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In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the...
Persistent link: https://www.econbiz.de/10012910113
The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...
Persistent link: https://www.econbiz.de/10012910119
Persistent link: https://www.econbiz.de/10009736952
Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...
Persistent link: https://www.econbiz.de/10003636008
We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...
Persistent link: https://www.econbiz.de/10011431503
Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...
Persistent link: https://www.econbiz.de/10013064150
Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...
Persistent link: https://www.econbiz.de/10013105658
Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new robust orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional...
Persistent link: https://www.econbiz.de/10012134234
- In this analysis; we study the four major cryptocurrency returns that are Bitcoin, Ethereum, XRP, and Litecoin, where the dynamics of volatility spillover are observed for a span of 7 years – 2013 to 2020; wherein the total number of sample observations collected and analyzed were 10,953 (Data...
Persistent link: https://www.econbiz.de/10013310640
Daul et al. (2003), Demarta and McNeil (2005) and Mcneil et al. (2005) underlined the ability of the grouped t-copula to take the tail dependence present in a large set of financial assets into account, particularly when the assumption of one global parameter for the degrees of freedom (as for...
Persistent link: https://www.econbiz.de/10013134397