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3 edition of Overnight stock returns and time-varying correlations found in the catalog.

Overnight stock returns and time-varying correlations

Overnight stock returns and time-varying correlations

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  • 4 Currently reading

Published by Indian Institute of Management in Ahmedabad .
Written in English

    Subjects:
  • Stocks -- Rate of return -- Mathemadical models.

  • Edition Notes

    StatementAjay Pandey.
    SeriesWorking paper ;, W.P. no. 2003-09-05, Working paper (Indian Institute of Management, Ahmedabad) ;, W.P. no. 2003-09-05.
    ContributionsIndian Institute of Management, Ahmedabad.
    Classifications
    LC ClassificationsMicrofiche 2004/60595 (H)
    The Physical Object
    FormatMicroform
    Pagination32 leaves
    Number of Pages32
    ID Numbers
    Open LibraryOL3336743M
    LC Control Number2004327305

    expected stock returns. Based on a novel high-frequency data set of almost one thousand stocks over two decades, we nd that the two rough betas associated with intraday discontinuous and overnight returns entail signi cant risk premiums, while the intraday continuous beta does not.


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Overnight stock returns and time-varying correlations Download PDF EPUB FB2

Overnight stock returns and time-varying correlations by Ajay Pandey,Indian Institute of Management edition, Microform in English. Time-varying correlation Overnight stock returns and time-varying correlations book the stock market returns across countries in the context of international investments has been well researched in the literature in last few years.

Overnight Stock. Abstract. Correlations between different asset returns represent a crucial element in assets allocation decisions and financial engineering.

In commodity markets, where prices result non stationary and returns are only mean stationary, a time varying measure of correlation has to be used. According to the prevailing literature, correlations among different markets are higher during Author: Rita Laura D'ecclesia, Denis Kondi.

volatilities. The model is used to regress the time-varying correlations against a constant, time trend and a dummy variable for the period of global financial crises. The model is of the form; U ij,t E 0 E 1 t E 2 D (6) t e it Where, E 0, E 1, and E 2 are regression coefficients.

U ij,t is the time varying correlation between stock returns. Further, multivariate analysis of Australian real estate and share market quarterly returns, spanning the period from the 3rd quarter to the 3rd quartersuggest that the correlation between real estate returns and share market returns is time-varying.

Finally, while all of the asset class correlation coefficients increased with the Cited by: This study investigates the time series properties of 5-minute, intraday returns of stock index and stock index futures contracts, and finds that SP and MM index futures returns tend to lead.

We attempt to better understand the varying correlations between stock and bond returns across countries and over sample periods using international data. The observation is that there are two forces that affect the correlation between stock and bond returns. The force that drives a positive correlation is identified as the income effect.

We employ both constant and time-varying correlation matrices for the t copulas and with the time-varying case the dependence structure of both returns depends on their previous dependence. Ajaya Kumar Panda, Swagatika Nanda, Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies, International Journal of Managerial Finance, IJMF, 14, 2, (), ().

A priori, the potential determinants of the time-varying stock and bond return correlation m a y b e d e d uc e d f ro m th e a s s e t p r i c i n g t h e o ry, w h i c h p o s t u l a t e s t h. economic growth expectations on the time-varying correlation between stock and bond returns. Similarly, Yang et al.

() employ monthly data from the U. and the U.   One of the best proxy for the stock market is the SPY etf. Heres the big number: 0, Thats the correlation between all nights and all intraday returns since SPY inception in Its slightly below zero.

It means the overnight session is not correlated at all with the intraday session. Its like a two uncorrelated stocks portfolio. Let R t be the return at time t: R t P t P t 1. Then we can rewrite the price series as: P 0, P 0 R 1, P 0 R 1 R 2,P 0 R 1 R T.

Imagine correlation calculated over these prices. The first return R 1 contributes to all the following entries and impacts every data point. ofexpected stock returns. Basedonanovelhigh-frequencydatasetofalmost1,stocks over two decades, we nd that the two rough betas associated with intraday discontinu-ous and overnight returns entail signicant risk premiums, while the intraday continuous beta does not.

These higher risk premiums for the discontinuous and overnight market. a strong negative correlation between variability in real stock prices and long-term interest rates of the U. and U. financial markets. Campbell () adopted an endogenous approach and decomposed stock and year bond returns.

He found that variability of stock and bond returns are conditional on future excess stock returns and inflation. returns are on average p. but the subsequent overnight returns average p. during Asian hours, p.

during European hours, and there is no reversal e ect between a. m - a. m ET. Thus, although market sell-o s and market rallies at U.

close are similar in. We investigate the dependence between the overnight (daytime) returns. The dependences between the overnight (daytime) returns are time-varying. The magnitude of the dependence decreases after the creation of the index futures.

Generally the dependences between daytime returns are larger than that of the overnight returns. I look at the prices every N market days, for each stock (where N is some number between 1 and ). I calculate the N-day returns, for each stock (that'd include daily, weekly, monthly etc.

I use these N-day returns to calculate the Correlation (and expect they won't be much different). Figure 1 presents the time-varying correlations for the US, the UK, Germany, France and Italy since data availability.

Second, infl ation has played a role in driving the correlation. We show this by evaluating the relative contribution of infl ation to the correlation, computing the correlation and the covariance between returns on stocks and.

Abstract: Dynamic average correlations of stock returns are predicted by the volatility of the market excess return and moving average returns of value, size and momentum portfolios.

While the influence of market volatility on average correlation is well-known, the role of value, size and momentum appears to be underappreciated. Time-varying Spillovers between IPI and Stock returns.

Notes: The left-column panels present the spillover from the IPI growth rates to US equity returns at low (α ), median (α ), and high (α ) quantiles.

The right ones show the corresponding spillovers from US equity returns. Over the long-term different types of stocks have varying correlations. Small-cap stocks, for instance, have a long-term correlation of with large-cap stocks, according to the Ibbotson SBBI.

factor data mining: 24 return factors forecast monthly stock returns Green Factor Models Stock quote: Fama and French (, FF92) measured the dimensionality of the cross-section of expected monthly U.

stock returns by regressing the potential factors beta, firm size, book-to-market, earnings-to-price and leverage. This paper addresses the relation between market risk and expected market returns under periodic trading breaks. We propose a model where asset prices are driven by a diffusive process that operates during the trading day and a separate process that captures overnight price changes.

Our empirical analysis shows that both components are important in explaining the equity market risk. The idea of allowing for time-varying market betas to help explain the cross-section of expected stock returns is related to the large literature on testing conditional versions of the CAPM.

8 In contrast to this literature, however, our empirical investigations should not be. The idea of allowing for time-varying market betas to help explain the cross-section of expected stock returns is related to the large literature on testing conditional versions of the CAPM.

8 In response to many of these studies,Lewellen and Nagel() have forcefully. Besides, to examine the impact of the creation of the CSI stock index futures on the dependence structure, we use the date (April, 16, ) when the index futures was launched to break the entire sample into two subsamples.

Our results show that the dependences between each pair of overnight (daytime) returns are time-varying. A time-varying volatility ratio thus also appears worthwhile for index returns and downside risk forecasting.

For indices, the time-varying volatility ratio model with infrequent updating (GAS roll- c t) generally performs the best, although it is sometimes slightly outperformed by the GAS tv- c t model or the HEAVY model.

economic variables, how correlations vary over time. 9 The analysis in Erb et al. shows that while there is some time variation in the correlations of the G7 coun-tries equity returns through time, the ranking of the correlations rarely changes.

That is, while there is variation in both the USUK and USJapan correlations. In this paper, we consider three methods for filtering pertinent information from a series of complex networks modelling the correlations between stock price returns of the DAX 30 stocks for the time period using the Thomson Reuters Datastream database and also the FNA platform to create the visualizations of the correlation-based networks.

These methods reduce the complete. Bates () nds that jump expectations in stock market returns change over time. Christo ersen et al. () nd that jump intensity is signi - cantly time-varying and that discrete-time models have better performances when incorporating jumps.

Yan () proxies the average jump size using. The long-run correlations between intraday or overnight returns are presented in Figure A. 7 in Linton and Wu (). Each subplot presents the averaged correlations between that individual stock and the remaining stocks.

The correlations exhibit an obvious upward trend during the sample period of Downloadable. We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two return series to have different properties.

We adopt a dynamic conditional score model with t-distributed innovations that captures the very heavy tails of overnight returns. We propose a several-step estimation procedure that captures the nonparametric slowly.

scaling would lead to cross-correlation variations, suggesting that the validity of dynamic correlations between two time series is conditional on a particular time scale.

Finally, we analyze scaling behaviors of the time series on returns to probe the stability of time series distributions. Following the scheme proposed by Mantegna and.

It provides a measure of condi- tional volatility in the presence of time-varying second-order moments, expressed as follows: rt ¼ l þ et ; where et jXt 1 N ð0; r2t Þ; ð1Þ r2t ¼ x þ ae2t 1 þ br2t 1 ; ð2Þ where rt is the stock return at interval t, l is a constant, et are the serially uncorre- lated errors (innovations) of stock.

To model this structure we use the functional-coefficient (FC) model of Cai, Fan, and Yao () where the coefficients are time-varying and dependent on the state of stock return volatility.

Out-of-sample forecast performances of the FC models and linear models where the coefficients are constant are also compared using the criteria of mean. Overnight stock returns, intraday returns, and firm-specific investor sentiment.

Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA. Investor Sentiment or Time-Varying Equity Premium. () estimated the time-varying conditional variance of returns, finding index evidence of mean-reversion and mlong emory in the betas.

More recently, Fama and French () extended the standard CAPM model to include additional factors five representing size, value, profitability, and investment patterns in average stock returns. ABSTRACT This paper investigates statistical properties of high-frequency intraday stock returns across various frequencies.

Both time series and panel data are utilized to explore the properties of probability distribution, dynamic conditional. This paper presents a novel, mixed-frequency based regression approach, derived from Functional Data Analysis (FDA), to analyze the effect of global crises on stock market correlations, using a long span of data, dating as far back as late s, thus covering a wide range of global crises that have not yet been examined in the literature in this context.

Correlations between changes in VIX and the stock index returns are also negative and significant; note the higher correlation coefficients for Canada at −Mexico at −and Brazil at −followed by Chile, Peru, Argentina, and Colombia with correlations ranging from −. Market correlation can be a great asset to anyone's stock trading portfolio.

It is crucial to know which markets and asset classes work best together for high reward and low risk opportunities. Just because two markets coincide with each other often, this does not make it suitable to use as correlation for upcoming trades.The present study attempts to track the transmission of volatility across major international stock markets over a span of 20 years, which includes both crisis (contagion form) and non-crisis periods.

It also investigates whether global transmission of volatility follows a pattern. The study uses bi-variate EGARCH model in order to capture spillover between a pair of stock markets and the.