Dow-Jones Index Research Articles

PENGARUH INDEKS REGIONAL TERHADAP JAKARTA ISLAMIC INDEX (JII)

The objectives of this study are to analyze the influence of IHSG, Dowjones, and Nikkei to JII. The data used in this study are monthly time series data from January 2006 – May 2012. Those data are JII, IHSG, Dowjones Index and Nikkei Index. Research method used in this study is Vector Error Correction Model (VECM). The cointegration test indicates that among research variables there is long term equilibrium and simultaneous relationship. The Empirical result of Impulse Response Function shown that the effect of IHSG, DowJones and Nikkei to JII are negative. The result on variance decomposition test had shown that the most effect of JII shock is influenced by JII itself. It can be conclude that Islamic Capital Market is more stable from the external shock rather than the conventional one.DOI: 10.15408/etk.v11i2.1888

Understanding Equity Option Prices

Principal component analysis of equity options on Dow-Jones …rms reveals a strong factor structure. The …rst principal component explains 66% of the variation in equity volatility level, 65% of the variation in the equity option skew and 61% of the implied volatility term structure across equities. Furthermore, the …rst principal component has an 88% correlation with S&P500 index option volatility, an 83% correlation with the index option skew, and a 66% correlation with the index option term structure. Based on these …ndings we develop an equity option valuation model that captures the cross-sectional market factor structure as well as stochastic volatility through time. The model assumes a Heston (1993) style stochastic volatility model for the market return but additionally allows for stochastic idiosyncratic volatility for each …rm. The model delivers theoretical predictions consistent with the empirical …ndings in Duan and Wei (2009). We provide a tractable approach for estimating the model on index and equity option data. The model provides a good …t to a large panel of options on stocks in the Dow-Jones index.

Mean-variance model based on filters of minimum spanning tree

This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree (MST) is proposed. Daily data of the 30 stocks of the Hang Seng Index (HSI) and Dow Jones Index (DJI) from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that (1) although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and (2) the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.

Estimating the algorithmic complexity of stock markets

Randomness and regularities in finance are usually treated in probabilistic terms. In this paper, we develop a different approach in using a non-probabilistic framework based on the algorithmic information theory initially developed by Kolmogorov (1965). We develop a generic method to estimate the Kolmogorov complexity of numeric series. This approach is based on an iterative “regularity erasing procedure” (REP) implemented to use lossless compression algorithms on financial data. The REP is found to be necessary to detect hidden structures, as one should “wash out” well-established financial patterns (i.e. stylized facts) to prevent algorithmic tools from concentrating on these non-profitable patterns. The main contribution of this article is methodological: we show that some structural regularities, invisible with classical statistical tests, can be detected by this algorithmic method. Our final illustration on the daily Dow-Jones Index reveals a weak compression rate, once well- known regularities are removed from the raw data. This result could be associated to a high efficiency level of the New York Stock Exchange, although more effective algorithmic tools could improve this compression rate on detecting new structures in the future.

Robust Backtesting Tests for Value-at-risk Models

Backtesting methods are statistical tests designed to uncover value-at-risk (VaR) models not capable of reporting the correct unconditional coverage probability or filtering the serial dependence in the data. We show in this paper that these methods are subject to the presence of model risk produced by the incorrect specification of the conditional VaR model and derive its effect in the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. We propose using resampling methods to implement robust backtests. Our experiments suggest that block-bootstrap outperforms subsampling methods in size accuracy. We carry out a Monte Carlo study to see the importance of model risk in finite samples for location-scale models that are incorrectly specified but correct on “average ”. An application to Dow–Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure. C52, C53, G32

Value at Risk – Matlab Application of Copulas on US and Indian Markets

Diversification globally has become a style more than a strategy and the need of the hour boils down to the efficient investment identification, better diversification of the risk, and better risk – adjusted return. One of the ways of such diversification is the investment in the indices, which has a portfolio of varied securities, globally. Such an investment immediately calls for a better exposure measurement to analyse the various investment parameters such as the portfolio variance, the Value at Risk, the associated return, the End Tail Loss and so on. This paper measures one such parameter, the Value at Risk (VaR) using the bivariate Gaussian Copula distribution implemented in MATLAB for the Dow-Jones index and the National Stock Exchange index. From a business point of view, with huge investments being made in India by Foreign Institutional Investors (FIIs) and vice-versa, this study holds much relevance with the risk identification and its mitigation. Retail investors are the least ones to be informed about the markets and this might serve as a platform for them to study their investment risk as a measure of VaR. For an academician the correlation, its dependence on the on copulas and its study would always be beneficial for risk management.

Analisi Hubungan Indeks Dow Jones, Indeks Hang Seng, Indeks Nikkei, Indeks Straits Times Dengan Indeks Harga Saham Gabungan

The aim of the present study was to investigate the relation between Dow Jones index, Hang Seng index, Nikkei index, Straits Times index and IHSG (the composite stock index of Indonesia Stock Exchange). Data were analyzed through use of correlation analysis. Results indicate that there is significant correlation analysis. Results indicate that there is significant correlation between dow jones index, Hang Seng index, Nikkei index, Straits Times index and IHSG. The Implications for future research are discussed.

Robust Backtesting Tests for Value-at-Risk Models

Backtesting methods are statistical tests designed to uncover excessive risk-taking from financial institutions. We show in this paper that these methods are subject to the presence of model risk produced by a wrong specification of the conditional VaR model, and derive its effect on the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. Our solution for the general case consists on proposing robust subsampling techniques to approximate the true sampling distribution of these tests. We carry out a Monte Carlo study to see the importance of these effects in finite samples for location-scale models that are wrongly specified but correct on average. An application to Dow-Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure.

Irving Fisher and Financial Economics: The Equity Premium Puzzle, the Predictability of Stock Prices, and Intertemporal Allocation Under Risk

Irving Fisher is renowned as the pundit who declared in October 1929 that stock prices appeared to have reached a permanently high plateau and who, having amassed a net worth of ten million dollars in the boom of the 1920s, proceeded to lose eleven million dollars of that fortune in the crash, which, as John Kenneth Galbraith (1977, p. 192) remarked, “was a substantial sum, even for an economics professor.” Along with the Dow-Jones index, Fisher's reputation for understanding financial markets declined relative to that of Roger Babson, the stock forecaster, amateur economist, and founder of Babson College, who presciently predicted the stock market crash of autumn 1929 (and, with less prescience, the stock market crashes of 1926, 1927, and 1928, and the stock market recovery of 1930). An editorial inThe Commercial and Financial Chronicle(November 9, 1929) declared of Fisher: “The learned professor is wrong as he usually is when he talks about the stock market” (quoted by Galbraith 1972, p. 151).

Multiple time scales and the empirical models for stochastic volatility

The most common stochastic volatility models such as the Ornstein–Uhlenbeck (OU), the Heston, the exponential OU (ExpOU) and Hull–White models define volatility as a Markovian process. In this work we check the applicability of the Markovian approximation at separate times scales and will try to answer the question which of the stochastic volatility models indicated above is the most realistic. To this end we consider the volatility at both short (a few days) and long (a few months) time scales as a Markovian process and estimate for it the coefficients of the Kramers–Moyal expansion using the data for Dow-Jones Index. It has been found that the empirical data allow to take only the first two coefficients of expansion to be non-zero that define form of the volatility stochastic differential equation of Itô. It proved to be that for the long time scale the empirical data support the ExpOU model. At the short time scale the empirical model coincides with ExpOU model for the small volatility quantities only.

Stochastic opinion formation in scale-free networks

The dynamics of opinion formation in large groups of people is a complex nonlinear phenomenon whose investigation is just beginning. Both collective behavior and personal views play an important role in this mechanism. In the present work we mimic the dynamics of opinion formation of a group of agents, represented by two states +/-1, as a stochastic response of each agent to the opinion of his/her neighbors in the social network and to feedback from the average opinion of the whole. In the light of recent studies, a scale-free Barabási-Albert network has been selected to simulate the topology of the interactions. A turbulent-like dynamics, characterized by an intermittent behavior, is observed for a certain range of the model parameters. The problem of uncertainty in decision taking is also addressed both from a topological point of view, using random and targeted removal of agents from the network, and by implementing a three-state model, where the third state, zero, is related to the information available to each agent. Finally, the results of the model are tested against the best known network of social interactions: the stock market. A time series of daily closures of the Dow-Jones index has been used as an indicator of the possible applicability of our model in the financial context. Good qualitative agreement is found.

Modelling the linkages between US and Latin American stock markets

This paper examines the linkages between US and Latin American stock markets during the 1995–2002 period using recently developed cointegration techniques that allow for structural shifts in the long-run relationship. Results suggest that when conventional cointegration tests are applied, a long-run relationship is found only in the cases of Brazil and Mexico for the Dow Jones (DJ) index, and in the case of Brazil for the Standard and Poor's 500 (SP500) index. In contrast, if the possibility of structural breaks is introduced, strong evidence is found in favour of such a relationship between the Argentine, Chilean and Venezuelan indices and the DJ index after the 1998 financial turmoil, and between the Brazilian and Mexican indices and the DJ index before such turbulence, while some marginal cointegration is detected between the Mexican and DJ indices from February 1998. Additionally, evidence is found of a cointegrating relationship between the Argentine, Chilean and Mexican indices and the SP500 index from August 1998, April 1999 and October 1999, respectively, and between the Brazilian and the SP500 indices before November 1997, as well as some marginal cointegration between the Mexican and SP500 indices before October 1999. The results suggest that the gains from international diversification for investors with long holding periods is limited.

Probability distribution of returns in the Heston model with stochastic volatility*

We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding Fokker-Planck equation exactly and, after integrating out the variance, find an analytic formula for the time-dependent probability distribution of stock price changes (returns). The formula is in excellent agreement with the Dow-Jones index for the time lags from 1 to 250 trading days. For large returns, the distribution is exponential in log-returns with a time-dependent exponent, whereas for small returns it is Gaussian. For time lags longer than the relaxation time of variance, the probability distribution can be expressed in a scaling form using a Bessel function. The Dow-Jones data for 1982-2001 follow the scaling function for seven orders of magnitude.

Modelling the Linkages Between US and Latin American Stock Markets

This paper examines the linkages between US and Latin American the stock markets during the 1995-2002 period using recently-developed cointegration techniques that allow for structural shifts in the long-run relationship. Our results suggest that, if we apply conventional cointegration tests, we only find a long-run relationship in the cases of Brazil and Mexico for the Dow Jones (DJ) index and in the case of Brazil for the Standard and Poor's 500 (SP500) index. In contrast, if we introduce the possibility of structural breaks, we find strong evidence in favour of such relationship between the Argentine, Chilean and Venezuelan indices and the DJ index after the 1998 financial turmoil, and between the Brazilian and Mexican indices and the DJ index before such turbulence, while some marginal cointegration is detected between the Mexican and DJ indices from February 1998. Additionally, we find evidence of a cointegrating relationship between the Argentine, Chilean and Mexican indices and the SP500 index from August 1998, April 1999 and October 1999, respectively, and between the Brazilian and the SP500 indices before November 1997, as well as some marginal cointegration between the Mexican and SP500 indices before October 1999. The results suggest that the gains from international diversification for investors with long holding periods is limited.

Finance: not all rocket science

Among the wide range of opportunities for physicists, the world of finance is certainly one of the most exciting. The trading of stocks and other financial products is becoming increasingly prominent in our society, with more and more news broadcasts devoted to financial information. Meanwhile, the Dow-Jones index of stocks and shares – together with the FTSE 100, the DAX and other financial indicators – are followed closely by a wide range of institutional and private investors. All in all, the financial sector represents a rapidly growing business area that is both fascinating and guaranteed to straddle international boundaries.

Are there psychological barriers in the Dow-Jones index?

The popular press attaches particular significance to certain numerical values of the Dow-Jones index. These magic numbers are referred to as ‘resistance levels’ or ‘psychological barriers’. In this note we examine 41 years of closing values of the Dow-Jones index to see if it is useful for predicting future stock market returns.

Communicating Financial Information Through Multidimensional Graphics

The power of graphs to convey relationships and changes in relationships between two variables is well accepted. In fact, it is common for anyone describing a relationship between variables to draw a graph depicting that relationship. Graphs also seem particularly useful in detecting changes in relationships. In the financial area, we see graphs of the Dow-Jones Index, unemployment rates, money supply, and of significant accounting numbers-income, rate of return, sales, etc. These items are graphed to help detect changes in trends not easily identified by standard statistical models. The major drawback of graphs has been the limitation of their use to describing the relationship between two variables. This paper examines a multidimensional graphic technique which may be useful for describing the financial status of a firm. Since it is a graphic technique, it can help individuals understand the relationships between financial variables and allow them to identify changes in the trend of firms' financial conditions more quickly than statistical models.

On the unstable behaviour of stock exchanges

The index. The simplest way of measuring the state of the market in a stock exchange is to choose some index, 1, such as the Dow-Jones index. Let J = i = dI/dt denote the rate of change of our chosen index. Then J = 0 represents a static market, while J > 0 represents a bull market and J 0 represents a floating revaluation and J < 0 a floating devaluation. The variable J can be regarded as a dependent variable, depending upon the rate of buying and selling of investors. At the same time there is a feedback because the knowledge of J in turn influences the investors, and this is what makes the dynamic interesting. To express this mathematically we need to introduce variables describing the activity of investors.

New York Bank Clearings and Stock Prices, 1866-1914

IN IIg an analysis of New York bank clearings and railroad and industrial stock prices for the period I903-I4 showed that these three speculative series displayed similar cyclical fluctuations, preceding corresponding fluctuations of outside bank clearings and money rates by several months.2 With the object of testing the synchronism of the cyclical fluctuations of the speculative series for a longer period of years, the study has been extended back to i866; and comparisons of the series for the entire period I866-I9I4 are set forth in this paper. For the period 1903-I4 the data underlying these comparisons were taken from the previous study. For the period I866-I902 new data were assembled. Monthly items for bank clearings for I866I902 were obtained from the Commercial and Financial Chronicle.3 In order to obtain a continuous series of monthly railroad stock prices beginning with I866, it was necessary to join three indexes. For the period I866-80 there was available the index of the prices of Io railroad common stocks previously published in this REVIEW.4 For the years I88I-96 no index was available and a new monthly index of the prices of Io railroad common stocks was constructed. This index is described in detail in the Appendix to this paper, pp. I93-98. For the remainder of the period under examination, we used the Dow-Jones index of railroad stocks, available from I897.5 These three indexes, which are on different bases, were adjusted to form a continuous series on the level of the Dow-Jones index by the use of factors based on overlapping data. Indexes of industrial stock prices were available in monthly form beginning with I872. For the years I872-96 the series compiled by the New York Federal Reserve Bank was available, and from I897, the Dow-Jones index.6 As the series for I872-96 was obtained by working backward from the Dow-Jones list of I2 stocks in I897, adjustment of the series for the two periods was unnecessary. No indexes of industrial stock prices were available earlier than I872 because of the lack of representative organized markets. Similarity in the cyclical fluctuations of the three speculative series would probably have been revealed by plotting them on the same logarithmic chart. But comparison would have been difficult at times owing to the diverging or crossing of the curves the result of varying trends. In New York bank clearings the general movement was upward throughout the period (Chart 2). A moderate, steady rate of growth persisted from i866 to I896, followed by a sharper upward trend during a transitional period which lasted until I903. Thereafter until the end of the period, the rate of increase was less marked. The series for railroad stock prices was subject to both changes in the character of the long-time movement and pronounced shifts of level. During the years i866 to about I882 the trend was uncertain; the succeeding movement, not far from horizontal, persisted until I897, and was followed by an abrupt transition in I897I903 to a markedly higher level, which was maintained through I9I4 (Chart 3). Industrial stock prices showed one sharp