Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has sometimes quite substantial upwards jumps and clusters on high levels. We investigate classical and nonclassical stochastic volatility models with respect to their extreme behavior. We show that classical stochastic volatility models driven by Brownian motion can model heav...

Horizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. Analyzing forecasting errors and...

In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly Gaussian, this unpredict...

American electoral volatility is in a free fall. Overtime variation in the partisan balance of presidential elections across states has matched an all-time low in American history and is a fraction of its 1970’s peak. The current decline in volatility parallels declines during the Gilded Age and Franklin Roosevelt’s presidency. Trends in electoral volatility vary regionally, and the South’s pat...

This paper proposes an unobserved fundamental component of volatility as a measure of risk. This concept of fundamental volatility may be more meaningful than the usual measures of volatility for market regulators. Fundamental volatility can be obtained using a stochastic volatility model, which allows us to `®lterÕ out the signal in the volatility information. We decompose four FTSE100 stock i...

In financial markets, errors in option hedging can arise from two sources. First, the option value is a nonlinear function of the underlying; therefore, hedging is instantaneous and hedging with discrete rebalancing gives rise to error. Frequent rebalancing can be impractical due to transaction costs. Second, errors in specifying the model for the underlying price movement (model specification ...

in this paper, in order to optimize the portfolio consisting of selected industrial stocks of petroleum products, automobiles and parts, electrical industry and extraction of minerals from tehran stock exchange member, first, time – varying conditional covariance matrix has been estimated based on the following multivariate garch models: diagonal-vech (1,1), ccc (1,1) and diagonal -bekk (1,1). ...

We study the dynamic relation between aggregate mutual fund flow and market-wide volatility. Using daily flow data and a VAR approach, we find that market volatility is negatively related to concurrent and lagged flow. A structural VAR impulse response analysis suggests that shock in flow has a negative impact on market volatility: An inflow (outflow) shock predicts a decline (an increase) in v...

Efficient financial markets with high degree of transparency do not substantiate the hypothesis that there are differences in the volatility of return. Generally, there are factors rejecting any perfect similarity in the volatility of return in the emerging stock markets, as previous studies in Iran have confirmed the complete difference. On the other hand, the hybrid model PANEL-GARCH has the ...