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Value at risk forecasting with the ARMA-GARCH family of models in times of increased volatility

Jánský, Ivo; Rippel, Milan - 2011

The paper evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and …

Persistent link: https://www.econbiz.de/10010322244

Systemic risk for financial institutions of major petroleum-based economies: The role of oil

Khalifa, Ahmed; Caporin, Massimiliano; Costola, Michele; … - 2017

This paper examines the relationship between oil price movements and systemic risk of many financial institutions in major petroleum-based economies. We estimate ΔcoVaR for those institutions and thereby observe the presence of elevated increases in the levels corresponding to the subprime and...

Persistent link: https://www.econbiz.de/10012064299

Forecasting realized volatility in turbulent times using temporal fusion transformers

Frank, Johannes - 2023

This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well as GARCH models. The models are trained on...

Persistent link: https://www.econbiz.de/10014000512

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...

Persistent link: https://www.econbiz.de/10010334248

Forecasting the Volatility of Nikkei 225 Futures

Asai, Manabu; McAleer, Michael - 2017

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers the stochastic volatility model with asymmetry and...

Persistent link: https://www.econbiz.de/10011662515

Copula-based vMEM specifications versus alternatives: The case of trading activity

Cipollini, Fabrizio; Engle, Robert F.; Gallo, Giampiero M. - In: Econometrics 5 (2017) 2, pp. 1-24

We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past...

Persistent link: https://www.econbiz.de/10011755372

Validierung von Konzepten zur Messung des Marktrisikos: Insbesondere des Value at Risk und des Expected Shortfall

Mehmke, Fabian; Cremers, Heinz; Packham, Natalie - 2012

Market risk management is one of the key factors to success in managing financial institutions. Underestimated risk can have desastrous consequences for individual companies and even whole economies, not least as could be seen during the recent crises. Overestimated risk, on the other side, may...

Persistent link: https://www.econbiz.de/10010309829

Estimating the volatility of asset pricing factors

Becker, Janis; Leschinski, Christian - 2018

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

Persistent link: https://www.econbiz.de/10012030917

Aggregating Credit and Market Risk: The Impact of Model Specification

Lucas, Andre; Verhoef, Bastiaan - 2012

credit risk. The specification effect can lead to Value-at-Risk (VaR) reductions in the range of 3 percent to 47 percent …

Persistent link: https://www.econbiz.de/10010326345

High frequency vs. daily resolution: The economic value of forecasting volatility models 2nd ed

Lilla, Francesca - 2017

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing … fat-tails) and leverage effects translates in more accurate VaR measure. …

Persistent link: https://www.econbiz.de/10011819006