Volatility and the role of order book structure

There is much literature that deals with modeling and forecasting asset return volatility. However, much of this research does not attempt to explain variations in the level of volatility. Movements in volatility are often linked to trading volume or frequency, as a reflection of underlying information flow. This paper considers whether the state of an open limit order book influences volatility. It is found that market depth and order imbalance do influence volatility, even in the presence of the traditional volume related variables.Realized volatility, bi-power variation, limit order book, market microstructure, order imbalance

Portfolio allocation: Getting the most out of realised volatility

Recent advances in the measurement of volatility have utilized high frequency intraday data to produce what are generally known as realised volatility estimates. It has been shown that forecasts generated from such estimates are of positive economic value in the context of portfolio allocation. This paper considers the link between the value of such forecasts and the loss function under which models of realised volatility are estimated. It is found that employing a utility based estimation criteria is preferred over likelihood estimation, however a simple mean squared error criteria performs in a similar manner. These findings have obvious implications for the manner in which volatility models based on realised volatility are estimated when one wishes to inform the portfolio allocation decision.Volatility, utility, portfolio allocation, realized volatility, MIDAS

A nonparametric approach to forecasting realized volatility

A well developed literature exists in relation to modeling and forecasting asset return volatility. Much of this relate to the development of time series models of volatility. This paper proposes an alternative method for forecasting volatility that does not involve such a model. Under this approach a forecast is a weighted average of historical volatility. The greatest weight is given to periods that exhibit the most similar market conditions to the time at which the forecast is being formed. Weighting occurs by comparing short-term trends in volatility across time (as a measure of market conditions) by the application of a multivariate kernel scheme. It is found that at a 1 day forecast horizon, the proposed method produces forecasts that are significantly more accurate than competing approaches.Volatility, forecasts, forecast evaluation, model confidence set, nonparametric

A Cholesky-MIDAS model for predicting stock portfolio volatility

This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.Cholesky, Midas, volatility forecasts

Are combination forecasts of S&P 500 volatility statistically superior?

Forecasting volatility has received a great deal of research attention. Many articles have considered the relative performance of econometric model based and option implied volatility forecasts. While many studies have found that implied volatility is the preferred approach, a number of issues remain unresolved. One issue being the relative merit of combination forecasts. By utilising recent econometric advances, this paper considers whether combination forecasts of S&P 500 volatility are statistically superior to a wide range of model based forecasts and implied volatility. It is found that combination forecasts are the dominant approach, indicating that the VIX cannot simply be viewed as a combination of various model based forecasts.Implied volatility, volatility forecasts, volatility models, realized volatility, combination forecasts.

A Kernel Technique for Forecasting the Variance-Covariance Matrix

The forecasting of variance-covariance matrices is an important issue. In recent years an increasing body of literature has focused on multivariate models to forecast this quantity. This paper develops a nonparametric technique for generating multivariate volatility forecasts from a weighted average of historical volatility and a broader set of macroeconomic variables. As opposed to traditional techniques where the weights solely decay as a function of time, this approach employs a kernel weighting scheme where historical periods exhibiting the most similar conditions to the time at which the forecast if formed attract the greatest weight. It is found that the proposed method leads to superior forecasts, with macroeconomic information playing an important role.Nonparametric, variance-covariance matrix, volatility forecasting, multivariate

Does implied volatility reflect a wider information set than econometric forecasts?

Much research has addressed the relative performance of option implied volatilities and econometric model based forecasts in terms of forecasting asset return volatility. The general theme to come from this body of work is that implied volatility is a superior forecast. Some authors attribute this to the fact that option markets use a wider information set when forming their forecasts of volatility. This article considers this issue and determines whether S&P 500 implied volatility reflects a set of economic information beyond its impact on the prevailing level of volatility. It is found, that while the implied volatility subsumes this information, as do model based forecasts, this is only due to its impact on the current or prevailing level of volatility. Therefore, it appears as though implied volatility does not reflect a wider information set than model based forecasts, implying that implied volatility forecasts simply reflect volatility persistence in much the same way of as do econometric models.Implied volatility, VIX, volatility forecasts, informational efficiency

Forecasting stock market volatility conditional on macroeconomic conditions.

This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts)is used in the parameter estimation process. Based on an application of this model to S&P500 share index returns, it is demonstrated that forecasts of macroeconomic variables can be easily incorporated into volatility forecasts for share index returns. It transpires that the model proposed here can lead to significantly improved volatility forecasts compared to traditional GARCH type volatility models.Volatility, macroeconomic data, forecast, spline, GARCH.

Investor Expectations and Systematic Risk

This study refines the estimation of beta risk within the Capital Asset Pricing Model (CAPM) framework. Evidence is provided that the link between ex-ante risk and ex-post returns is strengthened by more accurately reflecting the formation of investor expectations. An adaptive expectations approach is employed as an estimation technique consistent with the behavioural patterns of investors. Finally, the study compares the capability of risk estimates from both the standard CAPM and adaptive expectation methods to account for future asset returns in Australia.Asset Pricing; Adaptive Expectations; Australia.