This paper investigates the conditional volatility of the firm’s assets in contrast to existing studies that focus primarily on equity volatility. Using a novel dataset that allows us to map out significant portions of the capital structure, we examine the volatility properties of asset returns as calculated by a weighted average of equity, bond and loan prices. The two fundamental findings in ...

Modelling the asymmetric effect between return and volatility has long been an important issue in finance as well as in econometrics. While much literature interpret this asymmetric effect as a natural result from leverage and modelled as a threshold GARCH process, it has now accepted that it is more likely to due to the feedback effect from expected volatility. Considering this explanation, in...

Continuous-time autoregressive moving average (CARMA) processes with a nonnegative kernel and driven by a non-decreasing Lévy process constitute a useful and very general class of stationary, non-negative continuous-time processes which have been used, in particular, for the modelling of stochastic volatility. Brockwell, Davis and Yang (2011) considered the fitting of CARMA models to closely an...

We study pricing under the local volatility. Our research is mainly intended for pedagogical purposes. In the first part of our work we study the local volatility modeling. We derive the local volatility formula in terms of the European call prices and in terms of the market implied volatilities. We propose and calibrate to the DAX option data a functional form for the implied volatility which ...

Measuring and forecasting volatility of asset returns is very important for asset trading and risk management. There are various forms of volatility estimates, including implied volatility, realized volatility and volatility assumed under stochastic volatility models and GARCH models. Research has shown that these different methods are closely related but have different perspectives, strengths ...

Empirical evidence from equity markets clearly shows that the volatility of asset returns varies randomly in time. Typically, this randomness is referred to as stochastic volatility. In this article, we review how stochastic volatility can be modeled, and the use of asymptotic analysis to quantify (i) how the presence of stochastic volatility affects option prices, and (ii) how stochastic volat...

While volatile marketing spending, as opposed to even-level spending, may improve a brand’s financial performance, it can also increase the volatility of performance, which is not a desirable outcome. This paper analyzes how revenue and cash-flow volatility are influenced by own and competitive marketing spending volatility, by the level of marketing spending, by the responsiveness to own marke...

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in the information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variabl...

In this paper, we assess the efficiency, information content and unbiasedness of volatility forecasts based on the VIX/VXN implied volatility indexes, RiskMetrics and GARCHtype models at the 5-, 10and 22-day time horizon. Our empirical application focuses on the S&P100 and NASDAQ100 indexes. We also deal with the information content of the competing volatility forecasts in a market risk (VaR ty...