We estimate latent factor models of liquidity and volatility. Common liquidity and volatility factors are extracted using multiple liquidity and volatility measures. Additionally, latent factors are extracted by aggregating across both liquidity and volatility resulting in what we will call the common “uncertainty” factors. We find that volatility and the common uncertainty risk are significant...

This paper relates variation in stock market volatility to regime shifts in stock market returns. We apply a Markov switching model to market returns and examine the variation in volatility in different return regimes. We find that stock returns are best characterized by a model containing six regimes with significantly different volatility across the regimes. Volatility is higher when returns ...

Growth stability is an important objective—because development requires sustained increases in income, because volatility is costly for the poor, and because volatility deters growth. We study the determinants of average growth and its volatility as a two-equation system, and find that higher levels of democracy and diversification lower volatility, whereas volatility itself reduces growth. Mus...

The availability of intra-day data on the prices of speculative assets means that we can use quadratic variation like measures of activity in financial markets, called realised volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realised volatility erro...

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...