Search

This Malaysia is a developing Islamic state that faced government budget deficit since 1998. It is undeniable that a budget deficit or inability to cover government spending is not positively seen by external parties. The optimum level of government budget is the state where government spending is totally offset by government revenue and that can be achieved through an increase in tax revenue or decrease in spending. The paper aims to discover the existence of a theoretical relationship between government spending and the different types of government revenues namely direct and indirect taxes and non-tax revenues. Furthermore, the paper tries to find out which of the different government revenues leads government spending. As well as to discover each revenue structure relationship with government spending using sample data from Malaysia for the period of 1970-2013 and time series techniques. The paper found out that although majority of government revenue is from direct tax revenue, the government spending only varies due to a change in indirect government tax revenue and non-tax revenue. In addition, it discovered that there is a long run relationship between the variables and that direct tax and government spending are endogenous (follower) variables, while non-tax revenue and indirect tax are exogenous (leader) variables. The paper also discussed the necessity of tax reform in Malaysia, since inefficiency in direct tax revenue leads to a dependence on non-tax revenue and regressive indirect taxes.

Increasingly Islamic financial institutions are being pressured by critics to offer profit and loss sharing (PLS) financing for the purpose of entrepreneurial development. We believe the growth of PLS can be incentivized by increasing the participation of Islamic asset managers and mutual funds in this sector. To achieve this we are conducting an exploratory study to link PLS investments with portfolio optimization opportunities for these asset managers. Through our studies we were able to determine that there was indeed a portfolio optimization opportunity for fund managers who invested in PLS investments over the long run. We conducted our analysis using recently established techniques of dynamic conditional correlation and modified discrete wavelet transformation. We hope this exploratory study will lay down the foundation for more studies to be conducted, to help industry practitioners and policy makers to adopt this product.

This paper makes an attempt to investigate the impact of selected macroeconomic variables on the level of deposits in the Islamic banking system. Malaysia is used as a case study. We apply ‘Auto – Regressive Distributive Lag’ model which has taken care of a major limitation of the conventional cointegrating tests in that they suffer from pre-test biases. Based on above rigorous methodology, we try to measure both long- and short-run relationships among these variables. By applying ARDL techniques, we find that the determinants such as, Inflation has strong impact on deposits of Islamic banking system while other macroeconomic variables GDP and Kuala Lumpur composite Index do not have significant impact. Most of the theories related to savings behaviour are not applicable to Islamic banking customers. Therefore, there is a possibility that religious belief might play an important role in the banking decisions of Muslim customers. The most relevant finding from the policy perspective is the significant negative effect of inflation on the Islamic banks’ saving deposits. Controlling inflation and thereby providing macroeconomic stability is essential for promoting Islamic banking.

Today, it is widely recognized that housing price boom-bust cycles lay at the heart of the latest global financial crisis. A housing boom is commonly defined as a period in which a housing price exceeds its fundamental value. Like most of the European Union member countries, many economies experienced the housing boom during the period of 2000–2006. Moreover, housing booms turned into busts in many countries at about the same period, causing a deep crisis. Our aim in this paper is to look for the determinants of housing price cycles and to investigate the relationship between housing boom-bust cycles and indicators of housing development. For this, we first detect the turning points of housing prices and identify housing price boom-bust cycles for 27 European countries and the US from 1995 to 2013 using quarterly data and a judgmentally augmented version of the dating procedure due to Ball (1994). Having obtained a categorization of boom versus boom-bust countries, in the second step, we reveal the relationships between housing cycles, macroeconomic factors and financial development by means of panel probit analysis.

WA multivariate stochastic volatility (MSV) model based on a Cholesky-type decomposition of the covariance matrix to model dynamic correlation in the observation and transition error as well as in cross leverage terms is proposed. The empirically relevant asymmetric concept of cross leverage is defined as a nonzero correlation between the ith asset return at time t and the jth log-volatility at time t+1. Volatilities and covariances are modeled separately, which makes an interpretation of leverage parameters straightforward. The model is applied on a three-dimensional portfolio consisting of the S&P 500 sector indices Financials, Industrials and Healthcare, spanning the recent financial crisis 2008/09. During and in the aftermath of market turmoil, increased cross leverage effects, higher unconditional kurtosis and stronger correlated information flow are observed. However, there is risk of overfitting and restricting time variation to elements governing dynamics of the observation error may be advisable.

An efficient estimator is constructed for the quadratic covariation or integrated co-volatility matrix of a multivariate continuous martingale based on noisy and nonsynchronous observations under high-frequency asymptotics. Our approach relies on an asymptotically equivalent continuous-time observation model where a local generalised method of moments in the spectral domain turns out to be optimal. Asymptotic semi-parametric efficiency is established in the Cram\'{e}r-Rao sense. Main findings are that nonsynchronicity of observation times has no impact on the asymptotics and that major efficiency gains are possible under correlation. Simulations illustrate the finite-sample behaviour., Comment: Published in at http://dx.doi.org/10.1214/14-AOS1224 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We introduce a methodology for dynamic modelling and forecasting of realized covariance matrices based on generalization of the heterogeneous autoregressive model (HAR) for realized volatility. Multivariate extensions of popular HAR framework leave substantial information unmodeled in residuals. We propose to employ a system of seemingly unrelated regressions to capture the information. The newly proposed generalized heterogeneous autoregressive (GHAR) model is tested against natural competing models. In order to show the economic and statistical gains of the GHAR model, portfolio of various sizes is used. We find that our modeling strategy outperforms competing approaches in terms of statistical precision, and provides economic gains in terms of mean-variance trade-o. Additionally, our results provide a comprehensive comparison of the performance when realized covariance and more efficient, noise-robust multivariate realized kernel estimator, is used. We study the contribution of both estimators across different sampling frequencies, and we show that the multivariate realized kernel estimator delivers further gains compared to realized covariance estimated on higher frequencies.

This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in ordinary clock time and in trade time. We show that when controlling for pre-scheduled market news events, trade-time returns of the highly liquid near-month E-mini S&P 500 futures contract are well characterized by a Gaussian distribution at very fine time scales. Second, we develop a structured and parsimonious model of clock-time returns by subordinating a trade-time Gaussian distribution with a trade arrival process that is associated with a modified Markov-Switching Multifractal Duration (MSMD) model. This model provides an excellent characterization of high-frequency inter-trade durations. Over-dispersion in this distribution of inter-trade durations leads to leptokurtosis and volatility clustering in clock-time returns, even when trade-time returns are Gaussian. Finally, we use our model to extrapolate the empirical relationship between trade rate and volatility in an effort to understand conditions of market failure. Our model suggests that the 1,200 km physical separation of financial markets in Chicago and New York/New Jersey provides a natural ceiling on systemic volatility and may contribute to market stability during periods of extremely heavy trading.

A high frequency stochastic volatility (SV) model is proposed. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump state is identified. Model selection and predictive tests show superiority of the regime switching extension in- and out-of-sample.

We suggest a simple and general approach to fitting the discount curve under no-arbitrage constraints based on a penalized shape-constrained B-spline. Our approach accommodates B-splines of any order and fitting both under the L-1 and the L-2 loss functions. Simulations and an empirical analysis of US STRIPS data from 2001-2009 suggest that an active knot search and splines of order three and four are mandatory to obtain reasonable fits. The loss function appears to be less relevant.

The economic process that Euro Zone countries have gone through created contradictory claims about whether those particular financial markets have converged, which has implications for the portfolio diversification issue for the investors and policy makers alike. Thus, this paper aims to examine whether there are opportunities for creating optimally diversified portfolios from the perspectives of the Islamic investors which is a missing point in the existing literature for the above-mentioned markets. The focus of the study is the correlations among the selected Shariah Indices‟ daily returns covering the period from April 2008 to March 2014, to illustrate not only the static picture, but the dynamic comovements which can be tested through the recent econometric methodologies (M-GARCH/DCC, MODWT, and CWT/WTC). Hence, the unique contribution of this research is the enhancement of the existing literature by empirically testing the „time-varying‟ and „scale dependent‟ volatilities and correlations of the particular markets by relying on the Shariah (Islamic) indices. By incorporating scale dependence, the paper is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons/holding periods of stock (e.g. weekly, monthly, quarterly, etc.) The overall result driven is that since there appears to be only continuous portfolio diversification opportunities between the French and Danish Islamic equity markets for up to six-month investment holding periods, in general, the possibility of portfolio diversification benefits for the short-, medium- and long-run does not seem to be available among these markets. This result implies that the Euro Zone financial markets are highly correlated/converged yielding minimal portfolio diversification benefits for all the heterogeneous investment horizons.

This paper analyzes the Shariah compliant nature of Islamic banks (IB) by using Time Series Multivariate Forecasting techniques to test the correlation and direction of causality between interest rates and IB financing . Islamic finance defines a 0% Interest rate, both on the asset and on the liability side. Thus, in a utopian Islamic financial system, any movement in interest rates should have no direct impact on any aspect of any Islamic bank. However, the supposition of IBs being genuinely Shariah compliant from a Credit Risk perspective has been challenged by many Shariah scholars. Using Malaysia as a test case, this paper measures changes in KLIBOR (Kuala Lampur Interbank Offer Rate) and tests them for correlations and directional causality with the IB Lending rate (used as a proxy measure for financing by Malaysian IBs). If a correlation and causality can be established between KLIBOR and financing by IBs, then it is an indication that IB’s may not be genuinely Shariah compliant. This research is original in that it attempts to relate an important issue of a fiqhi nature to data analysis, via some time series forecasting techniques. It also discusses the policy impacts of the results, and the subsequent risk faced by the Regulators in managing the Interest rate risks for a financial system structured on dual banking - Islamic and Conventional. The findings of the research tend to indicate a correlation and lead-lag causality relationship between Interest rate changes and Islamic bank financing.

Islamic Finance as an industry in recent times has been celebrated for its stability and resilience. With the philosophy of risk sharing and strict rules governing its activities to be in line with Islamic Law (the Shariah), the industry is seen as an alternative to the conventional finance with its tainted image of profit maximizing at any cost causing the Global Financial Crisis of 2008 - 2009. Given this claim it would be interesting to investigate the stability of the Islamic Financial Services Institutions (IFSIs) in comparison to the conventional sector. The Malaysian IFSIs were chosen as a case study as the Malaysia‟s Islamic Finance industry developed in the world with strict Shariah screening. As such, the Malaysian IFSIs are investigated to gain insights into their performance in terms of volatility and correlations with the market and then compared to their competitors by employing an M-GARCH t-DCC and also MODWT Wavelet technique to further dissect this volatility into their contributions from the point of view of different time scales. The findings are that IFSIs are much more volatile than their competitors with seemingly independent spikes in volatility unique to themselves but are low in correlation to the market implying that IFSIs volatility may be independent of the market due to assets that require the risk taking in order to justify earnings. IFSIs may need to cooperate in developing risk management standards and practices to mitigate risk that are unique to themselves as well as review the contracts and assets that may expose the IFSIs to too much risk.

We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models.

When regulating the financial system, the volatility phenomenon seems to emerge, practically, as a phenomenon which is intrinsic to the capital market behaviour. Theoretically, the leverage of the firms appears to be a major determinant of the volatility of prices and returns. At the same time, the leverage has also got a role at both levels: the capital structure of the firm and the investors’ strategy. We examine the return and volatility in relation to leverage by considering different sized portfolios constructed based on the firm’s level of debt and taken from a panel of 320 firms distributed over eight European countries and classified by their level of debt and their size. The optimal portfolio weights are computed for each quarter by maximizing the value of Sharpe ratio. We analyze the return, the volatility and the Value at Risk (VaR) based on different investors’ strategies with a view to taking into account the capital structure and the level of the debt of the firms. Our findings tend to indicate that in the case of two separate equity funds (low debt and high debt), the optimal portfolio is obtained for a weight with high low debt fund. Overall, the leverage seems to have a big role for the portfolio return, volatility and value at risk (VaR). The high leverage is indicative of having a big role in making worse the portfolio return and volatility under shocks. Finally, we explore the value of systematic risk in the case of several portfolio strategies based on high and low debt in regard to the benchmark index (the MSCI Europe index). The presence of these effects is further explored through the response of the model's variables to market-wide return and volatility shocks.

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators (QMLE). To date, the statistical properties of the QMLE of the DCC parameters have been derived under highly restrictive and unverifiable regularity conditions. The paper shows that the DCC model can be obtained from a vector random coefficient moving average process, and derives the stationarity and invertibility conditions. The derivation of DCC from a vector random coefficient moving average process raises three important issues: (i) demonstrates that DCC is, in fact, a dynamic conditional covariance model of the returns shocks rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate ARCH or GARCH model for DCC is based on the standardized shocks rather than the returns shocks. The derivation of the regularity conditions should subsequently lead to a solid statistical foundation for the estimates of the DCC parameters.

This paper seeks to close the gap of the lack of empirical evidence surrounding the different impact of conventional interest rates on Islamic finance components – Islamic stock markets, Islamic banking and Islamic insurance (called takaful). Such evidence remains imperative in order for the Islamic finance system to formulate effective countermeasures against changes in conventional interest rates. Using Malaysia as a case in point, this paper employs time-series techniques to establish long-run and causal relationships among an Islamic stock market, an Islamic bank stock, an Islamic insurance company stock, the overnight conventional interbank money market rate and several control variables. Results suggest the distinct interaction of each Islamic finance component with conventional interest rates – the positive long-run relationship and bidirectional causality between Islamic stock markets and conventional interest rates, the negative long-run relationship and bidirectional causality between Islamic banking and conventional interest rates, and the negative long-run relationship and unidirectional causality from Islamic insurance to conventional interest rates. Policymakers should remain concerned primarily with the impact of conventional interest rates on Islamic stock markets and Islamic banking due to the negative income gap of Islamic banks which expose the Islamic finance system to higher financial risk. Thus, policymakers should incentivize Islamic banks to convert the negative income gap into a positive income gap through imposing higher capital requirements on fixed-rate nominal assets.

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. In particular, a popular metric for comparing stochastic volatility models is the DIC based on the conditional likelihood—obtained by conditioning on the latent variables. However, some recent studies have argued against the use of the conditional DIC on both theoretical and practical grounds. We show via a Monte Carlo study that the conditional DIC tends to favor overfitted models, whereas the DIC calculated using the observed-data likelihood—obtained by integrating out the latent variables—seems to perform well. The main challenge for obtaining the latter DIC for stochastic volatility models is that the observed-data likelihoods are not available in closed-form. To overcome this difficulty, we propose fast algorithms for estimating the observed-data likelihoods for a variety of stochastic volatility models using importance sampling. We demonstrate the methodology with an application involving daily returns on the Standard & Poors (S&P) 500 index.

Référence interne : Working paper AMSE 2014-14; International audience; This paper simultaneously estimates the impact of political majorities on the values of firms that would benefit from the platforms of the two main candidates at the 2007 French presidential election, Ségolène Royal and Nicolas Sarkozy, and of those that are ruled or owned by Sarkozy's friends. We use prediction-market data to track each candidate's victory probability, and investigate how this relates to firms' abnormal returns. Our estimates suggest that the value of firms that would likely benefit from the platforms of Royal and Sarkozy changed by 1% and 2%, respectively, with the candidates' victory probabilities, and that firms connected to Sarkozy out-performed others by 3% due to his election.

In the recent years the topic about pollution of environment is quite popular. Many countries organize the government policy taking into account environmentally friendly policy. Maybe because of big share of the world pollution the USA organized not only the emission market, but also the RIN market where RIN is a 38-digit serial number, tax, security and investment. All actors of the RIN market can be divided into six groups: farmers, refiners, blenders, owners of fuel stations, EPA and private agencies. Models which can forecast are ARMA, ARMA-GARCH, GARCH-M and MS ARMA-GARCH. We identify that non-path-dependent MS AR(1)-GARCH-M(1,1) model cannot forecast better than AR(1)-t-GARCH(1,1) model, because it cannot forecast zero returns. Additionally, according to White’s test we identify than standard normal distribution is better then Student-t. At the same time our forecast of volatility using MS GARCH with standard normal distribution does not work the right way. In other words, forecasted volatility and returns are not fluctuated and also forecasted returns differ significantly from the real returns, especially, after the fourth period. Futhermore, we compare our price forecast with data which are presented by EPA (bid and ask prices). Using White test again, we find that the statistic is less in our case. In addition, the price does not change the way it should do, in other words, maybe we do not include a significant factor in our analysis.

This paper investigates and analyzes the long-run equilibrium relationship between the Thai stock Exchange Index (SETI) and selected macroeconomic variables using monthly time series data that cover a 20-year period from January 1990 to December 2009. The following macroeconomic variables are included in our analysis: money supply (MS), the consumer price index (CPI), interest rate (IR) and the industrial production index (IP) (as a proxy for GDP). Our findings prove that the SET Index and the selected macroeconomic variables are cointegrated at I (1) and have a significant equilibrium relationship over the long run. Money supply demonstrates a strong positive relationship with the SET Index over the long run, whereas the industrial production index and consumer price index show negative long-run relationships with the SET Index. Furthermore, in non-equilibrium situations, the error correction mechanism suggests that the consumer price index, industrial production index and money supply each contribute in some way to restore equilibrium. In addition, using Toda and Yamamoto's augmented Granger causality test, we identify a bi-causal relationship between industrial production and money supply and unilateral causal relationships between CPI and IR, IP and CPI, MS and CPI, and IP and SETI, indicating that all of these variables are sensitive to Thai stock market movements. The policy implications of these findings are also discussed.

Global reliance on Hydrocarbon sector has dramatically increased multi-fold which has led to rise to dominance of GCC. GCC is home to 45% of the proven oil reserves and contributes nearly 35% of the world oil exports annually. With such heavy reliance of the world on GCC for its oil produce, and the GCC’s economic reliance on Oil and Gas exports, the matter of world oil price shocks and its transmission to other areas of economy is of immense importance to the GCC economies. With Oil wealth accumulating in GCC with some estimates of over $500 billion, oil shocks and stock market volatility in GCC has emerged as a key concern area. Our paper focuses on understanding the short term and long term correlation between oil price shocks and GCC stock market’s volatility and the presence of any lead lag relationships. This study provides unique findings, different from earlier studies as we are able to analyze with non-linearity and least restrictive assumptions. The findings of this study are paramount for portfolio managers for their diversification benefit as well as timing of investment and divestment purposes.

The financial and banking crises around the world have prompted the regulators to revise, among others, the capital level of the banks to deal with the excessive risks taken by the banks, both conventional and Islamic. This study is the first attempt to investigate the relationship between risky assets and capital level in a mixed banking system applying the panel VECM and dynamic GMM estimators. The Malaysian mixed banking system is used as a case study taking panel data covering the period from December 2006 to October 2013. Our statistical results based on dynamic OLS (DOLS) tend to indicate that there is a positive relationship between the capital ratio (CAR) and risk weighted asset ratio (RWA) in the long run and also, the causality analysis based on panel VECM and two-step dynamic System GMM tends to indicate unidirectional causality in that the RWA is positively driven by CAR. Our results appear to suggest that higher capital buffer (excess capital above regulatory capital requirement) might have opened up more space for bank managers to taking risky positions while assisted by increasing domestic demand for credit facilities under favorable economic condition of Malaysia. In other words, high capital growth and capital buffer provides an extra cushion for Malaysian banks to pursue relatively riskier financing activities. For the full-fledge Islamic banks (IB) and Islamic bank subsidiaries (IBS), the existence of a cointegrating relationship between RWA and CAR suggests that the way the managers of Islamic banks behave towards risky assets follows the conventional practice.

An understanding of how volatilities of and correlations between commodity returns change over time including their directions (positive or negative) and size (stronger or weaker) is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is a humble attempt to add value to the existing literature by empirically testing the ‘time-varying’ and ‘scale dependent’ volatilities of and correlations of the sample commodities. Particularly, by incorporating scale dependence, it is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or holding periods. In order to address the research objectives, we have applied the vector error-correction test and several recently introduced econometric techniques such as the Maximum Overlap Discrete Wavelet Transform (MODWT), Continuous Wavelet Transform (CWT) and Multivariate GARCH – Dynamic Conditional Correlation. The data used in this paper is the daily data of seven commodities (crude oil, gas, gold, silver, copper, soybean and corn) prices from 1 January 2007 until 31 December 2013. Our findings tend to suggest that there is a theoretical relationship between the sample commodities (as evidenced in the cointegration tests) and that the oil, gold and corn variables are leading the other commodities (as evidenced in the Vector Error-Correction models). Consistent with these results, our analysis based on the application of the recent wavelet technique MODWT tends to indicate that the gold price return is leading the other commodities. From the point of view of portfolio diversification benefits based on the extent of dynamic correlations between variables, our results tend to suggest that an investor should be aware that the gas price return is less correlated with the crude oil in the short run (as evidenced in the continuous wavelet transform analysis), but due to its high volatility, it offsets its benefit of diversification in the long run and that an investor holding the crude oil can gain by including corn in his/her portfolio (as evidenced in the Dynamic conditional correlations analysis). Our analysis based on the recent applications of the wavelet decompositions and the dynamic conditional correlations helps us unveil the portfolio diversification opportunities for the investors with heterogeneous investment horizons or holding stocks over different periods.

How does the extent of integration of the Malaysian equity market with the equity markets of Japan and USA vary at different time scales? How dynamic is the extent of co-movement of equity price with the major macroeconomic indicators of Malaysia? In order to answer these two major issues, this study attempts to investigate the dynamic integration of the Malaysian equity market with the equity markets of Japan and USA, along with the three major macroeconomic control variables: exchange rate, consumer’s price index (CPI) and industrial production (IP) of Malaysia. The methodology applied initially used the standard time series techniques such as, Johansen cointegration technique, vector error correction model (VECM), variance decompositions (VDCs), followed by the application of the recent dynamic rolling cointegration, Beveridge-Nelson (BN) time series decompositions and finally, wavelet coherence of time-scale decompositions on monthly data starting from February,1990. The study finds one significant cointegrating relationship, which could be an indication of incomplete integration process as evidenced in the dynamic rolling cointegration approach. VECM and VDC indicate that the Malaysian equity market appears to be more influenced by the Japanese equity market and CPI of Malaysia. BN decompositions evidence almost simultaneous co-movement of permanent and transitory components of all variables and the co-movement appears to be closer during the financial crises. Finally, the wavelet coherence suggests closer co-movement of the Malaysian equity market with the Japanese equity market, which tends to vary according to different time scales. The findings of wavelet coherence on co-movement of the equity prices at different time scales tend to be different from those of the standard time series techniques such as, VECM and VDCs. The results of the study have strong policy implications.

An understanding of how volatilities of and correlations between crude oil and macroeconomic variables change over time including their directions and size is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is a humble attempt to add value to the existing literature by empirically testing for the ‘time-varying’ and ‘scale dependent’ correlations between selected commodities and selected macroeconomic variables taking Malaysia as a case study. Particularly, by incorporating the scale dependence, it is possible to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or stock-holding periods. Our findings tend to suggest that there is a theoretical relationship between the selected macroeconomic variables and the selected commodities and that the crude oil, gold, KLCI, CPI, BLR and T-bill are exogenous but the corn, industrial production and M2 are endogenous. Consistent with these results, our analysis based on the application of Generalised variance decompositions (VDCs) tends to indicate that the gold commodity is the most exogenous variable that drives the other commodities and the Malaysian macroeconomic variables. Finally, the value added stemming from the findings of Continuous Wavelet Transformation (CWT) tends to indicate that an investor who has exposure in crude oil commodity and wants to invest in KLCI, industrial production and treasury bill in Malaysia, should not hold his/her portfolio for more than 8 months in order to obtain diversification benefit.

The causal relations and dynamic interactions among macroeconomic variables and stock market index are important in the formulation of a country’s macroeconomic policy. In this study, to investigate the lead-lag relationship between stock market index and macroeconomic variables, we employ several conventional time-series techniques and a recently introduced method – wavelet analysis - to economics and finance. The data used in this paper is the monthly data of the selected macroeconomic variables such as (1) Kuala Lumpur Composite Index, (2) exchange rate, (3) inflation, (4) government bond yield, (5) short-term interest rate and (6) export over the period of January 1996 to September 2013. Our findings tend to suggest that a cointegrating relationship does exist between KLCI and selected macroeconomic variables. The results of the error correction model, the generalized variance decompositions as well as the wavelet cross-correlation analysis suggest that the short-term interest rate, KLCI and government bond yields are exogenous variables; especially, the short-term interest rate is the most leading variable. Policy makers may concentrate on the adjustment and control of the short-term interest rate in order to achieve the desired results for the target economic variables.

An understanding of how volatilities of and correlations between commodity returns and Islamic stock indices change over time including their directions (positive or negative) and size (stronger or weaker) is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is the first attempt to add value to the existing literature by empirically testing for the ‘time-varying’ and ‘scale dependent’ correlations between the selected Islamic stock indices of Gulf Cooperation Council (GCC) countries and selected commodities. Particularly, by incorporating scale dependence, it is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or holding periods. In order to address the research objectives, we have applied the vector error-correction model and several recently introduced appropriate wavelet decomposition techniques such as the Maximum Overlap Discrete Wavelet Transform (MODWT) and Continuous Wavelet Transform (CWT). The data used in this paper are the daily data of three commodities (crude oil, gold and corn) prices and Islamic stock indices from 1 June 2007 until 28 February 2014. Our findings tend to suggest that there is a theoretical relationship between the selected Islamic stock indices and selected commodities (as evidenced in the cointegration tests) and that the Islamic stock indices of Saudi Arabia, Oman and crude oil price are leading the other Islamic stock indices and the commodities (as evidenced in the Vector Error-Correction models). Our analysis based on the application of the recent wavelet technique MODWT indicates mixed results in that in the short run, the crude oil price is leading the other Islamic indices but in the long run, it is the other way around. From the point of view of portfolio diversification benefits, our results tend to suggest that an investor will obtain diversification benefit if his/her investment horizon is below 128 days (as evidenced in the continuous wavelet transform analysis). This result is consistent with the cointegration test that indicates that the diversification benefits for crude oil and the other Islamic indices is minimised in the long run because the variables under review tend to move together toward the same direction. Our analysis based on the recent applications of the wavelet decompositions helps us unveil the portfolio diversification opportunities for the investors with heterogeneous investment horizons or holding stocks over different periods.

This paper investigates the dynamic causal linkages in the daily returns among seven major conventional and Islamic stock indices in East Asia through the application of the time series techniques. We analyse seven conventional and Shariah -compliant stock indices (such as, FTSE Shariah China Index, Asia Shariah index, Malaysia EMAS Shariah Index, China SSE Composite Index, Hang Seng Index, Nikkei 225 and KOSPI) covering the period from 26 October 2007 to 1 March 2011. Essentially, the purpose of this research is to identify the extent of influence of conventional and Islamic, regional and international equity markets on Shariah-compliant equity investment in China. Our study is focused on investigating the following empirical questions: (i) which indices do the Shariah China Index commove with? (ii) which indices is the Shariah China Index Granger-causally related with ? and (iii) which major stock market was driving the selective conventional and Islamic markets? Our findings tend to suggest: (i) the Sharia China Index appears to have a theoretical and long-run comovement with all the select conventional and Islamic markets (as evidenced in the Cointegration and LRSM tests) (ii) the Shariah China Index is Granger-caused by all the conventional and Islamic markets (as evidenced in the VECM tests) (iii) Finally, what stands out is the leadership of the China conventional SSE market followed by the Malaysia Shariah market in driving all indices including the Sharia China index (as evidenced in the VDCs tests).

An understanding of how volatilities of and correlations between commodity returns and Islamic stock indices change over time including their directions and size are of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is the first attempt to add value to the existing literature by empirically testing for the ‘time-varying’ and ‘scale dependent’ volatilities of and correlations between the selected Islamic stock indices of South East Asian countries and selected commodities for enhancing portfolio diversification benefits. The methodologies appropriate to achieving the objectives were the recently introduced dynamic conditional correlations and wavelet decompositions. Our findings tend to suggest that there is a theoretical relationship between the selected Islamic stock indices and the selected commodities and that the Islamic stock indices of Singapore, Philippines and Indonesia are leading the other Islamic stock indices and the commodities (as evidenced in the Vector Error-Correction models). Consistent with these results, our analysis based on the application of the recent wavelet technique MODWT tends to indicate that the Singapore Islamic index is leading the other Islamic indices and the commodities. From the point of view of portfolio diversification benefits based on the extent of dynamic correlations between variables, our results tend to suggest that an investor should be aware that the Philippine Islamic stock index is less correlated with the crude oil in the short run (as evidenced in the continuous wavelet transform analysis) and that an investor holding the crude oil can gain by including the Malaysian Islamic stock index in his/her portfolio (as evidenced in the Dynamic conditional correlations analysis).

A major issue facing the investors in the financial markets of the contemporary world is to identify whether the observed stock market fluctuations are due mainly to contagion or fundamentals. This is due to the fact that if the fluctuations are mainly due to a contagion, then it is something like a ‘virus’ which would disappear after a few days. In contrast, if the fluctuations are mainly due to fundamentals, then it is something like a ‘pneumonia’ which is likely to continue for a long time. This study is the first attempt at testing whether there has been any contagion among the Shari’ah-compliant stock indexes during the most recent international financial crisis: the US subprime crisis of 2007-2009 and the Lehman Brothers collapse in 2008, with the application of a time-frequency decomposition technique known as ‘wavelet approach’ both in discrete and continuous forms recently imported to finance from engineering sciences. We analyze the daily data covering the period from June 2005 to December 2011 for the 18 MSCI conventional and Islamic stock market indexes of the Islamic (Malaysia, Indonesia, Turkey, GCC ex-Saudi) and non-Islamic countries (Japan, China, Korea, Taiwan and Hong Kong). Our study is focused on investigating the following empirical question: are the co-movements of selective stock markets normal (interdependent) or excessive (contagious) during the first and the second wave of the financial crisis? Our findings based on the time-frequency decomposition of wavelet approach are as follows: i) The wavelet correlation analysis indicates that, there is no clear evidence of contagion at any time-scales during the subprime mortgage crisis in USA; ii) However, during the collapse of Lehman Brothers, in all conventional stock indexes of non-Islamic countries except Japan, the wavelet correlation coefficients changed significantly at the first time-scale implying that there was a clear evidence of contagion at the first time-scale iii) But the conventional stock indexes of Islamic countries did not suffer from contagion excepting Indonesian MSCI stock index due to overlapping of confidence intervals iv) In all Islamic stock indexes in both Islamic and non-Islamic countries excepting China and Hong Kong Islamic MSCI indexes, the wavelet correlation coefficients changed insignificantly at all scales. Therefore, we fail to reject the null hypothesis implying that there was no clear evidence of contagion at all time-scales excepting China and Hong Kong. Additionally, for robustness, we studied the dynamic correlations between two continuous wavelet transforms through wavelet coherency analysis. The findings whether the stock comovements triggered by the financial crises were showing interdependence or contagion are plausible and intuitive and have implications for both the conventional and Shari’ah-compliant stock markets in terms of asset allocation strategy of risk managers and for policymakers’ optimal policy response to crises.

Our study measures co-movements in Islamic and conventional equity markets, to discover contagion and to measure integration level. We apply wavelet decomposition to unveil the multi-horizon nature of co-movement. We find that the subprime crisis generates fundamental-based contagion for both markets. The less exposure for some Islamic indices can be due to low leverage effect and the exclusion of conventional financial stocks. We also find higher fundamental integration for Islamic markets, attributable to their allocation related to the real sector. Finally, we show a leading role of the LIBOR negatively over Islamic indices in the long run.

This study is the first attempt to investigate the relationship between firm’s leverage and systematic risk for seven European countries in relation to Shari’ah (Islamic) stock screening. This paper also aims to examine the shock transmission through the systematic risk and whether less debt could bring more stability to the capital market. Using a dynamic panel technique based on VAR (Vector autoregressive) and dynamic GMM framework, we analyse the levered and the unlevered beta of the firm based on the firm characteristics before adding the country characteristic effects in order to take into account the heterogeneity across firms, which ensures the robustness of the results. We find that leverage is significantly and positively associated with systematic risk and that high leverage augments systematic risk, which is more affected by the nature of the European market rather than the firm characteristic effect. However, the existence of high leverage is indicative of having a big role in making worse the firm conditions under shocks. The presence of these effects is further explored through the responses of the model’s variables to market-wide volatility and shocks. Finally, the sensitivity to the market appears to be impacted by the financial crisis in terms of contagion in leverage with implications for portfolio diversification. Our findings have implications on the stability of firm’s risk exposure and the appropriate level of debt’s commitment to be made by managers.

Nowadays financial markets such as stock markets, gold and currency because of their significant returns are the investors’ main target. Their aim is to invest in a way that they can earn the highest profit. Among these markets, the stock market is of utmost importance since it deals with buying and selling the shares of diverse companies. Thus using the approaches that yield the highest profit and the lowest risk is the greatest priority of investors. This paper wants to calculate the chaotic indicators in different groups of Tehran’s stock market using Discrete Wavelet Transform. For this purpose, by utilizing the wavelet toolbox of Matlab software, Hurst exponent and Fractal Dimension and Predictability index of Tehran stock market’s groups time series were estimated. Results prove that almost all of the group’s time series are demonstrating Non-Gaussian behavior. And the type of time series’ memories whether they are short-term or long-term were identified. Furthermore, Predictability indices of time series were calculated which is also useful in investor’s decision making.

Theory suggests that physical commodity prices may exhibit nonlinear features such as bubbles and various types of asymmetries. This paper investigates these claims empirically by introducing a new time series model apt to capture such features. The data set is composed of 25 individual, continuous contract, commodity futures price series, representative of a number of industry sectors including softs, precious metals, energy, and livestock. It is shown that the linear causal ARMA model with Gaussian innovations is unable to adequately account for the features of the data. In the purely descriptive time series literature, often a threshold autoregression (TAR) is employed to model cycles or asymmetries. Rather than take this approach, we suggest a novel process which is able to accommodate both bubbles and asymmetries in a flexible way. This process is composed of both causal and noncausal components and is formalized as the mixed causal/noncausal autoregressive model of order (r, s). Estimating the mixed causal/noncausal model with leptokurtic errors, by an approximated maximum likelihood method, results in dramatically improved model fit according to the Akaike information criterion. Comparisons of the estimated unconditional distributions of both the purely causal and mixed models also suggest that the mixed causal/noncausal model is more representative of the data according to the Kullback-Leibler measure. Moreover, these estimation results demonstrate that allowing for such leptokurtic errors permits identification of various types of asymmetries. Finally, a strategy for computing the multiple steps ahead forecast of the conditional distribution is discussed.

The purpose of this paper is to test the growing converging views regarding the destabilizing and growthhalting impact of interest-based debt financial system. The views are as advocated by the followers of Keynes and Hyman Minsky and those of Islam. Islam discourages interest rate based debt financing as it considers that it is not conducive to productive activities and to human solidarity. Likewise, since the onset of the crisis of 2007/2008, calls by skeptics of mainstream capitalism has been renewed, to reconsider the dynamics of the prevailing financial system with emphasis on its untamed credit-creating capacity and link (or rather delink) to real sector transactions. The paper applies a threshold regression model to Malaysian data and finds that the relationship between growth and financial development is non-linear. A threshold is estimated, after which credit expansion negatively impacts GDP growth. While the post-threshold negative relationship is found to be statistically significant, the estimated positive relationship at lower levels of financial development is insignificant. The findings are hoped to provide insights to monetary authorities for better growth-promoting policy-making..

We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels, and a multivariate Student’s t distribution for the returns. Using generalized autoregressive score dynamics for the unobserved true covariance matrix, our approach automatically corrects for the effect of outliers and incidentally large observations, both in returns and in covariances. Moreover, by an appropriate choice of scaling of the conditional score function we are able to retain a convenient matrix formulation for the dynamic updates of the covariance matrix. This makes the model highly computationally efficient. We show how the model performs in a controlled simulation setting as well as for empirical data. In our empirical application, we study daily returns and realized kernels from 15 equities over the period 2001-2012 and find that the new model statistically outperforms (recently developed) multivariate volatility models, both in-sample and out-of-sample. We also comment on the possibility to use composite likelihood methods for estimation if desired.

We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels, and a multivariate Student’s t distribution for the returns. Using generalized autoregressive score dynamics for the unobserved true covariance matrix, our approach automatically corrects for the effect of outliers and incidentally large observations, both in returns and in covariances. Moreover, by an appropriate choice of scaling of the conditional score function we are able to retain a convenient matrix formulation for the dynamic updates of the covariance matrix. This makes the model highly computationally efficient. We show how the model performs in a controlled simulation setting as well as for empirical data. In our empirical application, we study daily returns and realized kernels from 15 equities over the period 2001-2012 and find that the new model statistically outperforms (recently developed) multivariate volatility models, both in-sample and out-of-sample. We also comment on the possibility to use composite likelihood methods for estimation if desired.

One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator of the EGARCH parameters are not available under general conditions, but rather only for special cases under highly restrictive and unverifiable conditions. It is often argued heuristically that the reason for the lack of general statistical properties arises from the presence in the model of an absolute value of a function of the parameters, which does not permit analytical derivatives, and hence does not permit (quasi-) maximum likelihood estimation. It is shown in this paper for the non-leverage case that: (1) the EGARCH model can be derived from a random coefficient complex nonlinear moving average (RCCNMA) process; and (2) the reason for the lack of statistical properties of the estimators of EGARCH under general conditions is that the stationarity and invertibility conditions for the RCCNMA process are not known.

The aim of the paper is to analyse the conditional dependence structure between precious metal returns using a copula-DCC-GARCH approach. Conditional correlation matrices are used to identify the states of the precious metals market by assuming that a given state of the market corresponds to a typical pattern of the conditional dependence structure. Cluster analysis allows for pointing at transition points between the market states, that is the points of drastic change in the conditional dependence structure. The application of the methodology described above to the period between 1997 and 2013 indicates three market states of four major precious metals (gold, silver, platinum and palladium). The results obtained reveal a sudden increase in dependencies between precious metals at the turn of April and May 2004.

As far as the author’s knowledge, the paper is the first attempt dedicated to understanding the risk and volatility of constituents of the young and rapidly growing Islamic mutual funds’ industry. The novelty of our approach lies in the usage of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between returns of funds of different sizes in a completely different way. Major part of economic time series analysis is done in time or frequency domain separately. Wavelet analysis can combine these two fundamental approaches, so we can work in time-frequency domain. Using wavelet coherence, we have gained valuable insights into the volatility and continuous dynamics of cross-correlations between small, medium and large size Islamic mutual funds.

We propose an estimation method that circumvents the path dependence problem existing in Change-Point (CP) and Markov Switching (MS) ARMA models. Our model embeds a sticky infinite hidden Markov-switching structure (sticky IHMM), which makes possible a self-determination of the number of regimes as well as of the specification : CP or MS. Furthermore, CP and MS frameworks usually assume that all the model parameters vary from one regime to another. We relax this restrictive assumption. As illustrated by simulations on moderate samples (300 observations), the sticky IHMM-ARMA algorithm detects which model parameters change over time. Applications to the U.S. GDP growth and the DJIA realized volatility highlight the relevance of estimating different structural breaks for the mean and variance parameters.

A review of the general state-space modeling framework. The discussion focuses heavily on the three prediction problems of forecasting, filtering, and smoothing within the state- space context. Numerous examples are provided detailing special cases of the state-space model and its use in solving a number of modeling issues. Independent sections are also devoted to both the topics of Factor models and Harvey’s Unobserved Components framework.

Portfolio selection and risk management are very actively studied topics in quantitative finance and applied statistics. They are closely related to the dependency structure of portfolio assets or risk factors. The correlation structure across assets and opposite tail movements are essential to the asset allocation problem, since they determine the level of risk in a position. Correlation alone is not informative on the distributional details of the assets. By introducing TEDAS -Tail Event Driven ASset allocation, one studies the dependence between assets at different quantiles. In a hedging exercise, TEDAS uses adaptive Lasso based quantile regression in order to determine an active set of negative non-zero coefficients. Based on these active risk factors, an adjustment for intertemporal correlation is made. Finally, the asset allocation weights are determined via a Cornish-Fisher Value-at-Risk optimization. TEDAS is studied in simulation and a practical utility-based example using hedge fund indices.

Financial risk management is difficult at the best of times, but especially so in the presence of economic uncertainty and financial crises. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic uncertainty, especiallythe power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy: the role of micro and macroeconomic factors, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices: a regime switching approach, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting: dynamic features and predictive variables, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk: an application of CoVaR, model-free volatility indexes in the financial literature: a review, robust hedging performance and volatility risk in option markets: application to Standard and Poor’s 500 and Taiwan index options, price cointegration between sovereign CDS and currency option markets in the global financial crisis, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the global financial crisis, managing financial risk in Chinese stock markets: option pricing and modeling under a multivariate threshold autoregression, managing systemic risk in The Netherlands, mean-variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.

This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student- t and GED distribution. According to findings of study, ARFIMA model do not support long memory behaviour for the stock market returns. However, FIGARCH model indicate that volatility of market returns has long memory. Moreover, in order to test the feature of long memory in the return and volatility of the stock market simultaneously, ARFIMA-FIGARCH models are estimated according to different distributions simultaneously. Predictable structure of volatility of Pakistan Stock Market display that this market is the weak-form market inefficiency. Consequently, it is possible to say that technical analysis related to this stock market may be valid. This implies that it is possible to predict future stock prices and extra ordinary gains could be obtained trading in this market.

We study variations in the risk-neutral distributions of the exchange rates in Brazil, Chile, Colombia, Mexico, and Peru due to interventions implemented by these countries. For this purpose, we first estimate the risk-neutral densities of the exchange rates based on derivatives market data, for one-day and one-week horizons. Second, using a linear regression model, we assess possible effects on the distributions of the expected exchange rates due to these interventions. We find little evidence of an effect on the expected exchange rates' means, volatilities, skewness, kurtoses, risk premia, and tails' parameters. In the few cases for which we do find some statistical evidence of an effect, it tends to be short-lived or not economically significant. On the other hand, we find evidence that interventions which objective is to restore and/or assure the proper functioning of exchange rate markets have a higher probability of success. This probability increases as the amount of resources to intervene at the disposal of the central bank increases. Needless to say, there are limits to the methodology we use.

We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationary or returns, are established. The model is applied to modeling the conditional covariance data of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covolatilities, betas and scenario-based risk measures, where the model's performance is particularly strong at short forecast horizons.

In theory, the price of equity is determined by the dividend yields and growth potentials of the firms. There exists established empirical proof of the impact of macroeconomic changes to the equity markets. With the advent of Islamic equities, and the recent surge of interest in them have raised the question of whether the same theoretical framework and relationship be considered for Shariah compliant equities or not. This study explores the impact of macroeconomic changes on Islamic and conventional indices for a large set of 37 countries, classifying them according to developed and emerging countries. The study finds a higher impact of Industrial production on the Islamic equities, while the interest rate and money supply have a lesser impact as compared to the impact on conventional counterparts. This lends support to the argument that Shariah screening methodology provides a set of Islamic equities which are more founded on the real sector of the economy. In addition the adjustment process during the crisis is faster for the Islamic equities in both regions. These results provide initial empirical proof for further research on the impact of specific economic variables on the changes in Islamic equity prices.

We use the copula approach to study the structure of dependence between sell-side analysts' consensus recommendations and subsequent security returns, with a focus on asymmetric tail dependence. We match monthly vintages of I/B/E/S recommendations for the period January to December 2011 with excess security returns during six months following recommendation issue. Using a symmetrized Joe-Clayton Copula (SJC) model we find evidence to suggest that analysts can identify stocks that will substantially outperform, but not underperform relative to the market, and that their predictive ability is conditional on recommendation changes., 15 pages, 1 figure