shocks, even when these have constant variance by construction.     

Foreign exchange market volatility in EU accession countries in the run-up to Euro adoption: weathering uncharted waters

The paper analyzes foreign exchange market volatility in four Central European EU accession countries in 2001–2003. By using a Markov regime-switching model, it identifies two regimes representing high- and low-volatility periods. The estimation results show not only that volatilities are different between the two regimes, but also that some of the cross-correlations differ. Notably, cross-correlations increase substantially for two pairs of currencies (the Hungarian forint–Polish zloty and the Czech koruna–Slovak koruna) in the high-volatility period. The paper concludes by discussing the policy implications of these findings.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.