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Downside Risk Evaluation with the R Package GAS

Ardia, David - 2019

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so-called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions....

Persistent link: https://www.econbiz.de/10012902645

A Practical Guide to Harnessing the HAR Volatility Model

Clements, Adam - 2019

The standard heterogeneous autoregressive (HAR) model is perhaps the most popular benchmark model for forecasting … forecasting scheme made by the market practitioner. Another goal is to examine the effect of replacing its high-frequency data …

Persistent link: https://www.econbiz.de/10012888911

The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility

Hizmeri, Rodrigo - 2019

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in … forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that … and threshold bipower variation measures. Incorporating signed finite and infinite jumps generates significantly better …

Persistent link: https://www.econbiz.de/10012889687

Machine Learning for Realised Volatility Forecasting

Rahimikia, Eghbal; Poon, Ser-Huang - 2021

This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting … sample period of July 27, 2007, to November 18, 2016. We find strong evidence to support ML forecasting power dominating an …-ML has very strong forecasting power and adding news sentiment variables to the data set only improves the forecasting power …

Persistent link: https://www.econbiz.de/10013222880

Augmented Real-Time GARCH : A Joint Model for Returns, Volatility and Volatility of Volatility

Ding, Yashuang (Dexter) - 2021

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...

Persistent link: https://www.econbiz.de/10013234440

Beta-Adjusted Covariance Estimation

Boudt, Kris; Dragun, Kirill; Sauri, Orimar; Vanduffel, … - 2021

The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of...

Persistent link: https://www.econbiz.de/10013235022

Modeling Stock Returns Using Asymmetric Garch-Icapm with Mixture and Heavy-Tailed Distributions : An Application to COVID-19 Pandemic Forecasts

Khanthaporn, Rewat; Wichitaksorn, Nuttanan - 2021

COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we model the stock returns using...

Persistent link: https://www.econbiz.de/10013236407

The Economic Impact of Volatility Persistence on Energy Markets

Sklibosios Nikitopoulos, Christina; Thomas, Alice; … - 2021

This study examines the role of daily volatility persistence in transmitting information from macro-economy in the volatility of energy markets. In crude oil and natural gas markets, macro-economic factors, such as the VIX, the credit spread and the Baltic exchange dirty index, impact...

Persistent link: https://www.econbiz.de/10013237771

State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data

Chun, Dohyun; Kim, Donggyu - 2021

Recently, to account for low-frequency market dynamics, several volatility models, employing high-frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous...

Persistent link: https://www.econbiz.de/10013237877

Panel Data Nowcasting in a Data-Rich Environment : The Case of Price-Earnings Ratios

Babii, Andrii; Ball, Ryan T.; Ghysels, Eric; Striaukas, … - 2022

This paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled...

Persistent link: https://www.econbiz.de/10013492089