scholarly journals Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex

2019 ◽  
Vol 47 (1) ◽  
pp. 178-199 ◽  
Author(s):  
Joshua Huang ◽  
Teresa Serra ◽  
Philip Garcia
Keyword(s):  
Quantile Regression ◽  
Forecast Errors ◽  
Central Tendency ◽  
Futures Prices ◽  
Forecast Performance ◽  
Market Participants ◽  
Price Distribution ◽  
Forecasting Performance ◽  
Futures Price ◽  
Good Price

Abstract Using quantile regression, we evaluate the forecasting performance of futures prices in the soybean complex. The procedure provides a more complete picture of the distribution of forecasts than mainstream methods that only focus on central tendency measures. Forecast performance differs by location in the futures price distribution. Futures forecast perform well in the centre of the distribution. However, futures prices tend to over-forecast when futures prices are high and under-forecast when futures prices are low, suggesting that futures prices tend to under-estimate price reversion towards the centre of the distribution. Forecast errors are larger when futures prices are high. The findings are related to theories in the literature used to explain pricing bias, and their implications for market participants are discussed.

scholarly journals Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

2015 ◽  
Vol 19 (9) ◽  
pp. 3969-3990 ◽  
Author(s):  
F. Hoss ◽  
P. S. Fischbeck
Keyword(s):  
Quantile Regression ◽  
High Reliability ◽  
Forecast Error ◽  
Skill Score ◽  
Water Levels ◽  
Training Data ◽  
Forecast Errors ◽  
Data Set ◽  
Forecast Performance ◽  
Independent Variables

Abstract. This study applies quantile regression (QR) to predict exceedance probabilities of various water levels, including flood stages, with combinations of deterministic forecasts, past forecast errors and rates of water level rise as independent variables. A computationally cheap technique to estimate forecast uncertainty is valuable, because many national flood forecasting services, such as the National Weather Service (NWS), only publish deterministic single-valued forecasts. The study uses data from the 82 river gauges, for which the NWS' North Central River Forecast Center issues forecasts daily. Archived forecasts for lead times of up to 6 days from 2001 to 2013 were analyzed. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the continuous ranked probability score. Mainly, the resolution increases, as the forecast-only QR configuration already delivered high reliability. Combining the forecast with the other four predictors results in a much less favorable performance. Lastly, the forecast performance does not strongly depend on the size of the training data set but on the year, the river gauge, lead time and event threshold that are being forecast. We find that each event threshold requires a separate configuration or at least calibration.

scholarly journals Forecasting Performance Of Natural Gas Futures Market: An Assessment Of Recent Data

2011 ◽  
Vol 4 (11) ◽  
Author(s):  
Salah Abosedra ◽  
Khaled Elkhal ◽  
Faisal Al-Khateeb
Keyword(s):  
Natural Gas ◽  
Futures Market ◽  
Policy Implications ◽  
Energy Market ◽  
Futures Prices ◽  
Font Size ◽  
Spot Prices ◽  
Forecasting Performance ◽  
Futures Price ◽  
The U.S

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 1in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Natural gas has assumed increasing importance in the global energy market. This study evaluates the forecasting performance of futures prices of natural gas in the large market of the U.S. at various time horizons. The results indicate that futures prices are unbiased predictors at the 1-, 6-, and 12- month horizons, but not at the 3- and 9- month horizons. The results further suggest that futures prices of natural gas, although biased at some intervals, significantly outperform na&iuml;ve forecasts in predicting future movements of spot prices. In addition, the information content of the 1-month ahead futures price proves especially useful as a forecasting device. Policy implications are also discussed.<span style="mso-bidi-font-style: italic; mso-bidi-font-weight: bold;"></span></span></span></p>

scholarly journals Surface high-resolution temperature forecast in southern Italy

2011 ◽  
Vol 6 (1) ◽  
pp. 211-217
Author(s):  
S. Federico ◽  
E. Avolio ◽  
F. Fusto ◽  
R. Niccoli ◽  
C. Bellecci
Keyword(s):  
Southern Italy ◽  
Background Field ◽  
Horizontal Resolution ◽  
Optimal Interpolation ◽  
Maximum Temperature ◽  
Forecast Errors ◽  
Field Observations ◽  
Forecast Performance ◽  
Temperature Forecast ◽  
Analysis System

Abstract. Since June 2008, 1-h temperature forecasts for the Calabria region (Southern Italy) are issued at 2.5 km horizontal resolution at CRATI/ISAC-CNR. Forecasts are available online at http://meteo.crati.it/previsioni.html (every 6-h). This paper shows the forecast performance out to three days for one climatological year (from 1 December 2008 to 30 November 2009, 365 run) for minimum, mean and maximum temperature. The forecast is evaluated against gridded analyses at the same horizontal resolution. Gridded analysis is based on Optimal Interpolation (OI) and uses a de-trending technique for computing the background field. Observations from 87 thermometers are used in the analysis system. In this paper cumulative statistics are shown to quantify forecast errors out to three days.

scholarly journals Livestock Gross Margin–Dairy: An Assessment of Its Effectiveness as a Risk Management Tool and Its Potential to Induce Supply Expansion

2014 ◽  
Vol 46 (2) ◽  
pp. 245-256
Author(s):  
Kenneth H. Burdine ◽  
Yoko Kusunose ◽  
Leigh J. Maynard ◽  
Don P. Blayney ◽  
Roberto Mosheim
Keyword(s):  
Risk Management ◽  
Management Tool ◽  
Futures Prices ◽  
Gross Margin ◽  
Insurance Program ◽  
Multiple Regions ◽  
Estimated Risk ◽  
Futures Price ◽  
Supply Analysis ◽  
Risk Reducing

An evaluation of the risk-reducing effectiveness of the Livestock Gross Margin–Dairy (LGM-Dairy) insurance program, using historical futures price data, predicts economically significant reductions in downside margin risk (24–41%) across multiple regions. Supply analysis based on the estimated risk reduction shows a small supply response, assuming minimal subsidization. A decomposition of the simulated indemnities into milk price and feed price components shows comovements in futures prices moderating the frequency and levels of indemnities.

scholarly journals Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression

2020 ◽  
Author(s):  
Hui Tian ◽  
Andrew Yim ◽  
David P. Newton
Keyword(s):  
Quantile Regression ◽  
Forecast Error ◽  
Simulated Data ◽  
Ordinary Least Squares ◽  
Shape Effect ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Cash Flow Forecasting ◽  
Positive Effect ◽  
Better Than

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry. This paper was accepted by Shiva Rajgopal, accounting.

scholarly journals Coordinating expectations through central bank projections

2020 ◽  
Author(s):  
Fatemeh Mokhtarzadeh ◽  
Luba Petersen
Keyword(s):  
Central Bank ◽  
Rational Expectations ◽  
Inflation Targeting ◽  
Aggregate Stability ◽  
Central Banks ◽  
Output Gap ◽  
Forecast Errors ◽  
Market Participants ◽  
Wide Range ◽  
Ex Ante

AbstractCentral banks are increasingly communicating their economic outlook in an effort to manage the public and financial market participants’ expectations. We provide original causal evidence that the information communicated and the assumptions underlying a central bank’s projection can matter for expectation formation and aggregate stability. Using a between-subject design, we systematically vary the central bank’s projected forecasts in an experimental macroeconomy where subjects are incentivized to forecast the output gap and inflation. Without projections, subjects exhibit a wide range of heuristics, with the modal heuristic involving a significant backward-looking component. Ex-Ante Rational dual projections of the output gap and inflation significantly reduce the number of subjects’ using backward-looking heuristics and nudge expectations in the direction of the rational expectations equilibrium. Ex-Ante Rational interest rate projections are cognitively challenging to employ and have limited effects on the distribution of heuristics. Adaptive dual projections generate unintended inflation volatility by inducing boundedly-rational forecasters to employ the projection and model-consistent forecasters to utilize the projection as a proxy for aggregate expectations. All projections reduce output gap disagreement but increase inflation disagreement. Central bank credibility is significantly diminished when the central bank makes larger forecast errors when communicating a relatively more complex projection. Our findings suggest that inflation-targeting central banks should strategically ignore agents’ irrationalities when constructing their projections and communicate easy-to-process information.

Predictive Ability of Business Cycle Indicators under Test

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Kai Carstensen ◽  
Klaus Wohlrabe ◽  
Christina Ziegler
Keyword(s):  
Predictive Ability ◽  
Optimal Choice ◽  
Good Choice ◽  
Economic Stress ◽  
Forecast Errors ◽  
Business Climate ◽  
Forecast Performance ◽  
Leading Indicator ◽  
Forecast Models ◽  
Climate Indicator

SummaryIn this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check-whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that none of this indicators uniformly dominates all its competitors. The optimal choice rather depends on the specific forecast situation and the loss function of the user. For 1-month forecasts the business climate indicator of the European Commission and the OECD composite leading indicator generally work well, for 6-month forecasts the OECD composite leading indicator performs very good by all criteria, and for 12-month forecasts the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung is the only one that can beat the benchmark AR(1) model.

Modeling of Wind Power Production Forecast Errors for Wind Integration Studies

2010 ◽  
Author(s):  
Jason J. Kemper ◽  
Mark F. Bielecki ◽  
Thomas L. Acker
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Power Plants ◽  
Moving Average ◽  
Forecast Errors ◽  
Production Forecast ◽  
Wind Integration ◽  
Forecast Performance ◽  
Auto Regressive ◽  
Statistical Trends

In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.

Underpricing of Index Futures prices, Prospect Theory and Intraday Momentum

2013 ◽  
Vol 21 (1) ◽  
pp. 71-96
Author(s):  
Suhkyong Kim
Keyword(s):  
Prospect Theory ◽  
Large Body ◽  
Futures Prices ◽  
Momentum Effect ◽  
Index Futures ◽  
Risk Seeking ◽  
Intraday Data ◽  
Futures Price ◽  
The Difference ◽  
Shaped Pattern

We investigate intraday data for KOSPI 200 index and KOSPI 200 index futures. Hourly theoretical futures prices are calculated based on cost of carry model. we compare hourly index futures prices with their theoretical prices. Consistent with a large body of previous researches in this area, we find the persistent deviation of futures prices from their theoretical prices. Futures prices are undervalued relative to their theoretical prices. The data indicate that the difference between futures price and its theoretical price exhibits U-shaped pattern over the trading hours. The differences are higher at open and at 15:00 and are lower over intraday trading hours, implying that previous studies using daily closing prices overstate this mispricing. We also examine the effect of intraday spot return on the behavior of the difference between the hourly futures price and its theoretical price. The finding indicates that the intraday momentum generates U-shaped pattern of this mispricing. This contrasts with Kim and Park (2011)'s finding that the difference also increases as the prior 60 day spot return increases. Our finding invalidates their explanation the activities of arbitrageurs bring monotonic increasing pattern of the magnitude of this mispricing in their daily data. We propose a new explanation the U shaped patttern of the difference between the futures price and its theoretical price generated by the intraday spot return's moment. We introduce risk-seeking trader in our new explanation. The trader's risk-seeking behavior is based on prospect theory (Kahneman and Tversky (1979)). We argue that the risk-seeking traders cause intraday momentum effect to generate the U-shaped pattern of this mispricing. We add speculator's variables to Kim and Park (2011)'s regression equation and estimate it. The results from the regression analysis lend support to our new explanation as well as theirs, implying that speculators and arbitrageurs are present and active in the spot and futures markets and generate different pattern of the mispricing.

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