The Forecast Quality of CBOE Implied Volatility Indexes

2003 ◽  
Author(s):  
Charles J. Corrado ◽  
Thomas W. Miller
Keyword(s):  
Implied Volatility ◽  
Forecast Quality

The forecast quality of CBOE implied volatility indexes

2005 ◽  
Vol 25 (4) ◽  
pp. 339-373 ◽  
Author(s):  
Charles J. Corrado ◽  
Thomas W. Miller, Jr.
Keyword(s):  
Implied Volatility ◽  
Forecast Quality

Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?

2020 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Thomas Bosshard
Keyword(s):  
A Priori ◽  
Hydrological Regime ◽  
Climatic Conditions ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Key Drivers ◽  
Seasonal Streamflow ◽  
Forecast Quality

<p>Streamflow information for the months ahead is of great value to existing decision-making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate-related risks. Despite the large effort, there is still limited knowledge of the key drivers controlling the quality of the seasonal streamflow forecasts. In this investigation, we show that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro-climatic conditions. To reach these conclusions we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which vary in terms of climatology, scale and hydrological regime. We then link the forecast quality to various descriptors including physiography, hydro-climatic characteristics and meteorological biases. This allows the identification of the key drivers along a strong hydro-climatic gradient. Results show that, as expected, the seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. In addition, the predictability deteriorates with increasing lead months particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of drivers, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin’s hydrological regime, with quickly reacting basins (of low river memory) showing limited predictability. On the contrary, snow and/or baseflow dominated regions with long recessions (and hence high river memory) show high streamflow predictability. Finally, climatology and precipitation biases are also strongly related to streamflow predictability, highlighting the importance of developing robust bias-adjustment methods.</p>

scholarly journals Verification as a service to bring more transparency on forecast accuracy to weather services and users

2021 ◽  
Author(s):  
Evelyn Müller ◽  
Jan Hoffmann ◽  
Dennis Schulze
Keyword(s):  
Forecast Accuracy ◽  
Cost Benefit ◽  
Forecast Horizon ◽  
Weather Forecasts ◽  
Industry Applications ◽  
Cost Benefit Ratio ◽  
The Cost ◽  
Available Information ◽  
Forecast Quality

<p>Actual, continuously available information on the accuracy of forecasts can support both weather services and users of forecasts in quality assurance during operations and identify systematic weaknesses. Comparing the forecast success of different forecasting methods allows decision makers in the weather service and on the user side to evaluate the cost-benefit ratio of available forecasting approaches, be it different models, DMO and post-processing, or different providers. Finally, in addition to on-off experiments for version comparison, the success of developments to the forecast system can be seen in the comparison of time series of verification results against those of other forecasts. </p><p>From the development of the forecasting process to daily operations to the use of forecasts in subsequent industry applications, stakeholders have very different questions about the quality of weather forecasts. From the weather room, there is a particular need for up-to-date information on the previous day's forecast success and rapid access to case verification analyses following unusual events. Especially in B2B, case-specific comparison with the success of other forecasts is also in demand. For management, on the other hand, longer-term trends in forecast quality are the focus of interest. Finally, users often base their choice of a forecasting provider not only on procurement costs and convenience of access, but also take into account the current forecast accuracy of their relevant parameters, in their region, in the forecast horizon relevant to them. Especially weather-sensitive industries such as road weather services, energy production and transmission, but also media often agree with forecast suppliers on continuous monitoring of forecast quality. </p><p>We present different perspectives and questions and show possible answers as use cases in a verification portal.</p>

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals Dependence of Predictability of Precipitation in the Northwestern Mediterranean Coastal Region on the Strength of Synoptic Control

2020 ◽  
Author(s):  
Christian Keil ◽  
Lucie Chabert ◽  
Olivier Nuissier ◽  
Laure Raynaud
Keyword(s):  
Coastal Region ◽  
Heavy Precipitation ◽  
Forecast Model ◽  
Precipitation Event ◽  
Heavy Precipitation Event ◽  
Precipitation Characteristics ◽  
Synoptic Forcing ◽  
Convective Adjustment ◽  
Forecast Quality

Abstract. The weather regime dependent predictability of precipitation in the convection permitting kilometric scale AROME-EPS is examined for the entire HyMeX SOP1 employing the convective adjustment timescale. This diagnostic quantifies variations in synoptic forcing on precipitation and is associated with different precipitation characteristics, forecast skill and predictability. During strong synoptic control, which is dominating the weather on 80 % of the days in the 2-months period, the domain integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of most intense precipitation in the afternoon, as quantified with its 95th percentiles, is superior during weakly forced synoptic regimes. The study also considers a prominent heavy precipitation event that occurred during the NAWDEX field campaign in the same region, and the predictability during this event is compared with the events that occurred during HyMeX. It is shown that the unconditional evaluation of precipitation widely parallels the strongly forced weather type evaluation and obscures forecast model characteristics typical for weak control.

Recommendation-Forecast Consistency and Earnings Forecast Quality

2013 ◽  
Vol 27 (3) ◽  
pp. 451-467 ◽  
Author(s):  
Lawrence D. Brown ◽  
Kelly Huang
Keyword(s):  
Earnings Forecasts ◽  
Firm Valuation ◽  
Data Availability ◽  
Earnings Forecast ◽  
Market Reactions ◽  
Stock Recommendations ◽  
Ex Ante ◽  
Forecast Quality

SYNOPSIS: We investigate the implications of recommendation-forecast consistency for the informativeness of stock recommendations and earnings forecasts and the quality of analysts' earnings forecasts. Stock recommendations and earnings forecasts are often issued simultaneously and evaluated jointly by investors. However, the two signals are often inconsistent with each other. Defining a recommendation-forecast pair as consistent if both of them are above or below their existing consensus, we find that 58.3 percent of recommendation-forecast pairs are consistent in our sample. We document that consistent pairs result in much stronger market reactions than inconsistent pairs. We show that analysts making consistent recommendation forecasts make more accurate and timelier forecasts than do analysts making inconsistent recommendation forecasts, suggesting that consistent analysts make higher-quality earnings forecasts. We extend the literature on informativeness of analyst research by showing that recommendation-forecast consistency is an important ex ante signal regarding both firm valuation and earnings forecast quality. Investors and researchers can use consistency as a salient, ex ante signal to identify more informative analyst research and superior earnings forecasts. Data Availability: All data are available from public sources.

scholarly journals Quality of the Wind Wave Forecast in the Black Sea Including Storm Wave Analysis

2021 ◽  
Vol 13 (23) ◽  
pp. 13099
Author(s):  
Stanislav Myslenkov ◽  
Alexander Zelenko ◽  
Yuriy Resnyanskii ◽  
Victor Arkhipkin ◽  
Ksenia Silvestrova
Keyword(s):  
Black Sea ◽  
Wind Wave ◽  
Altimeter Data ◽  
The Black Sea ◽  
Bias Error ◽  
Regular Grid ◽  
Wavewatch Iii ◽  
Wave Forecast ◽  
Forecast Quality

This paper presents the results of wind wave forecasts for the Black Sea. Three different versions utilized were utilized: the WAVEWATCH III model with GFS 0.25 forcing on a regular grid, the WAVEWATCH III model with COSMO-RU07 forcing on a regular grid, and the SWAN model with COSMO-RU07 forcing on an unstructured grid. AltiKa satellite altimeter data were used to assess the quality of wind and wave forecasts for the period from 1 April to 31 December 2017. Wave height and wind speed forecast data were obtained with a lead time of up to 72 h. The presented models provide an adequate forecast in terms of modern wave modeling (a correlation coefficient of 0.8–0.9 and an RMSE of 0.25–0.3 m) when all statistics were analyzed. A clear improvement in the wave forecast quality with the high-resolution wind forecast COSMO-RU07 was not registered. The bias error did not exceed 0.5 m in an SWH range from 0 to 3 m. However, the bias sharply increased to −2 or −3 m for an SWH range of 3–4 m. Wave forecast quality assessments were conducted for several storm cases.

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