Do Jumps Matter in Realized Volatility Modeling and Forecasting? Empirical Evidence and a New Model

2020 ◽  
Author(s):  
Massimiliano Caporin
Keyword(s):  
Empirical Evidence ◽  
Realized Volatility ◽  
New Model ◽  
Volatility Modeling ◽  
Modeling And Forecasting

Self-Control as a Coordination Problem

2020 ◽  
pp. 65-80
Author(s):  
Asael Y. Sklar ◽  
Kentaro Fujita
Keyword(s):  
Empirical Evidence ◽  
Self Control ◽  
Future Research ◽  
Conceptual Frameworks ◽  
Alternative Account ◽  
Coordination Problem ◽  
New Model ◽  
The Mind

This chapter presents an analysis of self-control from a motivational perspective, modeling it as the resolution of a conflict between proximal and distal concerns. It briefly reviews “divided-mind” models that suggest that self-control entails competition between opposing elements of the mind, and discusses some of the empirical and conceptual challenges to these conceptual frameworks. The authors then propose an alternative account that addresses these challenges, suggesting that coordination of (rather than competition between) elements of the mind is key to self-control. They review empirical evidence for the new model, and then conclude by outlining some of its implications for future research and theory.

Structural breaks in unbiased volatility estimator: Modeling and forecasting

2017 ◽  
Vol 11 (1) ◽  
pp. 27-50 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
Extreme Value ◽  
Trading Strategy ◽  
Risk Averse ◽  
Memory Property ◽  
Economic Significance ◽  
Forecasting Performance ◽  
Modeling And Forecasting

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

scholarly journals Localized Realized Volatility Modeling

2010 ◽  
Vol 105 (492) ◽  
pp. 1376-1393 ◽  
Author(s):  
Ying Chen ◽  
Wolfgang Karl Härdle ◽  
Uta Pigorsch
Keyword(s):  
Realized Volatility ◽  
Volatility Modeling

scholarly journals Modeling and Forecasting Realized Volatility

Econometrica ◽  
2003 ◽  
Vol 71 (2) ◽  
pp. 579-625 ◽  
Author(s):  
Torben G. Andersen ◽  
Tim Bollerslev ◽  
Francis X. Diebold ◽  
Paul Labys
Keyword(s):  
Realized Volatility ◽  
Modeling And Forecasting

Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting

2015 ◽  
Vol 48 (3) ◽  
pp. 379-398 ◽  
Author(s):  
Leandro Maciel ◽  
Fernando Gomide ◽  
Rosangela Ballini
Keyword(s):  
Financial Volatility ◽  
Volatility Modeling ◽  
Modeling And Forecasting

scholarly journals A New Model for Pricing Collateralized OTC Derivatives

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Credit Risk ◽  
Empirical Evidence ◽  
Market Price ◽  
New Model ◽  
Otc Derivatives ◽  
Risk Measurements ◽  
The Impact ◽  
Unique Dataset

This paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. https://arabixiv.org/b9vg8/download

scholarly journals A New Model for Pricing Collateralized OTC Derivatives

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Credit Risk ◽  
Empirical Evidence ◽  
Market Price ◽  
New Model ◽  
Otc Derivatives ◽  
Risk Measurements ◽  
The Impact ◽  
Unique Dataset

ABSTRACTThis paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. https://frenxiv.org/am8zy/download

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