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 high-frequency measures of volatility improve forecasts of return distributions?

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV)$log\left(RV\right)$ dynamics; the timing of information availability; and the assumed distributions of both return and log(RV)$log\left(RV\right)$ innovations. We find that a joint model of returns and volatility that features two components for log(RV)$log\left(RV\right)$ provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.     

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 policymakers need to cool the UK housing market?

The combination of government schemes and a recovery in the wider economy underpinned a robust pickup in housing transactions and house prices through 2013. But there is no evidence of a housing bubble across most of the country. Across the majority of regions prices are still below previous peaks in nominal terms and much lower still in real terms. Meanwhile measures of affordability and indebtedness are in a much better state than they were prior to the financial crisis. The exception is London, where supply shortages and strong demand have pushed both the price‐to‐income ratio and average income multiple back to previous highs. An improving macroeconomic backdrop and ongoing support from Help to Buy should ensure that demand continues to strengthen, supporting further growth in transactions. There has been a strong supply response over the past nine months and this should continue, which will help to keep a lid on price growth. Divergent macroeconomic prospects across the regions will lead to a wide variation in house price growth, with London expected to lead the way. We do not see a case for changing the terms of Help to Buy, particularly given that the most likely source of a bubble is London, where the impact of Help to Buy is likely to be small. In our view, the average income multiple is crucial and macro prudential tools should be used if it continues to rise above previous peaks in any regions. The most likely cause of a bubble at the national level would be an inadequate supply response. Alongside its policies to support demand, the government should implement a series of measures aimed at increasing supply, including planning reform, and it could also consider using its low borrowing costs to fund public sector house building.     

Does market attention affect Bitcoin returns and volatility?

Decisions in Economics and Finance - In this paper, we analyze the relative impact of attention measures either on the mean or on the variance of Bitcoin returns by fitting nonlinear econometric...     

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).     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

50 Years of research on organizing the purchasing function: Do we need any more?

Research on organizing the purchasing function has seen the rise and fall of different topics, for example the buying centre. And although the increasingly strategic role of purchasing still possesses practical challenges to its effective organizational setup, one has to question whether, after 50 years, there really is a need for more research in this specific domain.As starting point to answer the question, 212 articles published since 1962 are reviewed to provide a comprehensive overview of existing insights. Via content analysis, we inductively establish 12 specific research areas dealing with distinct aspects of purchasing organization.Then the need for further research is assessed by combining those findings with relevant business trends that induce practical challenges in each research area and, consequently, also the need for future research. We conclude that almost all research areas will remain relevant in the future, but need an adjustment of their focus.     

Are forecasting competitions data representative of the reality?

Forecasters typically evaluate the performances of new forecasting methods by exploiting data from past forecasting competitions. Over the years, numerous studies have based their conclusions on such datasets, with mis-performing methods being unlikely to receive any further attention. However, it has been reported that these datasets might not be indicative, as they display many limitations. Since forecasting research is driven somewhat by data from forecasting competitions, it becomes vital to determine whether they are indeed representative of the reality or whether forecasters tend to over-fit their methods on a random sample of series. This paper uses the data from M4 as proportionate to the real world and compares its properties with those of past datasets commonly used in the literature as benchmarks in order to provide evidence on that question. The results show that many popular benchmarks of the past may indeed deviate from reality, and ways forward are discussed in response.     

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 price limits hurt the market?

Under an artificial stock market composed of bounded-rational and heterogeneous traders, this paper examines whether or not price limits generate the negative effects on the market. Through testing the volatility spillover hypothesis, the delayed price discovery hypothesis, and the trading interference hypothesis, we find that no evidence of volatility spillover is observed. However, the phenomena of delayed price discovery and trading interference indeed exist, and their significance depends on the level of the price limits.     

Stock holdings over the life cycle: Who hesitates to join the market?

In this paper, we study the empirical relationship between age and individual wealth held in stocks, focusing on the heterogeneity of risk-taking over the life cycle in the population. We use micro-data and nonparametric quantile regression to argue that there is a pronounced life cycle pattern of risk-taking for households, which is conditional upon ownership. Specifically, we show that the fraction of stock investment decreases to bottom significantly in midlife and increases afterwards, contradicting the popular evidence claiming a hump-shaped pattern. The pressure of large financial obligations during middle age may be the reason for the crowding out of stock market risk-taking and could induce low capital returns for households.     

Forecasting stock market volatility: Can the risk aversion measure exert an important role?

In this paper, we predict realized volatility of stock return by utilizing time-varying risk aversion based on a simple linear autoregressive model. Our in-sample results suggest that time-varying risk aversion have significant impact for stock return volatility. In terms of out-of-sample forecasting performance, the empirical results indicate that the incorporation of time-varying risk aversion in the benchmark model can yield more accurate stock return volatility forecasts. Notably, the out-of-sample forecasting results confirm that our conclusions are robust when we apply alternative lag orders and alternative prediction evaluation periods. Finally, we study links between the prediction ability of time-varying risk aversion and the volatility of other stock indices and two kinds of crude oil, and find that the new predictor can effectively strengthen forecasting performance in most case. In view of the importance of volatility risk in the asset pricing process, our research is of great significance for financial asset participants.     

Are low-frequency data really uninformative? A forecasting combination perspective

In this study, we investigate whether low-frequency data improve volatility forecasting when high-frequency data are available. To answer this question, we utilize four forecast combination strategies that combine low-frequency and high-frequency volatility models and employ a rolling window and a range of loss functions in the framework of the novel Model Confidence Set test. Out-of-sample results show that combination forecasts with GARCH-class models can achieve high forecast accuracy. However, the combination forecast methods appear not to significantly outperform individual high-frequency volatility models. Furthermore, we find that models that combine low-frequency and high-frequency volatility yield significantly better performance than other models and combination forecast strategies in both a statistical and economic sense.     

Do we have all the necessary data? The challenge of measuring populism through metaphors

Quality & Quantity - The aim of this paper is to highlight one possible method how to recognize populism in political communication. The method synthesizes frameworks of content analysis,...     

Do precious metal prices help in forecasting South African inflation?

In this paper we test whether the key metals prices of gold and platinum significantly improve inflation forecasts for the South African economy. We also test whether controlling for conditional correlations in a dynamic setup, using bivariate Bayesian-Dynamic Conditional Correlation (B-DCC) models, improves inflation forecasts. To achieve this we compare out-of-sample forecast estimates of the B-DCC model to Random Walk, Autoregressive and Bayesian VAR models. We find that for both the BVAR and BDCC models, improving point forecasts of the Autoregressive model of inflation remains an elusive exercise. This, we argue, is of less importance relative to the more informative density forecasts. For this we find improved forecasts of inflation for the B-DCC models at all forecasting horizons tested. We thus conclude that including metals price series as inputs to inflation models leads to improved density forecasts, while controlling for the dynamic relationship between the included price series and inflation similarly leads to significantly improved density forecasts.     

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.     

Election forecasting: Too far out?

We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much “lead” still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the “optimal” lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US, and France.