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Volatility Changes (volatility + change)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	75%

Selected Abstracts

Housing Price Volatility Changes and Their Effects

REAL ESTATE ECONOMICS, Issue 1 2002
Walter Dolde
We examine significant volatility shifts in regional housing price changes, adapting a method of Haugen, Talmor and Torous (1991) independent of predefined sampling blocks. We identify 36 volatility events, most of which are purely regional, but three of which are national. We find significant associations of volatility events and economic conditions, especially national and regional income growth, inflation, and interest rates. During an initial adjustment period after a volatility shift, realized housing returns move opposite to volatility. We find evidence of significant interregional diffusion of volatility increases, but not of decreases. New insights on links between economic conditions and housing volatility and returns should be of value to household investors and mortgage investors. [source]

A High-Frequency Investigation of the Interaction between Volatility and DAX Returns

EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2010
Philippe Masset
G10; G12; G13 Abstract One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead-lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse-response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5-minute intervals, we find that the relationship is return-driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns. [source]

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2008
Cathy W. S. Chen
Summary To capture mean and variance asymmetries and time-varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy-tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time-delay parameter. Self-exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value-at-risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models. [source]