In this paper, the author uses geometrical and topological aspects of Exploratory Data Analysis (EDA) to examine Standard and Poor's (S&P), MSCI's and Thomson Reuters' (TRI) ways of determining which stocks are growth and which are value. The results of the analysis are that two of the firms -...

As a preliminary study of the effect of return and sampling on chaos and stochastic data pattern, this research tests chaos pattern by using Henon attractor as a sample of two-dimensional discrete chaos data with an assumption that economic and finance data are generated by low dimensional chaos...

We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identifiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments...

Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to...

With the exception of naive methods for portfolio selection, such as the equal weighted approaches, all other methods of portfolio allocation are more or less sensitive to the quality of the inputs considered in constructing the models and risk measures utilised in the allocation framework. The...

This paper studies the return behaviour at National Stock Exchange, India and the Rupee-Dollar exchange rate, using NSE's Nifty as the benchmark for stock returns, while INR-USD rate is used as the benchmark for exchange rate. The daily closing levels of the two benchmarks for a period beginning...

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency...

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning...

Using high-frequency intraday data, we construct, test and model seven new realized volatility estimators for six international equity indices. We detect jumps in these estimators, construct the jump components of volatility and perform various tests on their properties. Then we use the class of...