231,204 research outputs found
Volatility forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3,4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Operational river discharge forecasting in poorly gauged basins: the Kavango River Basin case study
Operational probabilistic forecasts of river discharge are essential for
effective water resources management. Many studies have addressed this topic
using different approaches ranging from purely statistical black-box
approaches to physically based and distributed modeling schemes employing
data assimilation techniques. However, few studies have attempted to develop
operational probabilistic forecasting approaches for large and poorly gauged
river basins. The objective of this study is to develop open-source software
tools to support hydrologic forecasting and integrated water resources
management in Africa. We present an operational probabilistic forecasting
approach which uses public-domain climate forcing data and a
hydrologic–hydrodynamic model which is entirely based on open-source
software. Data assimilation techniques are used to inform the forecasts with
the latest available observations. Forecasts are produced in real time for
lead times of 0–7 days. The operational probabilistic forecasts are
evaluated using a selection of performance statistics and indicators and the
performance is compared to persistence and climatology benchmarks. The
forecasting system delivers useful forecasts for the Kavango River, which
are reliable and sharp. Results indicate that the value of the forecasts is
greatest for intermediate lead times between 4 and 7 days
Maximum Entropy Approach for the Prediction of Urban Mobility Patterns
The science of cities is a relatively new and interdisciplinary topic. It
borrows techniques from agent-based modeling, stochastic processes, and partial
differential equations. However, how the cities rise and fall, how they evolve,
and the mechanisms responsible for these phenomena are still open questions.
Scientists have only recently started to develop forecasting tools, despite
their importance in urban planning, transportation planning, and epidemic
spreading modeling. Here, we build a fully interpretable statistical model
that, incorporating only the minimum number of constraints, can predict
different phenomena arising in the city. Using data on the movements of
car-sharing vehicles in different Italian cities, we infer a model using the
Maximum Entropy (MaxEnt) principle. With it, we describe the activity in
different city zones and apply it to activity forecasting and anomaly detection
(e.g., strikes, and bad weather conditions). We compare our method with
different models explicitly made for forecasting: SARIMA models and Deep
Learning Models. We find that MaxEnt models are highly predictive,
outperforming SARIMAs and having similar results as a Neural Network. These
results show how relevant statistical inference can be in building a robust and
general model describing urban systems phenomena. This article identifies the
significant observables for processes happening in the city, with the
perspective of a deeper understanding of the fundamental forces driving its
dynamics.Comment: 14 pages, 7 figure
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