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Index Returns (index + return)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	92%

Kinds of Index Returns

stock index return

Selected Abstracts

Mean Reversion and the Distribution of United Kingdom Stock Index Returns

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 9-10 2006
David Ashton
Abstract:, Our purpose here is to develop the Pearson Type IV distribution as a candidate for modelling the evolution of short period stock index returns. Here, early work by Praetz (1972 and 1978) and Blattberg and Gonedes (1974) has shown that the scaled ,t' distribution, which is a particular (symmetric) interpretation of the Pearson Type IV, provides a reasonable description of the way stock index returns evolve over time. Our analysis shows this is certainly not the case for the daily stock index returns on which our empirical analysis is based. There is significant skewness in the data and this cannot be captured by symmetric distributions like the scaled ,t' and normal distributions. However, the Pearson Type IV, which is a skewed generalisation of the scaled ,t', is capable of modelling the skewness inherent in our data and in such a way that it satisfies asymptotically efficient goodness of fit criteria. Furthermore, the Pearson Type IV can be derived from a stochastic differential equation with standard Markov properties. This enables one to integrate the distributional and time series properties of the returns process and thereby, facilitates both the interpretation and understanding of the role played by the distribution's parameters in the generation of the underlying stock index returns. [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]

The Impact of Macroeconomic and Financial Variables on Market Risk: Evidence from International Equity Returns

EUROPEAN FINANCIAL MANAGEMENT, Issue 4 2002
Dilip K. Patro
Using a GARCH approach, we estimate a time,varying two,factor international asset pricing model for the weekly equity index returns of 16 OECD countries. We find significant time,variation in the exposure (beta) of country equity index returns to the world market index and in the risk,adjusted excess returns (alpha). We then explain these world market betas and alphas using a number of country,specific macroeconomic and financial variables with a panel approach. We find that several variables including imports, exports, inflation, market capitalisation, dividend yields and price,to,book ratios significantly affect a country's exposure to world market risk. Similar conclusions are obtained by using lagged explanatory variables, and thus these variables may be useful as predictors of world market risks. Several variables also significantly impact the risk,adjusted excess returns over this time period. Our results are robust to a number of alternative specifications. We further discuss some economic hypotheses that may explain these relationships. [source]

Cash Flows and Discount Rates, Industry and Country Effects and Co-Movement in Stock Returns

FINANCIAL REVIEW, Issue 2 2007
John Ammer
F36; G15 Abstract We apply the Campbell decomposition to industry-by-country, national, global industry, and world stock index returns using 1995,2003 data. World, global industry, and country factors are all important for each of the two key components of stock returns: news about future dividends and news about future discount rates. Furthermore, the world component of future discount rates is more important than the idiosyncratic component, while the reverse is true for news about future dividends. Our results are broadly consistent with co-movement in future discount rates arising from perceptions of common elements of risk in international equity markets. [source]

Macroeconomic factors and share returns: an analysis using emerging market data

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2002
S.G.M. Fifield
Abstract This paper investigates the extent to which global and local economic factors explain returns in emerging stock markets (ESMs). The economic factors are determined using principal components analysis. The results suggest that the local economic variables included in this study can be summarized by GDP, inflation, money and interest rates, while the selected global variables can be sufficiently characterized by world industrial production and world inflation. These components are then used as inputs into a regression analysis in order to explain the index returns of 13 ESMs over the period 1987,96. The analysis indicates that while world factors are significant in explaining ESM returns, local factors may also play a crucial role. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Mean Reversion and the Distribution of United Kingdom Stock Index Returns

Modelling Regime-Specific Stock Price Volatility,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2009
Carol Alexander
Abstract Single-state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state-dependent behaviour of volatility nor explain why the equity index skew persists into long-dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state-dependent leverage effect to NM-GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time-varying volatility risk premia. An empirical study on European equity indices identifies two-state asymmetric NM-GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean-reversion and leverage effects during crash markets are quite different from those in the stable regime. [source]

The Bias of the RSR Estimator and the Accuracy of Some Alternatives

REAL ESTATE ECONOMICS, Issue 1 2002
William N. Goetzmann
This paper analyzes the implications of cross-sectional heteroskedasticity in the repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross-sectional variance of asset returns affects the magnitude of the bias in the average return estimate for each period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest an unbiased maximum likelihood alternative to the RSR that directly estimates index returns, which we term MLRSR. The unbiased MLRSR estimators are analogous to the RSR estimators but are arithmetic averages of individual asset returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and that the MLRSR may be more accurate than RSR and some alternative methods. [source]

INTANGIBLE ASSETS, BOOK-TO-MARKET, AND COMMON STOCK RETURNS

THE JOURNAL OF FINANCIAL RESEARCH, Issue 1 2006
James M. Nelson
Abstract I examine two anomalies where the Fama and French three-factor model fails to adequately explain monthly industry and index returns. Both anomalies are consistent with a bad model problem where the book-to-market factor introduces a negative bias in the intercepts. I propose the intangibles model as an alternative where the three-factor model is known to have difficulty. This alternative model, which replaces the book-to-market factor with zero investment portfolio returns based on prior investments in intangible assets, is well specified in random samples, has comparable power, and fully explains both anomalies. [source]

Does an index futures split enhance trading activity and hedging effectiveness of the futures contract?

THE JOURNAL OF FUTURES MARKETS, Issue 12 2006
Lars Nordén
Recently, several stock index futures exchanges have experimented with an altered contract design to make the contract more attractive and to increase investor accessibility. In 1998, the Swedish futures exchange (OM) split the OMX-index futures contract with a factor of 4:1, without altering any other aspect of the futures contract design. This isolated contract redesign enables a ceteris paribus analysis of the effects of a futures split. The purpose is to investigate whether the futures split affects the futures market trading activity, as well as hedging effectiveness and basis risk of the futures contract. A bivariate GARCH framework is used to jointly model stock index returns and changes in the futures basis, and to obtain measures of hedging efficiency and basis risk. Significantly increased hedging efficiency and lower relative basis risk is found following the split. In addition, evidence of an increased trading volume is found after the split, whereas the futures bid-ask spread appears to be unaffected by the split. The results are consistent with the idea that the futures split has enhanced trading activity and hedging effectiveness of the futures contract, without raising the costs of transacting at the futures market. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:1169,1194, 2006 [source]

Forecasting stock index volatility

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2001
Riccardo Bramante
Abstract Accurate volatility forecasting is the key to successful risk analysis. In fact, volatility forecasts lie at the centre of many financial systems, such as value at risk modelling and pricing of derivative securities. This paper is concerned with how to construct stock index volatility predictors using the returns histories of the stocks that define the Index. Specifically, our approach presupposes that the total volatility of the index returns can be explained by the volatility of the related components. Copyright © 2001 John Wiley & Sons, Ltd. [source]