We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) to improve both in-sample analysis and out-of-sample forecast performance traditional conditional heteroskedastic models. In particular, incorporate auxiliary deterministic processes, governed by recurrent neural networks, into variance e.g. GARCH-type flexibly capture dynamics ...

An understanding of volatility in stock markets is important for determining the cost of capital and for assessing investment and leverage decisions as volatility is synonymous with risk. Substantial changes in volatility of financial markets are capable of having significant negative effects on risk averse investors. Using daily returns from 1992 to 2002, we investigate volatility co-movement ...

The environmental literature lacks the use of volatility based models for environmental stochastic processes. To overcome this deficiency, we use EGARCH, IGARCH, TGARCH, GJR-GARCH, NGARCH, AVGARCH and APARCH models for functional relationships of the pathogen indicators time series for recreational activates at beaches. We use generalized error, Student’s t, exponential, normal and normal inver...

Referring to related documents and papers, we implement several different approaches to compute the VaR of a delta-hedged portfolio constructed by 41 stocks and corresponding options. First we interpreted the concepts and techniques involved with our study. Then we discussed the details about both Historical Simulation and Monte Carlo Simulation, and pointed out their shortcomings through exper...

In this study, we employ a recently developed econometric technique of the threshold model with the GJR-GARCH process on error terms to investigate the relationships between weather factors and stock market returns in Taiwan using daily data for the period of 1 July 1997–22 October 2003. The major weather factors studied include temperature, humidity, and cloud cover. Our empirical evidence sho...

Atmospheric carbon dioxide concentration (ACDC) is a crucial variable for many environmental simulation models, and is regarded as an important factor for predicting temperature and climate changes. However, the conditional variance of ACDC levels has not previously been examined. This paper analyses the trends and volatility in ACDC levels using monthly data from January 1965 to December 2002....

Across the globe, COVID-19 has disrupted financial markets, making them more volatile. Thus, this paper examines market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, Sugar during pandemic. We applied GARCH (1, 1), GJR-GARCH EGARCH 1) econometric models on daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical finding...

Financial series occasionally exhibit large changes. To deal with those events, we assume that the observed return series consists of a conditionally Gaussian ARMA-GARCH (or -GJR) model contaminated by an additive jump component. In this framework, we propose a new test for additive jumps. The test is based on standardised returns, where the first two conditional moments of the non-contaminated...