Federal Reserve and private forecasts of growth in investment

This study examines the Federal Reserve and private forecasts of growth in both business and residential investment for 1983-2004 and reaches four main conclusions: First, in support of the asymmetric information hypothesis, the shorter (longer) horizon Federal Reserve forecasts of growth in business (residential) investment contain useful predictive information beyond that included in private forecasts. Second, the Federal Reserve forecasts are all biased. The same is true for the private forecasts of growth in residential (but not entirely for the forecasts of growth in business) investment. Third, the private forecasts overall do better than those of the Federal Reserve in outperforming the univariate forecasts. Fourth, the Federal Reserve and private forecasts of growth in business (residential) investment, while directionally accurate, imply symmetric (asymmetric) loss. We conclude this study by discussing the usefulness of these forecasts.     

Improving the Accuracy of Recent Survey Forecasts of the T-bill Rate

This study concentrates on the Survey of Professional Forecasters (SPF) to demonstrate a way to improve the consensus forecasts of interest rates. It promotes the notion that, in improving the survey forecast accuracy of a variable, one should investigate the usefulness of the predictive information contained in the survey forecasts of other theoretically relevant variables. This idea has been applied to the SPF forecasts of the 3-month Treasury-bill rate, which are shown to be one-sided for 2001.1-2003.4. We improve the accuracy of these forecasts by exploiting the predictive information contained in the SPF forecasts of inflation and output growth. We thus recommend that the possible improvement should be investigated before such interest rate forecasts are utilized for decision-making.JEL Classification C530     

Volatility forecasts embedded in the prices of crude-oil options

This paper evaluates the ability of alternative option-implied volatility measures to forecast crude-oil return volatility. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black–Scholes and model-free volatility expectations, as well as those generated by a realized volatility model. This measure ranks favorably in regression-based tests, delivers the lowest forecast errors under different loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the Chicago Board Options Exchange's “oil-VIX” index performs poorly, as it routinely produces the least accurate forecasts.     

Using Neural Nets to Forecast the Unemployment Rate

The paper identifies leading indicators of the unemployment rate. Forecasts of the unemployment rate are obtained with an econometric model, and with an artificial neural network. Both model-based forecasts outperform forecasts from the Survey of Professional Forecasters. This is important because the unemployment rate forecast from the Survey of Professional Forecasters has outperformed other forecasts based on time-series models to the point that some observers view it as a proxy for a full-information forecast. JEL Classification C450,E240     

Forecasting the Downturn of the Great Recession

We examine the quantitative forecasts of Wall Street Journal economists made during the Great Recession. The recession was not predicted in advance, and the severity of the decline was not recognized immediately. An important problem was that the real-time data did not reflect the actual state of the economy and contributed to the forecast errors. At times there was substantial disagreement among the forecasters. A time-series forecast disagreement might provide valuable information about impending recessions.     

Information content of volatility spreads

This study reexamines the determinants of volatility spreads and suggests a new forecast of future volatilities. Contrary to earlier volatility forecasts, the newly introduced forecast is applicable when investors are not risk‐neutral or when underlying returns do not follow a Gaussian probability distribution. This implies that the method is consistent with the presence of risk premia for other risks such as volatility risk. Using S&P 500 index options, we show that the new volatility forecast outperforms other volatility forecasts including risk‐neutral implied volatility and historical volatility in two aspects. First, the new forecast is superior to other estimates in terms of forecasting errors for future realized volatilities. Second, it is an unbiased estimator of future realized volatilities. This is shown using an encompassing regression analysis. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:533–558, 2010     

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:339–373, 2005     

Subjective Probability Forecasts for Recessions

Probabilistic forecasts are often more useful in business than point forecasts. In this paper, the joint subjective probabilities for negative GDP growth during the next two quarters obtained from the Survey of Professional Forecasters (SPF) are evaluated using various decompositions of the Quadratic Probability Score (QPS). Using the odds ratio and other forecasting accuracy scores appropriate for rare event forecasting, we find that the forecasts have statistically significant accuracy. However, compared to their discriminatory power, these forecasts have excess variability that is caused by relatively low assigned probabilities to forthcoming recessions. We suggest simple guidelines for the use of probability forecasts in practice. JEL Classification E32,E37     

Managerial intervention in forecasting. An empirical investigation of forecast manipulation

There has been a continuing debate in the forecasting literature concerning the relative advantages of quantitative versus qualitative approaches to forecasting. However, although a considerable number of studies have contrasted the merits of the two approaches, relatively few efforts have investigated the application of human judgement on forecasts generated by quantitative forecasting models. This study is an empirical investigation of the effects of human intervention on forecast accuracy. It examines the consequences of managerial manipulation of sales forecasts initially produced by a smoothing model. A total of 281 products are investigated using multiple measures of forecast accuracy and the effects of subjective revision are discussed in terms of size, directionality, and distribution of errors. The results indicate that human intervention can lead to an overall improvement in forecast performance as reflected in a reduction in the variability and absolute size of forecasting errors. The results also show that more forecasts are improved by manipulation than are degraded. Finally, there is some indication that subjective revision may result in an increase in overall forecasting bias. These findings appear to conflict with previous evidence, however, the empirical setting of the present study is specific to shortterm sales forecasting which is not strictly comparable with the settings of earlier efforts.     

The quality of volatility traded on the over‐the‐counter currency market: A multiple horizons study

Previous studies of the quality of market‐forecasted volatility have used the volatility that is implied by exchange‐traded option prices. The use of implied volatility in estimating the market view of future volatility has suffered from variable measurement errors, such as the non‐synchronization of option and underlying asset prices, the expiration‐day effect, and the volatility smile effect. This study circumvents these problems by using the quoted implied volatility from the over‐the‐counter (OTC) currency option market, in which traders quote prices in terms of volatility. Furthermore, the OTC currency options have daily quotes for standard maturities, which allows the study to look at the market's ability to forecast future volatility for different horizons. The study finds that quoted implied volatility subsumes the information content of historically based forecasts at shorter horizons, and the former is as good as the latter at longer horizons. These results are consistent with the argument that measurement errors have a substantial effect on the implied volatility estimator and the quality of the inferences that are based on it. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:261–285, 2003     

Realized Volatility Forecast: Structural Breaks,Long Memory,Asymmetry, and Day‐of‐the‐Week Effect

We investigate the properties of the realized volatility in Chinese stock markets by employing the high‐frequency data of Shanghai Stock Exchange Composite Index and four individual stocks from Shanghai Stock Exchange and Shenzhen Stock Exchange, and find that the volatility exhibits the properties of long‐term memory, structural breaks, asymmetry, and day‐of‐the‐week effect. In addition, the structural breaks only partially explain the long memory. To capture these properties simultaneously, we derive an adaptive asymmetry heterogeneous autoregressive model with day‐of‐the‐week effect and fractionally integrated generalized autoregressive conditional heteroskedasticity errors (HAR‐D‐FIGARCH) and use it to conduct a forecast of realized volatility. Compared with other heterogeneous autoregressive realized volatility models, the proposed model improves the in‐sample fit significantly. The proposed model is the best model for the day‐ahead realized volatility forecasts among the six models based on various loss functions by utilizing the superior predictive ability test.     

Foreign‐Exchange Trading Volume and Federal Reserve Intervention

We find a large positive correlation between daily trading volume in currency futures markets and foreign‐exchange intervention by the Federal Reserve over the period 1979 to 1996. Neither contemporaneous nor predicted volatility can fully account for the increases in trading activity. Whether or not the intervention operation is publicly reported appears to be an important determinant of trading volume. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:851?860, 2001     

Volatility term structures in commodity markets

In this study, we comprehensively examine the volatility term structures in commodity markets. We model state-dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a substantial interconnectedness of the volatility term structures of commodities. Accounting for intra-commodity-market spillovers significantly improves out-of-sample forecasts of the components of the volatility term structure. Spillovers following macroeconomic news announcements account for a large proportion of this forecast power. There thus seems to be substantial information transmission between different commodity markets.     

Varying monetary policy regimes: A vector autoregressive investigation

Recently, two stylized facts about the behavior of the U.S. economy have emerged: first, macroeconomic aggregates appear to be less volatile post-1984 than in the preceding 2 decades; second, monetary policy appears more responsive to inflationary pressures – and thereby more “stabilizing” – during the Volcker/Greenspan chairmanships relative to earlier regimes. Does a causal relationship exist between these two observations? In particular, has “better” policy by the Federal Reserve Board contributed significantly to the lessened volatility of the U.S. economy? This paper uses a structural vector autoregressive (VAR) specification to address these questions, examining the advantages and limitations of such an approach. In contrast with much of the existing research on these topics, I find that most of the quantitatively significant changes in volatility are attributed to breaks in the non-policy portion of the structural VAR, and not to the identified policy equation.     

Top‐Down Versus Bottom‐Up Demand Forecasts: The Value of Shared Point‐of‐Sale Data in the Retail Supply Chain

Accurate demand forecasts are critical to maintaining customer service levels and minimizing total costs, yet increasingly difficult to achieve. Using weekly point‐of‐sale (POS) and order data for 10 ready‐to‐eat cereal stock‐keeping units from 18 regional U.S. grocery distribution centers, this research empirically investigates two demand forecasting issues: (1) the accuracy of top‐down versus bottom‐up demand forecasts; and (2) whether shared POS data improve demand forecast accuracy. The results reveal a previously unexplored relationship between demand forecast methodology and the use of shared POS data. We find that the superiority of the top‐down or bottom‐up forecasting as the more accurate demand forecast method depends on whether shared POS data are used.     

AN examination of short QQQ option trades

This study shows that unconditional QQQ option selling strategies from January 2001 through November 2004 are generally significantly profitable after transactions costs. However, when straddle and strangle sales are combined with purchases of out of the money puts, few of the strategies are significantly profitable. Profits improve when the QQQ Volatility Index is high relative to time series volatility forecasts, but only when actual volatility is forecast to be moderate. Active delta‐hedging reduces profitability, whereas stop loss/take profit orders enhance profitability. Overall, QQQ short volatility trades appear to be less compelling than what others have found with S&P options. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:739–770, 2007     

Useful Properties of Exchange Rate Forecasts for Risk Management and Derivative Pricing

We examine three useful properties of the yen/dollar exchange‐rate forecasts that are published in January and July in the Wall Street Journal. Those properties are the level of explanatory power, whether some forecasters are consistently better than others, and whether the dispersion of forecasts can predict the volatility of the exchange rates. The results show that the relative accuracy of the individual forecasts has not been random each period, and the evidence suggests that some forecasters are consistently better than others. The forecasts from the best forecasters explain about half of the variability in the semiannual exchange‐rate series. Finally, the dispersion of all the forecasts each period, as measured by the standard deviation, has predictive power with respect to the daily volatility of the forecasts for the three months following the survey. This final property has implications for the pricing and use of currency options. © 2001 John Wiley & Sons, Inc.     

Return distributions and volatility forecasting in metal futures markets: Evidence from gold,silver, and copper

The characterization of return distributions and forecast of asset‐price variability play a critical role in the study of financial markets. This study estimates four measures of integrated volatility—daily absolute returns, realized volatility, realized bipower volatility, and integrated volatility via Fourier transformation (IVFT)—for gold, silver, and copper by using high‐frequency data for the period 1999 through 2008. The distributional properties are investigated by applying recently developed jump detection procedures and by constructing financial‐time return series. The predictive ability of a GARCH (1,1) forecasting model that uses various volatility measures is also examined. Three important findings are reported. First, the magnitude of the IVFT volatility estimate is the greatest among the four volatility measures. Second, the return distributions of the three markets are not normal. However, when returns are standardized by IVFT and realized volatility, the corresponding return distributions bear closer resemblance to a normal distribution. Notably, the application of financial‐time sampling technique is helpful in obtaining a normal distribution. Finally, the IVFT and realized volatility proxies produce the smallest forecasting errors, and increasing the time frequency of estimating integrated volatility does not necessarily improve forecast accuracy. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:55–80, 2011     

Forecasting bitcoin volatility: Evidence from the options market

This paper studies a large number of bitcoin (BTC) options traded on the options exchange Deribit. We use the trades to calculate implied volatility (IV) and analyze if volatility forecasts can be improved using such information. IV is less accurate than AutoRegressive–Moving-Average or Heterogeneous Auto-Regressive model forecasts in predicting short-term BTC volatility (1 day ahead), but superior in predicting long-term volatility (7, 10, 15 days ahead). Furthermore, a combination of IV and model-based forecasts provides the highest accuracy for all forecasting horizons revealing that the BTC options market contains unique information.     

USER INFLUENCE ON THE RELATIONSHIP BETWEEN FORECAST ACCURACY,APPLICATION AND LOGISTICS PERFORMANCE

When companies develop and apply more accurate forecasts in their planning and management activities, they have the potential to improve performance throughout their organization and across the supply chain. To realize these improvements, however, companies must (1) implement techniques and practices that improve forecast accuracy, and (2) integrate the more accurate forecasts into their planning and management activities. While much research has focused on accomplishing the first of these requirements, few studies have investigated the important role that user's play in the application of forecasts for logistics planning and management. This article establishes a connection between forecast performance, user perceptions of the quality of forecasts they receive, the extent that they use the forecasts and the resulting impact on logistics performance.