In this paper, we focus on the trade and quote data for the IBM stock traded at the NYSE. We present two di erent frameworks for analyzing this dataset. First, using regularly sampled observations, we characterize the intraday volatility of the mid-point of the bid-ask quotes by estimating GARCH and EGARCH models, with intraday seasonality being accounted for. We also highlight the impact of ch...

The study examines the ability of three volatility-forecasting models to estimate the term structure of implied volatilities. The tests are performed on equity Warrants listed on the JSE with the three measures being the Generalized Autoregressive Conditional Heteroscedicity (GARCH), the exponential GARCH (EGARCH) and the Exponentially Weighted Moving Average (EWMA). The Black-Scholes implied v...

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurre...

We propose an extension of the CPPI method, which is based on conditional floors. In this framework, we examine in particular the margin based strategies. This method allows to keep part of the past gains and to protect the portfolio value against future high drawdowns of the financial market. However, as for the standard CPPI method, the investor can benefit from potential market rises. To con...

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...

In this paper, we empirically investigate the effect of short selling on market volatility during exogenously-induced uncertainties. Using Covid-19 pandemic and onset Russian-Ukraine Conflicts periods as event study, employ asymmetric EGARCH model. We show high persistence effects pre-covid outbreak and post-covid periods. find evidence that increases during period while conflict is characte...

this paper examines regime shifts in tedpix return and volatility and the effects of positive and negative crude oil shocks and gold price fluctuations on stock market shifts behavior using markov switching egarch model with student’s t-distribution. we detect two episodes of series behavior, one relative to low mean/high variance regime namely bear state and the other to high mean/low variance...