The present thesis deals with asymptotic analysis of financial time series models with conditional heteroscedasticity. It is well-established within financial econometrics that most financial time series data exhibit time varying conditional volatility, as well as other types of non-linearities. Reflecting this, all four essays of this thesis consider models allowing for time varying conditiona...

The paper estimate 1-day Value at Risk (VaR) taking into consideration the financial integration of Indian capital market (BSE-SENSEX and NSE-NIFTY) with other global indicators and its own volatility using daily returns covering the period January 2003 to December 2009. The paper specifies a generalized autoregressive conditional heteroscedasticity (GARCH) framework to model the phenomena of v...

F orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Real...

The unstable and uncertain nature of natural rubber prices makes them highly volatile prone to outliers, which can have a significant impact on both modeling forecasting. To tackle this issue, the author recommends hybrid model that combines autoregressive (AR) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. utilizes Huber weighting function ensure forecast value remai...

This study aims to analyze the level of beef price volatility before fasting (D-7) after Eid (H + 7) in Jambi City, and compile a forecast model. used survey method for traders Angso Duo market, City. The analysis calculate prices is ARCH (Autoregressive Conditional Heteroscedastic) model GARCH (Generalized Autoregressive Heteroscedasticity) analysis. average during period City was IDR 124,147 ...

Over the past years, cryptocurrencies have drawn substantial attention from media while attracting many investors. Since then, cryptocurrency prices experienced high fluctuations. In this paper, we forecast high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling to select best model. We propose various generalized autore...

Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a twolayer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two ste...

This is the first paper that estimates price determinants of Bitcoin in a generalized autoregressive conditional heteroscedasticity (GARCH) framework using high-frequency data. Derived from theoretical model, we structurally estimate transaction demand and speculative models hourly data for 2013–21. In line with hypothesis, our empirical results confirm both have statistically significant impac...

This research aims to investigate the causal relationship between Islamic stock market and exchange rate, as well examine volatility of index in emerging countries. The study utilized Granger causality test analyze rate employed Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for analysis forecasting. For this research, daily time series data ranging from 2012 2022 indic...

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...