This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio...

Investors invest in a foreign market to reap the benefits of currency differences. The change in the value of underlying assets affects these hedged funds and, at the same time, restricts investors from higher return possible in unhedged funds. This study aims to examine the performance of most...

This article has studied several fluctuations in the Iranian currency market and multiple turmoils in the economy that have not only wiped out Iranians private savings but also affected financial market activists to provide a better understanding of fluctuations' movement between markets. To...

The research on asset volatility in financial market is the foundation of predict asset volatility accurately, Bollerslev built a generalised ARCH (GARCH) model based on the ARCH model. provides a more real financial instruments. to estimate a bes term and lastly, test for significance. The...

For many financial applications, it is important to have reliable and tractable models for the behavior of assets and indexes, for example in risk evaluation. A successful approach is based on ARCH processes, which strike the right balance between statistical properties and ease of computation....

The cryptocurrency market has recently gained a lot of interest from investors worldwide but is also much more volatile than the stock market. Volatility is a significant characteristic of both exchange markets and has a great influence on investment behavior. However, there is little research...

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model...