Daily Closing Values Research Articles

Effectiveness of the RMB exchange rate regime reform: A new perspective from MF-DMA and MF-X-DMA

This paper investigates the successes and flaws of China’s RMB exchange regime reform launched on August 11, 2015, from three perspectives: The market efficiency, interaction between the onshore and offshore RMB markets and the effectiveness of Chinese monetary policy. The dataset covers the period of June 27, 2011–April 20, 2018, and contains 1,435 observations for each series, including the daily closing values of the spot RMB exchange rate, 1-, 3-, 6-, and 9-month forward exchange rates, and 7-day Shanghai Interbank Offered Rate (Shibor). Our empirical results show that the reform of China’s exchange rate regime in 2015 has had both successes and flaws, which was not completely successful as a whole. The successful part of the reform consists of its contribution in enhancing the efficiency of the RMB exchange rate market, reducing the disturbance caused by monetary policy on the onshore market, and increasing the influence of monetary policy on the offshore market. The unsuccessful part of the exchange regime reform consists of its role in reducing the correlation between the onshore and offshore markets.

A Markov chain analysis for BIST participation index

This study addresses the trend estimation of the participation indices (PARTI) in the Istanbul Stock Exchange (BIST) using Markov chain (MC) theory. PARTI can be regarded as the Participation 50 Index (KAT50) and the Participation 30 Index (KATLM). Since KAT50 has only been calculated since 9th July 2014, there are only a few studies on this index. Therefore, in this study, we examine the PARTI indices. Firstly, we have employed MC method using 520 daily closing values of KATLM, between 1st July 2014 and 29th July 2016. For the KAT50 index, we used 514 daily closing values between 9th July 2014 and 29th July 2016, considering the states of these indices as increasing, decreasing or remaining stable. In order to perform a Markov chain analysis relating to prediction of the future situation, a transition probability matrix was created. Using this matrix, a steady-state analysis of the chain was performed and the future trends of KAT50-KATLM were forecasted successfully. It can be concluded that the results of this study are very helpful for individual and institutional investors’ investment decisions within global economies.

The Effect of Index Futures Contracts on Indices in Futures Markets

The floating exchange rate system has caused both interest rate and exchange rate risk. This situation required companies to focus on risk management. Measuring and managing risk has come to the fore. At this point, the person responsible for the finance of a firm must know how to use the derivatives and how they will be priced. Today, the importance of futures markets is very huge. The period in which they affect the spot market may also change in the country-based period. The aim of the study from developing countries like Turkey, India, Brazil and China, to examine the impact of the spot market on futures markets. In this context, the daily closing values of the mentioned markets and the end-of-day settlement prices of the futures contracts are used. Stability of series was examined in country basis, and the appropriate delay lengths were determined by establishing VAR model. In addition, the existence of any long term relation has been examined by using the Johansen cointegration test and the status of the relationship is determined by applying the Granger causality test. As a result, it has been determined that in India and China, the future markets have a positive effect on spot market whereas in Turkey and Brazil they have no effect. Keywords : Johansen Cointegration Test, Granger Causality, Futures market, Quantitive study, BIST30 DOI : 10.7176/JSTR/5-2-29

BİST ŞEHİR ENDEKSLERİNİN VOLATİLİTE YAPILARI VE REJİM DEĞİŞİMLERİNİN ANALİZİ

This study attempts to determine volatility and regime switching structure of BIST Cıty Indices over 2012-2017 period by using daily closing values. Three asymmetrical (EGARCH, TGARCH and PARCH) as well as two symmetrical (ARCH and GARCH) models were tested to reveal any asymmetrical conditions in comparing the volatilities and regime switching structure of XSADA, XSANT, XSANK, XSBAL, XSBUR, XSDNZ, XSIST, XSIZM, XSKAY, XSKOC, XSKON and XSTKR. For each model, three lagged values were calculated. TIC coefficients were used in comparing the models. The analyses of the volatility persistency reveal that XSKOC index is the most volatile and XSKAY index is the most stable according to remaining indices. The results of daily volatilities reveal that XSANT is the most volatile index while XSKOC is the most stable index. As a result of the analysis in order to determine regime structure of indices, two regimes were detected for all (12) indices which were taken into consideration. According to findings, the indices generally prefer to stay in higher regime if they are in the high regime and they tend to shift from low regime to the high regime if they are in low regime. XSBUR Index offers significant opportunities to the investors while staying 62.06 days in the high regime period whereas XSADA index was determined as the shortest high regime-staying period through only 9.41 days. XSIZM Index was determined as the worst index based on its duration (7.18 days) for staying in low regime. On the other hand, the XSKAY Index was detected as the shortest-staying index in low regime and fastest index escape from the decline trend.

A Study of Impact of Demonetization on Private and PSU Bank Indices

The present study is undertaken to examine demonetization effect on public and private sector banking, represented by Nifty Private Bank Index and Nifty PSU Bank Index in India. The data consists of daily closing values of Nifty Private Bank Index and Nifty PSU Bank Index, obtained from official website of National Stock Exchange. Impact of demonetization on banking sector is studied by comparing the data of Nifty Private Bank Index and Nifty PSU Bank Index for 30 trading days and 10 trading days before and after November 8, 2016 (event date). To study the statistical significance of the impact of demonetization decision, paired t-test and wilcoxon signed ranks test are used. The results exhibited that demonetisation decision has significantly reduced value of Nifty Private Bank Index. On the contrary, Nifty PSU Bank Index has significantly increased (10 days) after demonetization. But there is no significant increase in Nifty PSU Bank Index (30 days) after demonetization decision. It may be due to the reason that people have more confidence in PSU banks owing to government backing.

Forecasting NEPSE Index: An ARIMA and GARCH Approach

In this study, an attempt has been made to demonstrate the usefulness of univariate time series analysis as both an analytical and forecasting tool for Nepali stock Market. The data set covers the daily closing value of NEPSE index for two and half years starting from the middle of 2012 to end 2015. The forecasting analysis indicates the usefulness of the developed model in explaining the variations, trend and fluctuations in the values of the price index of Nepali stock exchange. Explanation of the fit of the model is described using the Correlogram, Unit Root tests and ARCH tests, which finally confirm that the ARIMA and EGARCH are good in forecasting and predicting daily stock index of Nepal. Furthermore, it is inferred that the daily stock price index contains an autoregressive, seasonal and moving average components; hence, one can predict stock returns through the identified models.

Week End Effect:Evidence from Indian Stock Market

The study provides the evidence for the presence of seasonality across the days of the week by using the percentage changes of daily closing values of two NSE indices, i.e. CNX 500 index and CNX Sharia 500 index for the period from 1st January 2010 to 31st December 2016. The Kruskall-Wall is nonparametric test (by computing 'H' statistic) is adopted in lieu of parametric one-way analysis of variance in this study to test the week form efficiency of the market. From the results it is observed the market may be inefficient during the short period of time, but in the long run it is efficient. Hence, the investors cannot outperform the market for a long period of time by investing on the particular day of the week.

Week End Effect: Evidence from Indian Stock Market

The study provides the evidence for the presence of seasonality across the days of the week by using the percentage changes of daily closing values of two NSE indices, i.e. CNX 500 index and CNX Sharia 500 index for the period from 1st January 2010 to 31st December 2013. The Kruskall-Wallis nonparametric test (by computing ‘H’ statistic) is adopted in lieu of parametric one-way analysis of variance in this study to test the week form efficiency of the market. From the results it is observed that there is no significant difference among the return of week days. However, the results indicate that the returns are leptokurtic distribution with the presence of highest variance on Friday. This clearly indicates that the market is more volatile in Friday than any other days.

A Forecast Comparison of GARCH Models and Implied Volatility

This research paper empirically investigates the performance of various models of volatility forecasting. Along with “unconditional standard deviation”, most commonly use measure of volatility, present research considered six others models of volatility forecasting, namely Standard GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, GJR-GARCH model, Exponential GARCH model (eGARCH), Asymmetric Power GARCH model (apGARCH), Component Standard GARCH model (csGARCH), and Option Implied Volatility model. To forecast volatility, the daily closing values of NSE Nifty 50 (Index) are considered from period 1st April 2002 to 31st March 2015, a span of 13 years. The present study found no single model as the best performing model. Overall performance found GJR-GARCH and option implied volatility having consistence performance. Interestingly, most commonly used model i.e. unconditional standard deviation found to be the worst model among all models studies in present research.

The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

<div><p><em>Stock market volatility is a measure of risk in investment and it plays a key role in securities pricing and risk management. </em><em>The paper empirically analyzes the relationship between India VIX and volatility in Indian stock market. </em><em>India VIX is a measure of implied volatility which reflects markets’ expectation of future short-term stock market volatility.</em><em> It is a volatility index based on the index option prices of Nifty. </em><em>The study is based on time series data comprising of daily closing values of CNX Nifty 50 index comprising of 1656 observations from March 2009 to December 2015. </em><em>The results of the study </em><em>reveal that India VIX has predictive power for future short-term stock market volatility. It has higher forecasting ability for upward stock market movements as compared to downward movements. Therefore, it is more a bullish indicator. Moreover, the accuracy of forecasts provided by India VIX is higher for low magnitude future price changes relative to higher stock price movements. The current value of India VIX is found to be affected by past period volatility up to one month and it has forecasting ability for next one-month’s volatility which means the volatility in the Indian stock markets can be forecasted for up to 60 days period. </em></p></div>

EURO EXCHANGE RATE FORECASTING WITH DIFFERENTIAL NEURAL NETWORKS WITH AN EXTENDED TRACKING PROCEDURE

This paper is aimed at developing a new kind of non-parametrical artificial neural network useful to forecast exchange rates. To do this, we departure from the so-called Differential or Dynamic neural Networks (DNN) and extend the tracking procedure. Under this approach, we examine the daily closing values of the exchange rates of the Euro against the US dollar, the Japanese yen and the British pound. With our proposal, Extended DNN or EDNN, we perform the tracking procedure from February 15, 1999, to August 31, 2013, and, subsequently, the forecasting procedure from September 2 to September 13, 2013. The accuracy of the obtained results is remarkable, since the percentage of the error in the predicted values is within the range from 0.001% to 0.69% in the forecasting period.

Volatility Spill-over between the JSE/FTSE Indices and the South African Rand

In this paper the inter-market relationship between the South African Rand and the 5 main indices of the Johannesburg Stock Exchange was investigated. The dataset used in this paper was the daily closing values, from January 2002 to end September 2014. A multivariate CCC-GARCH (1,1) model was used to test the spill-over effects and the impact of shocks into both market. The Rand is more volatile to market shocks compared to the JSE/FTSE's All Share Index (J203), Top 40 Index (J200), Midcap Index (J201), but less volatile to market shocks than JSE/FTSE's Small Cap Index (J202) and Fledgling Index (J204).

Monitoring procedure for parameter change in causal time series

We propose a new sequential procedure to detect change in the parameters of a process X=(Xt)t∈Z belonging to a large class of causal models (such as AR(∞), ARCH(∞), TARCH(∞), or ARMA–GARCH processes). The procedure is based on a difference between the historical parameter estimator and the updated parameter estimator, where both these estimators are quasi-likelihood estimators. Unlike classical recursive fluctuation test, the updated estimator is computed without the historical observations. The asymptotic behavior of the test is studied and the consistency in power as well as an upper bound of the detection delay is obtained. Some simulation results are reported with comparisons to some other existing procedures exhibiting the accuracy of our new procedure. This procedure coupled with retrospective tests is applied to solve off-line multiple breaks detection in the daily closing values of the FTSE 100 stock index.

Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales

This paper uses a differential neural network (DNN) to describe the behavior of daily closing values of German DAX and USA S&P 500 stock indices between July 3, 2000 and January 13, 2012. Then, by the use of DNN a four-week forecast is performed of the daily closing values of these indices, from January 16 to February 10, 2012. The results obtained confirm that the differential neural networks can become one of the most powerful and accurate tools to predict future values of financial assets.

An empirical study on weak market efficiency of Karachi Stock Exchange

A lot of research has been conducted to check whether the Frontier and emerging markets show weak form of efficiency or follow a random walk. Karachi Stock Exchange, being a frontier market, was tested on the basis of daily closing values from 2006 to 2011. The study showed KSE to be a weak form inefficient market following non randomness. Three tests were applied on the data. KSE proved to have a unit root, the values in the series showed a strong correlation. Moreover, a Z-statistic value was far greater than 1.96 which proved KSE to be an inefficient market. Inefficient markets have several implications for international as well as local investors.

The Use of Statistical Tests to Calibrate the Black-Scholes Asset Dynamics Model Applied to Pricing Options with Uncertain Volatility

A new method for calibrating the Black-Scholes asset price dynamics model is proposed. The data used to test the calibration problem included observations of asset prices over a finite set of (known) equispaced discrete time values. Statistical tests were used to estimate the statistical significance of the two parameters of the Black-Scholes model: the volatility and the drift. The effects of these estimates on the option pricing problem were investigated. In particular, the pricing of an option with uncertain volatility in the Black-Scholes framework was revisited, and a statistical significance was associated with the price intervals determined using the Black-Scholes-Barenblatt equations. Numerical experiments involving synthetic and real data were presented. The real data considered were the daily closing values of the S&P500 index and the associated European call and put option prices in the year 2005. The method proposed here for calibrating the Black-Scholes dynamics model could be extended to other science and engineering models that may be expressed in terms of stochastic dynamical systems.

Analysis on Runs of Daily Returns in Istanbul Stock Exchange

The aim of this paper is to obtain some statistical properties about runs of daily returns of ISE30, ISE50 and ISE100 indices and compare these results with the empirical stylized facts of developed stock markets. In this manner, all time historical daily closing values of these indices are studied and the following observations are obtained; exponential law fits pretty well for the distribution of both run length and magnitude of run returns. Market is equally likely to go up or go down everyday. Market depth has improved over recent years. Large magnitudes of run returns are more likely to be seen in positive runs. As in the developed stock markets, daily returns in Istanbul Stock Exchange don’t have significant autocorrelations but absolute values (i.e. magnitudes) of daily returns exhibit strong and slowly decaying autocorrelations up to several weeks suggesting volatility clustering. Similar to the absolute daily returns, absolute value of run returns display strong and slowly decaying autocorrelations which again supporting the existence of volatility clustering. Unlike magnitudes of run returns, lenghts of runs don’t have significant autocorrelations.

A Fusion Model Integrating ANFIS and Artificial Immune Algorithm for Forecasting Indian Stock Market

Stock market forecasting provides challenging and interesting task to both investors and academic researchers because trading decision at an appropriate time makes more profit for investors. In present study, a new approach has been proposed to integrate Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Immune Algorithm (AIA) for predicting the future index value of National Stock Exchange (NSE) of India. In order to make an efficient forecasting model, ANFIS is employed to optimize decision-making process and an efficient artificial immune algorithm is adopted to adjust membership function parameters of Fuzzy Inference System (FIS). The proposed system was simulated using daily closing value of NSE Nifty data and well-known technical indicators as input data values and output is the predicted future index value of NSE Nifty. Simulation results of our fusion model have been compared with other soft computing models and actual NSE Nifty data as benchmark. The experimental results showed that the proposed forecasting model yielded significantly higher forecasting accuracy values than other forecasting models.

Are stock prices in the US nonstationary? Evidence from contemporary unit root tests

This article extends the empirical literature on the efficiency of stock markets in the US by applying a battery of unit root tests to empirically ascertain whether stock prices are mean reverting. This article, unlike previous studies, employs a disaggregated approach using the daily closing values of the Dow Jones industrial average, NASDAQ composite and S&P 500 index covering the period 5 February 1971 to 31 December 2009 to investigate the integration properties of the US stock market. The empirical findings reveal that the three major stock price series are nonstationary, indicating that they do not follow a trend path. The primary implication is that trading strategies that simply rely on mean reversion of stock prices are valueless.

Suitability of Volatility Models for Forecasting Stock Market Returns: A Study on the Indian National Stock Exchange

Problem statement: Measuring volatility is an important issue for stock market traders. Also, volatility has been used as a proxy for riskiness associated with the asset. This study aims to compare the different volatility models based on how well they model the volatility of the India NSE. Approach: The study has made use of five models which are Historical/Rolling Window Moving Average Estimator, (ii) Exponentially Weighted Moving Average (EWMA), (iii) GARCH models, (iv) Extreme Value Indicators (EVI) and (v) Volatility Index (VIX). The data includes the daily closing, high, low and open values of the NSE returns from 2005-2008. The model comparison was done on how well the models explained the ex-post volatility. Wald’s constant’s test was used to test which method best suited the requirements. Results: It was concluded that the AGARCH and VIX models proved to be the best methods. At the same time Extreme Value models fail to perform because of the low frequency data being used. Conclusions: As other research suggests these models perform best when they are applied to high frequency data such as the daily or intraday data. EVIs give the best forecasting performance followed by the GARCH and VIX models.