SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements,...

Using a flexible statistical framework that accounts for time-varying skewness and leptokurtosis, we examine the stochastic behavior of Bitcoin in comparison to five major currencies. The empirical findings reveal that the distribution of all series is leptokurtic. Once the effect of...

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator...

This paper aims to empirically investigate the dynamic connectedness between oil prices and stock returns of clean energy-related and technology companies in China and U.S. financial markets. We apply three multivariate GARCH model specifications (CCC, DCC and ADCC) to investigate the return and...

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other...

Understanding the impact of economic shocks on exchange-rate volatility is important because it could help mitigate risks and vulnerabilities that affect investment decisions and macroeconomic stability. In this paper, we examine how asymmetric shocks and volatility are transmitted across major...

We scrutinize the impact of dividend policy on stock price volatility by considering the seminal paper of Baskin (1989). In this context, we examine the relationship between volatility and three dividend policy indicators, dividend yield, dividend payout, and stock repurchases, for 1,221 firms...

We use Deep Neural Networks to reconstruct GARCH parameters for empirical financial time series. The algorithm we develop, allows us to fit autocovariance of squared returns of financial data, with certain time lags, the second order statistical moment and the fourth order standardised moment....

In this short paper we try to systematically address and answer some question arising in the daily practice of hedge fund risk management. Nowadays several intraday measures of risk are available to the risk manager, but the informativeness of these higher frequency statistics is not yet...

We measure bond and stock conditional return volatility as a function of changes in sentiment, proxied by six indicators from the Tel Aviv Stock Exchange. We find that changes in sentiment affect conditional volatilities at different magnitudes and often in an opposite manner in the two markets,...