In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional, return-volatility, distribution with a infinite mixture of bivariate Normal distributions with mean zero ...

Since the introduction of volatility derivatives, there has been growing interest in option implied volatility (IV). Many studies have examined informational content, and or forecast accuracy of IV, however there is relatively less work on directly modeling and forecasting IV. This paper uses a semi-parametric forecasting approaching to implement a time varying long volatility hedge to combine ...

The pervasive intraday periodicity in the return volatility in foreign exchange and equity markets is shown to have a strong impact on the dynamic properties of high frequency returns. Only by taking account of this strong intraday periodicity is it possible to uncover the complex intraday volatility dynamics that exists both within and across different financial markets. The explicit periodic ...

Moment conditions for multivariate generalized Ornstein-Uhlenbeck (MGOU) processes are derived and first and second moment are given in terms of the driving Lévy processes. In the second part of the paper a class of multivariate, positive semidefinite processes of MGOU–type is developed and suggested for use as squared volatility process in multivariate financial modelling.

We study the issuance and payout policy that maximizes the value of a firm facing both agency costs of free cash-flow and external financing costs. We find that firms have target cash levels and optimally issue equity when they run out of cash. We characterize the process modelling the number of outstanding shares and the dynamics of the stock prices. In line with the leverage effect identified...

The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models take the dependency of the conditional second moments. The idea behind ARCH/GARCH model is quite intuitive. For ARCH models, past squared innovations describes the present squared volatility. For GARCH models, both squared innovations and the past squared volatil...

We overview different methods of modeling stock prices and exchange rates volatility, focusing on their ability to reproduce the empirical properties in the corresponding time series. The properties of volatility change across the time scales of observations. Adequacy of volatility models for describe price dynamics at several time horizons simultaneously Special attention is a central topic of...

We study the effect of introducing stochastic volatility in the first passage structural approach to default risk. We analyze the impact of volatility time scales on the yield spread curve. In particular we show that the presence of a short time scale in the volatility raises the yield spreads at short maturities. We argue that combining first passage default modeling with multiscale stochastic...

In this paper, we modeled an artificial European option market under unknown volatility with liquidity costs using an agent-based modeling and simulation approach. The option price in the presence of liquidity costs is given by solving a partial differential equation. We proved that both unknown volatility and the unknown drift have significant effects in the pricing bias. Moreover, pricing bia...