Does the option market produce superior forecasts of noise‐corrected volatility measures?

This paper assesses the robustness of the relative performance of spot‐ and options‐based volatility forecasts to the treatment of microstructure noise. Robustness of the results to the method of constructing option‐implied forecasts is also investigated. Using a test for superior predictive ability, model‐free implied volatility, which exploits information in the volatility ‘smile’, and at‐the‐money implied volatility, which does not, are both tested as benchmark forecasts of a range of alternative volatility proxies. The results provide compelling evidence against the model‐free forecast for three Dow Jones Industrial Average stocks, over a 2001–2006 evaluation period. In contrast, the at‐the‐money implied volatility forecast is given strong support for the three equities over this period. Neither benchmark is supported for the S&P500 index. Importantly, the main qualitative results are invariant to the method of noise correction used in measuring future volatility. Copyright © 2008 John Wiley & Sons, Ltd.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Can we improve the perceived quality of economic forecasts?

A number of topics are discussed concerning how economic forecasts can be improved in quality or at least in presentation. These include the following: using 50% uncertainty intervals rather than 95%; noting that even though forecasters use many different techniques, they are all occasionally incorrect in the same direction; that there is a tendency to underestimate changes; that some expectations and recently available data are used insufficiently; lagged forecasts errors can help compensate for structural breaks; series that are more forecastable could be emphasized and that present methods of evaluating forecasts do not capture the useful properties of some methods compared to alternatives.     

Does judgment improve macroeconomic density forecasts?

This paper presents empirical evidence on how judgmental adjustments affect the accuracy of macroeconomic density forecasts. Judgment is defined as the difference between professional forecasters’ densities and the forecast densities from statistical models. Using entropic tilting, we evaluate whether judgments about the mean, variance and skew improve the accuracy of density forecasts for UK output growth and inflation. We find that not all judgmental adjustments help. Judgments about point forecasts tend to improve density forecast accuracy at short horizons and at times of heightened macroeconomic uncertainty. Judgments about the variance hinder at short horizons, but can improve tail risk forecasts at longer horizons. Judgments about skew in general take value away, with gains seen only for longer horizon output growth forecasts when statistical models took longer to learn that downside risks had reduced with the end of the Great Recession. Overall, density forecasts from statistical models prove hard to beat.     

How does inter-industry spillover improve the performance of volatility forecasting?

This study aims to investigate whether introducing inter-industry spillover information into the GARCH-MIDAS model improves out-of-sample forecasting attempts. We explore the transmission of volatility across sectors, as well as the reliance on inter-industry business links. Our findings demonstrate strong cross-industry volatility spillovers that are related to the degree of the industry-to-industry trading linkage. We compare the out-of-sample volatility forecasting performance of the spillovers-information-incorporated GARCH-MIDAS model with that of the traditional GARCH model. The empirical results show that the GARCH-MIDAS model outperforms traditional GARCH models. Notably, we discover that good (bad) news is always transferred from the back end of the production process to the front end, meaning that economic growth (decline) is driven by consumption expansion (shrinkage).     

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

We consider whether survey respondents’ probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.     

Do voluntary disclosures of bad news improve liquidity?

Can managers improve market liquidity and lower the cost of capital by providing voluntary earnings guidance? This study examines the impact of profit warnings on market liquidity and finds that voluntary disclosure of bad news actually improves market liquidity. By conducting an empirical study over the period 1995–2010 on NYSE, NASDAQ and AMEX listed firms, we find that firms that issue profit warnings show enhanced market liquidity during the post-announcement period. We show that profit warnings reduce information asymmetry and lower bid-ask spreads and increase trading volumes. These results are invariant to daily (short run) and monthly (long run) data after controlling for firm specific attributes. The results have major corporate policy implications. By voluntarily disclosing negative earnings guidance by managers, firms will experience significant improvement in market liquidity, thereby lowering the cost of capital. Our results are even more profound for firms that release bad news with extremely negative stock market impact. In other words, voluntary disclosure of bad news is good for market liquidity.     

Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?

Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV.     

Do salaries improve worker performance?

We establish the effects of salaries on worker performance by exploiting a natural experiment in which some workers in a particular occupation (football referees) switch from short-term contracts to salaried contracts. Worker performance improves among those who move onto salaried contracts relative to those who do not. The finding is robust to the introduction of worker fixed effects indicating that it is not driven by better workers being awarded salary contracts. Nor is it sensitive to workers sorting into or out of the profession. Improved performance could arise from the additional effort workers exert due to career concerns, the higher income associated with career contracts (an efficiency wage effect) or improvements in worker quality arising from off-the-job training which accompanies the salaried contracts.     

Forecasting exchange rate volatility using high-frequency data: Is the euro different?

We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.     

Do good institutions improve fiscal transparency?

We study the role of institutional quality on the fiscal transparency of a country. We show that such a link does exist even when controlling for endogeneity. Our findings are robust to changes in specification and a host of transparency sub-measures. An advantage of our study is that we use new data on fiscal transparency for a cross-section of 82 countries, which are based on in-depth reports based on a standardized methodology and protocol. Furthermore, the fiscal measures were obtained with the collaboration of government authorities, which makes them particularly reliable.     

Which implied volatility provides the best measure of future volatility?

The volatility smile/skew phenomenon makes it unclear which implied volatility provides the best measure of the market volatility expectation over the remaining life of the option. Due to the high liquidity of at-the-money option and the low sensitivity of its implied volatility to the price error, the at-the-money implied volatility is often considered a good measure of future volatility. In this paper, we raise the question: is at-the-money implied volatility the best we can do? We provide in this paper an analytical rationale that the implied volatility from option with highest vega outperforms the at-the-money implied volatility in terms of forecasting ability, especially for long forecasting horizons. Our empirical findings are consistent with our theoretical argument.     

Moments structure of ? 1-stochastic volatility models

We consider Taylor??s stochastic volatility model (SVM) when the innovations of the hidden log-volatility process have a Laplace distribution (? ₁ exponential density), rather than the standard Gaussian distribution (? ₂) usually employed. Recently many investigations have employed ? ₁ metric to allow better modeling of the abrupt changes of regime observed in financial time series. However, the estimation of SVM is known to be difficult because it is a non-linear with an hidden markov process. Moreover, an additional difficulty yielded by the use of ? ₁ metric is the not differentiability of the likelihood function. An alternative consists in using a generalized or efficient method-of-moments (GMM/EMM) estimation. For this purpose, we derive here the moments and autocovariance function of such ? ₁-based stochastic volatility models.     

Inflation forecasts and core inflation measures: Where is the information on future inflation?

This paper uses the forecast from a random walk model of inflation as a benchmark to test and compare the forecast performance of several alternatives of future inflation, including the Greenbook forecast by the Fed staff, the Survey of Professional Forecasters median forecast, CPI inflation minus food and energy, CPI weighted median inflation, and CPI trimmed mean inflation. The Greenbook forecast was found in previous literature to be a better forecast than other private sector forecasts. Our results indicate that both the Greenbook and the Survey of Professional Forecasters median forecasts of inflation and core inflation measures may contain better information than forecasts from a random walk model. The Greenbook's superiority appears to have declined against other forecasts and core inflation measures.     

Do individual disclosure rules for parliamentarians improve government effectiveness?

The pros and cons of stricter disclosure rules for parliamentarians are hotly debated. Some argue that disclosure rules for parliamentarians increase transparency of the legislative branch, leading to lower levels of rent-seeking and corruption, increased citizen trust in parliament, and better quality of law-making. Others argue that disclosure rules endanger the privacy of parliamentarians, that their introduction would stop businesspeople and lawyers from running for seats, which would decrease the quality of law-making. This is the first attempt to empirically test these conjectures on the composition of parliament empirically. We find that the introduction of disclosure rules is usually not accompanied by a significant shift in the proportion of lawyers and businesspeople in parliament.     

Do China's anti-corruption efforts improve corporate productivity? A difference-in-difference exploration of Chinese listed enterprises

Corruption affects corporate investment and diverts resources away from growth-improving factors, including R&D activities and human capital, thereby lowering productivity. Using a time-varying difference-in-differences approach, we identified the causal effect of China's anti-corruption campaign on corporate productivity during 2011–2021. The findings uncovered that China's anti-corruption campaign increased corporate productivity by approximately 18.43%. Results from heterogeneity analysis showed that the promoting effect was particularly significant in non-state-owned firms, firms without political ties, and firms in areas with weak legal systems. Additional mechanism analysis revealed that firm productivity could be significantly boosted by improving resource allocation efficiency and advancing technological innovation.     

How accurate are government forecasts of economic fundamentals? The case of Taiwan

A government’s ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate.In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.     

Do IMF forecasts respect Okun’s law? Evidence for advanced and developing economies

This paper provides an assessment of the IMF’s unemployment forecasts, which have not received much scrutiny to date. The focus is on the internal consistency of the IMF’s growth and unemployment forecasts, and specifically on seeing whether the relationship between the two is consistent with the relationship in the data, i.e., with Okun’s Law. We find that the average performance is good, in the sense that the relationship between growth and unemployment forecasts is fairly comparable to that which prevails in the data: on average, the Okun coefficient in the forecasts mirrors the Okun coefficient in the data. Nevertheless, there is room for improvement, particularly in the year-ahead forecasts and for the group of middle-income countries. We show that a linear combination of Okun-based unemployment forecasts and WEO unemployment forecasts can deliver significant gains in forecast accuracy for developing economies.     

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.     

Do environmental,social, and governance activities improve corporate financial performance?

This study investigated the relationship between corporate efficiency and corporate sustainability to determine whether firms concerned about environmental, social, and governance (ESG) issues can also be efficient and profitable. We applied data envelopment analysis to estimate corporate efficiency and investigated the nonlinear relationship between corporate efficiency and ESG disclosure. Evidence shows that corporate transparency regarding ESG information has a positive association with corporate efficiency at the moderate disclosure level, rather than at the high or low disclosure level. Governance information disclosure has the strongest positive linkage with corporate efficiency, followed by social and environmental information disclosure. Moreover, we explored the relationship between particular ESG activities and corporate financial performance (CFP), including corporate efficiency, return on assets, and market value. We found that most of the ESG activities reveal a nonnegative relationship with CFP. These findings may provide evidence about voluntary corporate social responsibility strategy choices for enhancing corporate sustainability.