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Volatility Clustering (volatility + clustering)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	80%

Selected Abstracts

MEASURING RISK IN ENVIRONMENTAL FINANCE

JOURNAL OF ECONOMIC SURVEYS, Issue 5 2007
Suhejla Hoti
Abstract Environmental sustainability indices, such as the Dow Jones Sustainability Indexes and the Ethibel Sustainability Index, quantify the development and promotion of sustainable social, ethical and environmental values in the community. Moreover, such indices provide a benchmark for managing sustainability portfolios, and developing financial products and services that are linked to sustainable economic, environmental, social and ethical criteria. This paper reviews the existing data and risk indices in environmental finance. The main purpose of the paper is to analyse existing sustainability and ethical indices in environmental finance, and evaluate empirical environmental risk by estimating conditional volatility clustering that is inherent in these indices. Financial volatility models are estimated to analyse the underlying conditional volatility or time-varying risk that is inherent in alternative environmental sustainability indices. Volatility clustering is observed for most series, but some extreme observations are also evident. The log- and second-moment conditions suggest that valid inferences can be drawn for purposes of sensible empirical analysis. [source]

Modelling volatility clustering in electricity price return series for forecasting value at risk

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 1 2009
R. G. Karandikar
Abstract Modelling of non-stationary time series using regression methodology is challenging. The wavelet transforms can be used to model non-stationary time series having volatility clustering. The traditional risk measure is variance and now a days Value at Risk (VaR) is widely used in finance. In competitive environment, the prices are volatile and price risk forecasting is necessary for the market participants. The forecasting period may be 1 week or higher depending upon the requirement. In this paper, a model is developed for volatility clustering in electricity price return series and its application for forecasting VaR is demonstrated. The first model is using GARCH (1, 1). The VaR of variance rate series, that is worst-case volatility is calculated using variance method using wavelet transform. The model is used to forecast variance rate (volatility) for a sample case of 1-week half-hourly price return series. The second model developed is for forecasting VaR for price return series of 440 days. This model is developed using wavelets via multi-resolution analysis and uses regime-switching technique. The historical data of daily average prices is obtained from 100% pool type New South Wales (NSW), a zonal market of National Electricity Market (NEM), Australia. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Financial Intermediaries and Interest Rate Risk: II

FINANCIAL MARKETS, INSTITUTIONS & INSTRUMENTS, Issue 5 2006
Sotiris K. Staikouras
The current work extends and updates the previous survey (Staikouras, 2003) by looking at other aspects of the financial institutions' yield sensitivity. The study starts with an extensive discussion of the origins of asset-liability management and the subsequent work to identify effective ways of measuring and managing interest rate risk. The discussion implicates both regulatory and market-based approaches along with any issues surrounding their applicability. The literature is enriched by recognizing that structural and regulatory shifts affect financial institutions in different ways depending on the size and nature of their activities. It is also noted that such shifts could change the bank's riskiness, and force banks to adjust their balance sheet size by altering their maturity intermediation function. Besides yield changes, market cycles are also held responsible for asymmetric effects on corporate values. Furthermore, nonstandard investigations are considered, where embedded options and basis risk are significant above and beyond the intermediary's rate sensitivity, while shocks to the slope of the yield curve is identified as a new variable. When the discount privilege is modeled as an option, it is shown that its value is incorporated in the equities of qualifying banks. Finally, volatility clustering is further established while constant relative risk aversion is not present in the U.S. market. Although some empirical findings may be quite mixed, there is a general consensus that all forms of systematic risk, risk premia, and the risk-return trade-off do exhibit some form of variability, not only over time but also across corporate sizes and segments. [source]

MEASURING RISK IN ENVIRONMENTAL FINANCE

Bootstrapping Financial Time Series

JOURNAL OF ECONOMIC SURVEYS, Issue 3 2002
Esther Ruiz
It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate. [source]