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Nowcasting volatility of financial time series appears difficult with classical volatility models. This paper proposes a simple model, based on an ARMA representation of the log-transformed squared returns, that allows to estimate current volatility, given past and current returns, in a very simple way. The model can be viewed as a degenerate case of the stochastic volatility model with perfect correlation between the two error terms. It is shown that the volatility nowcasts do not depend on this correlation, so that both models provide the same nowcasts for given parameter values. A simulation study suggests that the ARMA and SV models have a similar performance, but that in cases of moderate persistence the ARMA model is preferable. An extension of the ARMA model is proposed that takes into account the so-called leverage effect. Finally, the alternative models are applied to a long series of daily S&P 500 returns.

The authors perform an original research on the fundamentals of winning virtuous strategies creation toward the leveraged buyout transactions implementation during the private equity investment in the conditions of the resonant absorption of discrete information in the diffusion - type financial system with the induced nonlinearities at the influences by the Schumpeterian creative disruption processes in the free market economy. We propose that the money is a financial computing process, which is executed by the operating system, representing an exchange medium, at a computing device. We make a comprehensive academic literature review on the various aspects of modern financial engineering approaches to make the private equity investments and design the leveraged buyout and venture capital firms, funds and transactions in the finances. We highlight a private equity’s important role in the Schumpeterian creative destruction processes in the free market economy, discussing the leverage buyout transactions process and properties. We develop the MicroLBO software: 1) to create the winning virtuous strategies toward the LBO transactions implementation, and 2) to compute the direct/reverse LBO transaction probability number for the selected public/private companies. We formulate the Ledenyov theory on the winning virtuous strategies creation toward the LBO transactions implementation at the resonant absorption of discrete information in the diffusion - type financial economic system with the induced nonlinearities.

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. In order to account for both the skewness and the excess kurtosis in returns, we combine the BEKK model from the multivariate GARCH literature with different multivariate densities for the returns. The set of distributions we consider comprises the normal, Student, Multivariate Exponential Power and their skewed counterparts. Applying this framework to a sample of ten assets from the Dow Jones Industrial Average Index, we compare the performance of equally- weighted portfolios derived from the symmetric and skewed distributions in forecasting out-of-sample Value-at-Risk. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures. The results unanimously show that the inclusion of fat-tailed densities into the model specification yields more accurate VaR forecasts, while the further addition of skewness does not lead to significant improvements.

This study explores the volatility models and evaluates the quality of one-step ahead forecasts of volatility constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test for correct unconditional coverage and index- Dependence of violations using Likelihood Ratio tests. The tests suggest that VaR forecasts at 90 % and 95% have desirable properties. Regarding 99% VaR forecasts, We find significant evidence that suggests none of the models can reliably predict at this confidence level.

This study aims to describe the size distribution of Portuguese firms, as measured by annual sales and total assets, between 2006 and 2012, giving an economic interpretation for the time evolution of the distribution. Three distributions are fitted to data: the lognormal, the Zipf and the Simplified Canonical Law (SCL). Methods of estimation include Maximum Likelihood, modified Ordinary Least Squares in log-log scale and Nonlinear Least Squares considering the Levenberg-Marquardt algorithm. Lognormal and Zipf distributions can be justified from the Gibrat's law and, in Zipf case a constraint must be added, for instance, on the minimum size of firms. As for SCL, firstly presented by Mandelbrot in the context of linguistics, the argument used to deduce the distribution of words frequency in texts is adapted to the distribution of firms. This is done by defining production units which can be glued to constitute firms so that the network of firms can be used flexibly to satisfy the most diverse needs in the economy in the less costly way and reducing at the maximum the delay in achieving it. For this distribution, different interpretations of the estimated parameters found in the literature are confronted and discussed in the light of data. Using this interpretation, we attempt to find a characterization of the Portuguese economic activity rhythm, diversity of activity sectors and competition. Diversity is captured by an entropy measure at different levels of aggregation and not just at the higher level as is usually done in literature. The interrelations between these economic characteristics and others, such as the level of concentration of firm size distribution, are reassembled from literature and confronted. Analyzing entropy at different levels of aggregation allows verifying how the evolution of activity rhythm can have a distinct impact in diversity at those different levels.

This paper introduces several new Bayesian nonparametric models suitable for capturing the unknown conditional distribution of realized covariance (RCOV) matrices. Existing dynamic Wishart models are extended to countably infinite mixture models of Wishart and inverse-Wishart distributions. In addition to mixture models with constant weights we propose models with time-varying weights to capture time dependence in the unknown distribution. Each of our models can be combined with returns to provide a coherent joint model of returns and RCOV. The extensive forecast results show the new models provide very significant improvements in density forecasts for RCOV and returns and competitive point forecasts of RCOV.

Traditionally, the main role of the bank is to offer loans to its customers, to facilitate the intermediary role in the financial market between the investors and feed the need of the big corporations in terms of investment. This study is an attempt to analyze, at the same time, the impact of three factors that are involved in the income of the European banks. The first two are endogenous to the bank and the third one is deemed, a priori, to be exogenous to the bank. Our objective is to look at the influence of “Fee & commissions”, the “Net Non-interest income” and the interest rate on banks’ profitability in a panel data of 34 banks chosen from different European countries. The interest rate of reference is supposed to be under the control of the central bank but subject to movement due to the interactions between the cross-border countries and the competitive framework within the same country. The “Fee & commissions” and the “Net Non-interest income” are more related to the efficiency of the management team and the effectiveness of the processing inside the same bank. Our main finding is that the “Fee & commissions” are not really influencing the profitability of the European banks. However, the “Net Non-interest income” and the interest rate are significantly impacting the profitability of the European banks.

We study whether it is possible to find optimal hedge ratios for a foreign currency bond portfolio to lower significantly the risk and increase the risk adjusted return of a portfolio. The analysis is conducted from the perspective of euro area based investors to whom short-selling restrictions might apply. The ordinary least squares approach is challenged with the optimal hedge ratios found by the DCC-GARCH approach in order to investigate whether time-varying hedging is superior to the standard constant hedge ratios found by OLS. We find that hedging significantly lowers the portfolio risk in domestic currency terms and improves the Sharpe ratios for both single instrument and equally weighted multi asset portfolios. Optimal hedging using the standard OLS approach and using time-varying hedging give similar results, the latter being superior to the first in terms of risk-adjusted return.

This paper is the first to draw a global picture of worldwide microfinance equity by taking full advantage of daily quoted prices. We revisit previous findings showing that investors should consider microfinance as a self-standing sector. Our results are threefold. First, microfinance has become less risky and more closely correlated with the financial sector. This convergence might be followed by a decline in the proportion of women borrowers. Second, microfinance and finance shares have equivalent currency exposure. Last, introducing a self-standing microfinance sector presents few diversification benefits. This paper confirms that microfinance has changed dramatically during the last decade., SCOPUS: ar.j, info:eu-repo/semantics/published

Hedonic price indices are currently considered to be the state-of-the-art approach to computing constant-quality price indices. In particular, hedonic price indices based on imputed prices have become popular both among practitioners and researchers to analyze price changes at an aggregate level. Although widely employed, little research has been conducted to investigate their asymptotic properties and the influence of the econometric model on the parameters estimated by these price indices. The present paper therefore tries to fill the actual knowledge gap by analyzing the asymptotic properties of the most commonly used imputed hedonic price indices in the case of linear and linearizable models. The obtained results are used to gauge the impact of bias adjusted predictions on hedonic imputed indices in the case of log-linear hedonic functions with normal distributed errors.

Hundreds of papers and hundreds of factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make any economic or statistical sense to use the usual significance criteria for a newly discovered factor, e.g., a t-ratio greater than 2.0. However, what hurdle should be used for current research? Our paper introduces a multiple testing framework and provides a time series of historical significance cutoffs from the first empirical tests in 1967 to today. Our new method allows for correlation among the tests as well as missing data. We also project forward 20 years assuming the rate of factor production remains similar to the experience of the last few years. The estimation of our model suggests that a newly discovered factor needs to clear a much higher hurdle, with a t-ratio greater than 3.0. Echoing a recent disturbing conclusion in the medical literature, we argue that most claimed research findings in financial economics are likely false.

We study the real-time characteristics and drivers of jumps in option prices. To this end, we employ high frequency data from the 24-hour E-mini S&P 500 options market. We find that option prices do not jump simultaneously across strikes and maturities and are uncorrelated with jumps in the underlying futures price. 14% to 28% of detected option price jumps occur around scheduled news releases. However, it is illiquidity rather than the news content that drives jumps. Evidence suggests that option traders increase bid-ask spreads to account for trading against investors who are skilled processors of public releases.

We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.

Previous studies have investigated the interdependence of Malaysian stock market with its major trading partners without taking into account the time-varying correlations and different investment horizons of the investors. The main objective of this paper is to study the extent to which the Malaysian Shari’ah (Islamic) investors can benefit from portfolio diversification with the Shari’ah indices of its major trading partners (China, Singapore, Japan, United States and Thailand). The recent Multivariate GARCH Dynamic Conditional Correlation, the Continuous Wavelet Transform and the Maximal Overlap Discrete Wavelet Transform are applied. Findings tend to indicate that the Malaysian Shari’ah investors who have allocated their investments in major trading partners like China and Singapore may not reap great diversification benefits for almost all investment horizons but may reap moderate benefits arising from Thailand and Japan up to the investment horizons of 32-64 days and longer. The evidence suggests that the portfolio diversification benefits are greater if the Malaysian Shari’ah investors invest in the US Shari’ah stock index excepting the long investment horizons. The stock holding periods exceeding 32 to 64 days contain minimal benefits of portfolio diversification. As an implication, the Malaysian Shari’ah investors should carry out the reassessment of their stock exposures and investment horizons more frequently.

The focus of this paper is to investigate the potential for portfolio diversification strategies based on investing across international markets or economic sectors, using Malaysia as a case study. Analysing the comovement and correlation between returns and volatilities of the different markets or assets, therefore, is the key to gauge the potential benefits from diversification. Two important features of the comovement are their dynamic fluctuations across time period and time horizon or scales. Thus, the paper applies recent techniques of multivariate volatility modelling and wavelet transform, which can analyse time series over both the time and frequency domain. Our findings suggest that there are potential for gains from portfolio diversification strategies into both international markets, as well as sectors of the domestic stock market. There are international stock markets and domestic sectors which have low correlations and comovement with the Kuala Lumpur Composite Index. The low correlation makes it ideal to diversify the portfolio and reduce the overall investment risks. However, the findings also noted that the correlations vary across time and scales. Hence, fund managers need to be aware of the dynamics which may change at any particular point in time, which may affect the portfolio risks.

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). The underlying stochastic specification to obtain GARCH was demonstrated by Tsay (1987), and that of EGARCH was shown recently in McAleer and Hafner (2014). These models are important in estimating and forecasting volatility, as well as in capturing asymmetry, which is the different effects on conditional volatility of positive and negative effects of equal magnitude, and purportedly in capturing leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. As there seems to be some confusion in the literature between asymmetry and leverage, as well as which asymmetric models are purported to be able to capture leverage, the purpose of the paper is three-fold, namely, (1) to derive the GJR model from a random coefficient autoregressive process, with appropriate regularity conditions; (2) to show that leverage is not possible in the GJR and EGARCH models; and (3) to present the interpretation of the parameters of the three popular univariate conditional volatility models in a unified manner.

We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the hedged asset and among themselves. We find that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is defined as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the infinite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average of correlation and cointegration parameters. The results are general and can be applied for any portfolio of assets that can be modeled by the CVAR of any rank and order.

We re-examine the dynamics of returns and dividend growth within the present-value framework of stock prices. We find that the finite sample order of integration of returns is approximately equal to the order of integration of the first-differenced price-dividend ratio. As such, the traditional return forecasting regressions based on the price-dividend ratio are invalid. Moreover, the nonstationary long memory behaviour of the price-dividend ratio induces antipersistence in returns. This suggests that expected returns should be modelled as an ARFIMA process and we show this improves the forecast ability of the present-value model in-sample and out-of-sample.

We build on the work in Fackler and King 1990, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time dependence between densities with the same maturity, and for dependence across maturities at the same point in time. This approach to the problem encompasses model flexibility, parameter parsimony and, more importantly, information pooling across densities., 27 pages, 4 figures

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm. A particular feature of our approach is that smoothed estimates of the states and the marginal likelihood are obtained directly as an output of the algorithm. Our method provides a computationally efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock returns and corporate default panel data. (This paper is an updated version of the paper that appeared earlier as Barra, I., Hoogerheide, L.F., Koopman, S.J., and Lucas, A. (2013) "Joint Independent Metropolis-Hastings Methods for Nonlinear Non-Gaussian State Space Models". TI Discussion Paper 13-050/III. Amsterdam: Tinbergen Institute.)

Sequential Monte Carlo (SMC) methods are widely used for filtering purposes of non-linear economic or financial models. Nevertheless the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov- Chain Monte-Carlo (MCMC) methods. Not only SMC algorithms draw posterior distributions of static or dynamic parameters but additionally provide an estimate of the normalizing constant. The tempered and time (TNT) algorithm, developed in the paper, combines (off-line) tempered SMC inference with on-line SMC inference for estimating many slightly different distributions. The method encompasses the Iterated Batch Importance Sampling (IBIS) algorithm and more generally the Resample Move (RM) algorithm. Besides the number of particles, the TNT algorithm self-adjusts its calibrated parameters and relies on a new MCMC kernel that allows for particle interactions. The algorithm is well suited for efficiently back-testing models. We conclude by comparing in-sample and out-of-sample performances of complex volatility models.

In recent years, a series of European labour market forecasts have been produced on behalf of, and have been published by, the European Centre for the Development of Vocational Training (Cedefop). These forecasts were generated using a modular modelling approach containing two major components, a multi-sector macroeconomic model (E3ME) for 29 European countries, and a labour market extension (WLME). The countries are treated as an integrated system in E3ME but the extension is applied to each country separately. Forecasts of employment by industry are determined by E3ME; forecasts of employment by occupation and qualification are determined by the extension. Both components rely mainly on time series econometric techniques to generate their forecasts. Meagher et al.(2014) describe how the WLME can be replaced with an alternative extension (MLME) which uses computable general equilibrium (CGE) modelling techniques. Compared to the WLME, the MLME relies less on time series analysis and more on explicitly modelled economic behaviour, based on theoretical considerations. In this paper, the design of the hybrid E3ME-MLME model is advanced in two ways. Firstly, MLME is configured such that, in the absence of any shocks and assuming that the occupational labour markets clear, it reproduces the forecasts derived using WLME. In that case, the MLME forecasts can be regarded as providing enhanced information about the WLME forecasts. In particular, MLME provides forecasts of changes in relative wage rates which can be used to identify structural pressures in the markets for labour, pressures which remain only implicit in the WLME employment forecasts produced for Cedefop. Secondly, when suitably configured, MLME can be used to determine the deviations to the WLME employment forecasts which would result if some of the conditions (either explicit or implicit) under which they were derived are relaxed. In particular, MLME is used to determine how the forecasts would be different if wage rates are not sufficiently flexible to clear the occupational labour markets. The attendant surpluses and shortages revealed by MLME provide corroborative evidence on the underlying structural pressures in the Cedefop forecasts. Results are reported for the United Kingdom, Greece and the Netherlands.

Quantitative finance combines mathematical finance, financial statistics, financial econometrics and empirical finance to provide a solid quantitative foundation for the analysis of financial issues. The purpose of this special issue on "Recent developments in quantitative finance" is to highlight some areas of research in which novel methods in quantitative finance have contributed significantly to the analysis of financial issues, specifically fast methods for large-scale non-elliptical portfolio optimization, the impact of acquisitions on new technology stocks: the Google–Motorola case, the effects of firm characteristics and recognition policy on employee stock options prices after controlling for self-selection, searching for landmines in equity markets, whether CEO incentive pay improves bank performance, using a quantile regression analysis of U.S. commercial banks, testing price pressure, information, feedback trading, and smoothing effects for energy exchange traded funds, actuarial implications of structural changes in El Niño-Southern Oscillation Index dynamics, credit spreads and bankruptcy information from options data, QMLE of a standard exponential ACD model: asymptotic distribution and residual correlation, and using two-part quantile regression to analyze how earnings shocks affect stock repurchases.

When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the subsample-averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all subsamples.

This paper studies the reactions of commercial banks to the changes in monetary policy tools in mid-1994, when the Federal Reserve Bank altered its policy implicitly targeting the Federal Funds Rate (FFR). Prior to 1994, the FFR had affected, with a considerable lag, the Prime Rate. However, after the move by the Fed in 1994, commercial banks responded immediately by changing their Prime lending rate to the Federal Funds Rate plus a three-percent spread. Based on the response of commercial banks, it is evident that a more transparent monetary policy can, in fact, more effectively achieve its underlying objectives.

Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model parameters are derived by a sequential testing procedure that yields adapted predictions at every time point. Applying the proposed method to monthly data on 2000 U.S. public rms over a sample period from 1991 to 2011, we estimate default probabilities over various prediction horizons. The prediction performance is evaluated against the global FIA that employs all past observations. For the six months prediction horizon, the local adaptive FIA performs with the same accuracy as the benchmark. The default prediction power is improved for the longer horizon (one to three years). Our local adaptive method can be applied to any other speci cations of forward intensities.

Econometricians have recently been interested in estimating and testing the mean reversion parameter (κ) in linear diffusion models. It has been documented that the maximum likelihood estimator (MLE) of κ tends to over estimate the true value. Its asymptotic distribution, on the other hand, depends on how the data are sampled (under expanding, infill, or mixed domain) as well as how we spell out the initial condition. This poses a tremendous challenge to practitioners in terms of estimation and inference. In this paper, we provide new and significant results regarding the exact distribution of the MLE of κ in the Ornstein-Uhlenbeck process under different scenarios: known or unknown drift term, fixed or random start-up value, and zero or positive κ. In particular, we employ numerical integration via analytical evaluation of a joint characteristic function. Our numerical calculations demonstrate the remarkably reliable performance of our exact approach. It is found that the true distribution of the MLE can be severely skewed in finite samples and that the asymptotic distributions in general may provide misleading results. Our exact approach indicates clearly the non-mean-reverting behavior of the real federal fund rate.

In recent years, a series of European labour market forecasts have been produced on behalf of, and have been published by, the European Centre for the Development of Vocational Training (CEDEFOP). These forecasts were generated using a modular modelling approach containing two major components: * a multi-sector macroeconomic model (E3ME) for 29 European countries, primarily developed and operated by Cambridge Econometrics, and * a labour market extension (WLME), primarily developed and operated by the Institute for Employment Research at the University of Warwick. The countries are treated as an integrated system in E3ME but the extension is applied to each country separately. Forecasts of employment by industry are determined by E3ME; forecasts of employment by occupation and qualification are determined by the extension. The two components rely mainly on time series econometric techniques to generate their forecasts. This paper describes how WLME can be replaced with an alternative extension (MLME) which incorporates a computable general equilibrium model. The CGE model has been developed primarily at the Centre of Policy Studies at Monash University. Compared to WLME, MLME relies less on time series extrapolation and more on explicitly modelled economic behaviour. This approach introduces a range of behavioural and technical parameters which offer more scope for modelling developments in the labour market which impact on occupations and skills rather industries. Forecasts produced using the new E3ME-MLME system are reported for the United Kingdom, Greece and the Netherlands, and compared with the corresponding forecasts produced using the existing E3ME-WLME system. The focus of the comparison is on qualitative differences in the way the two sets of forecasts are to be interpreted. In particular, the sense in which explicit specification of technical change and economic behaviour (in the new system) can be substituted for time series extrapolation techniques (in the existing system) is carefully considered. The primary objective of the paper, therefore, is to demonstrate the empirical feasibility of the alternative methodology rather than to produce robust alternative forecasts.

We investigate the dynamic relationships between the US five-year financial CDS sector index spreads for the banking, financial services and insurance sectors in the short- and long-run over the recent period which is marked by the onset of the global financial crisis. For this purpose, we implement a Smooth Transition Error- Correction Model (STECM) to accommodate the presence of nonlinearities and asymmetry in the adjustment process of the CDS variables towards their long-run equilibrium. Our findings provide evidence of significant long-run equilibrium links for two out of the three CDS indices, which are found to be typically asymmetric and nonlinear. These findings are more potent and more strongly policy oriented when the control variables are introduced into an extended STECM.

In this article, we use the recently developed nonlinear autoregressive distributed lags (NARDL) model to examine the pass-through of crude oil prices into gasoline and natural gas prices. Our approach allows us to simultaneously test the short- and long-run nonlinearities through positive and negative partial sum decompositions of the predetermined explanatory variables. It also offers the possibility to quantify the respective responses of gasoline and natural gas prices to positive and negative oil price shocks from the asymmetric dynamic multipliers. The obtained results indicate that oil prices affect gasoline prices and natural gas prices in an asymmetric and nonlinear manner, but the price transmission mechanism is not the same. Important policy implications can be learned from the empirical findings.

Two recent nonlinear causality tests are used to examine the causal linkages across increasingly important international equity and commodity markets. We show that nonlinear causality is more appealing when asymmetric patterns are accounted for. The results are crucial to hedging and portfolio diversification strategies.

This paper shows that fluctuations in the U.S. equity returns signal information about the behavior of the returns of energy, metal and agricultural commodity futures. We find that asymmetry plays an important role in this relationship. In addition, while asymmetry seems to be more relevant for metal commodities in the nineties, it emerges more powerfully for agricultural commodities since 2000. Moreover, the asymmetric linkages are observed both when returns are measured in nominal and real terms and at different time horizons, even though they are especially strong in the case of short-term commodity futures returns.

This article uses the DCC–FIAPARCH model to examine the time-varying properties of conditional return and volatility of crude oil and US stock markets as well as their dynamic correlations over the period 1988–2013. Our results indicate that both the long memory and asymmetric behavior characterize the conditional volatility of oil and stock market returns. On the other hand, the dynamic conditional correlations (DCC) between the crude oil and US stock markets are affected by several economic and geopolitical events. When looking at the optimal design of an oil-stock portfolio, we find that investors in the US markets should have more stocks than crude oil asset in order to reduce their portfolio risk. Finally, the use of the DCC–FIAPARCH model that explicitly accounts for long memory and asymmetric volatility effects enables the investors to effectively hedge the risk of their stock portfolios with lower costs, compared to the standard DCC–GARCH model.

We study the conditional dependence structure between crude oil prices and U.S. dollar exchange rates using a copula-GARCH approach. Various copula functions of the elliptical, Archimedean and quadratic families are used to model the underlying dependence structure in both bearish and bullish market phases. Over the 2000–2011 period, we find evidence of significant and symmetric dependence for almost all the oil-exchange rate pairs considered. The rise in the price of oil is found to be associated with the depreciation of the dollar. Moreover, we show that Student- t copulas best capture the extreme dependence, and that taking the extreme comovement into account leads to improve the accuracy of VaR forecasts. Our main results remain unchanged when considering alternative GARCH-type specifications and the crisis period, but are sensitive to the use of raw returns.

We provide clear-cut evidence for economically and statistically significant multivariate jumps (multi-jumps) occurring simultaneously in stock prices by using a novel nonparametric test based on smoothed estimators of integrated variances. Detecting multi-jumps in a panel of liquid stocks is more statistically powerful and economically informative than the detection of univariate jumps in the market index. On the contrary of index jumps, multi-jumps can indeed be associated with sudden and large increases of the variance risk-premium, and possess a statistically significant forecasting power for future volatility and correlations which implies a sizable deterioration in the diversification potential of asset allocation.

Determination of diversification strategies by investors depends on the nature and magnitude of the relationships existing between different stock markets. Therefore, it is important for international investors to understand the relationship among various markets in order to diversify risk and derive high return. In a co-movement analysis a consideration that is being looked upon is the distinction between the short and the long term investor, who have different term objectives. Wavelets were used to assess the comovement and interactions of East and West stock index, namely; FBKLCI (Malaysia), FSSTI (Singapore), INDU (Indonesia), HIS (Hong Kong) and JCI (New York). The findings suggest that most of the indexes investigated in this study through the wavelet coherency shows that high coherency exists among them on the daily time scale of 32 to 512 days band. A negative correlation between them was also found among the markets, which shows a tendency in the correlation coefficients to move downwards with the timescale, except for the very long-run. In addition, it is also observed that there exists a linear relationship between the wavelet variance and wavelet timescale. The variance for most of the indexes decreases as the wavelet timescale increases. The cross correlation analysis showed that the short and medium term fluctuations for the indexes are more closely related compared to those over the long term.

This paper sheds light on the economic impacts of political uncertainty caused by the civil uprisings that swept across the Arab World and have been collectively known as the Arab Spring. Measuring documented effects of political uncertainty on regional stock market indices, we examine the impact of the Arab Spring on the volatility of stock markets in eight countries in the Middle East and North Africa (MENA) region: Egypt, Lebanon, Jordon, United Arab Emirate, Qatar, Bahrain, Oman and Kuwait. This analysis also permits testing the existence of financial contagion among equity markets in the MENA region during the Arab Spring. To capture the time-varying and multi-horizon nature of the evidence of volatility and contagion in the eight MENA stock markets, we apply two robust methodologies on data from November 2008 to March 2014: MGARCH-DCC, Continuous Wavelet Transforms (CWT). Our results tend to indicate two key findings. First, the discrepancies between the volatile stock markets of countries directly impacted by the Arab Spring and the countries that were not directly impacted indicate that international investors may still enjoy portfolio diversification and investment in MENA markets. Second, the lack of financial contagion during the Arab Spring suggests that there is little evidence of cointegration among MENA markets implying the opportunities of portfolio diversification. Providing a general analysis of the economic situation and the investment climate in the MENA region during and after the Arab Spring, this study bears significant importance for the policy makers, local and international investors, and market regulators.

This paper is the first attempt to investigate the multiscale tendency of the co-movement and cross-correlation of nine Islamic Exchange Traded Fund (ETF) returns across the global developed and emerging markets using both wavelet coherence and wavelet MODWT methods. The wavelet coherence results tend to indicate consistent co-movement between most of the ETF returns especially in the long run. The study also uncovers evidence of wide variation of co-movement across the time-scales during the global financial crisis and the Euro debt crisis. Strong co-movement can be observed during the global financial crisis, both for the medium term investors and long term investors. The paper studies the relationship between different ETF returns using wavelet multi-resolution analysis. The cross-correlation analysis also shows certain significant and positive correlations between the ETF returns, especially during the period of global financial crisis. The findings from these two recent dynamic time-scale decomposition methodologies have important policy implications for both risk management and investors’ investment policy.

Volatility is a measure of variability in the price of an asset and is associated with unpredictability and uncertainty about the price. Even it is a synonym for risk; higher volatility means higher risk in the respective context. With regard to stock market, the extent of variation in stock prices is referred to stock market volatility. A spiky and rapid movement in the stock prices may throw out risk-averse investors from the market. Hence a desired level of volatility is demanded by the markets and its investors. The traditional methods of volatility and correlation analysis did not consider the effect of conditional (or time-varying) volatility and correlation. Hence a major issue facing the investors in the contemporary financial world is how to minimize risk while investing in a portfolio of assets. An understanding of how volatilities of and correlations between asset 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 as well as for dynamic option pricing. Therefore, appropriate modelling of volatility is of importance due to several reasons such as it becomes a key input to many investment decisions and portfolio creations, the pricing of derivative securities and financial risk management. Thus, in this paper, we aim to estimate and forecast conditional volatility of and correlations between daily returns of the seven selected Dow Jones Islamic and conventional price indexes spanning from 01/1/2003 to 31/3/2013. The sample period from January 30, 2003 to December, 13, 2010 amounting to 2053 daily observations are used for estimation and the remaining sample period is used for evaluation, through the application of the recently-developed Dynamic Multivariate GARCH approach to investigate empirical questions of the time-varying volatility parameters of these selected Dow Jones stock indices and time varying correlation among them. The contribution of this work is an improvement on others‘ works particularly in terms of time-varying volatility and correlation of assets incorporating Islamic assets. We find that all volatility parameters are highly significant, with the estimates very close to unity implying a gradual 2 volatility decay. The t-distribution appears to be more appropriate in capturing the fat-tailed nature of the distribution of stock returns and the conditional correlations of returns of all Dow Jones Islamic Markets, Dow Jones Islamic UK, and Dow Jones Islamic US Indices with other indices are not found constant but changing. The policy implications of this finding are that the shariah investors should monitor these correlations and mange their investment portfolios accordingly. In addition to this, the different financial markets offer different opportunities for portfolio diversification.

This study makes the initial attempt to investigate the time varying volatility and return linkages of Moroccan Islamic stock indices with the market and regional based indices especially during times of political unrest. More specifically, we use the Dow Jones Islamic Developed markets and Emerging markets indices to represent the market based indices, whereas Dow Jones Islamic Europe and Dow Jones Islamic Asia Pacific were used to represent regional based indices. In an attempt to unravel the time-varying nature of these co-movements, the MGARCH-DCC which is one of the recent research methodologies was adopted and applied on daily data covering from June 2007 to December 2012. From the discovered results, it is seen that the Moroccan Islamic investors may derive benefits from portfolio diversification across stock markets of Developed markets and the Asia Pacific market especially during times when there is no political unrest. The findings obtained under this study are crucial for understanding the role of political uncertainty on the stability of the stock market and is of great interest to investors especially the Islamic ones.

The primary aim of this study is to investigate the causal chain among output, money, prices, exchange rate and inflation in the context of Nigerian economy following the global economic crisis that hit many countries. The data used are from 1970 to 2012. The methodology employed uses several econometric techniques such as unit root tests, cointegration, vector error-correction model(VECM), variance decompositions and persistent profile in order to capture both the within-sample and out- of- sample causality. The result obtained is quite in line with our expectation given the nature of the Nigerian economy that relies heavily on the crude oil revenue and also imports from abroad. The result of cointegration analysis reveals that there exist long run relationships among the variables under study. From the VECM analysis, it suggests that output, interest rate and prices are the leading variables while exchange rate and money appear to have borne the brunt of the short run adjustments. This finding is in line with the real business cycle theory. In order to capture the impact of economic crisis on the selected variables, a dummy variable was created in the VECM analysis. It indicates an absence of the impact of the global economic crisis on the Nigerian economy as evidenced by the insignificance of the coefficient of the dummy variable. The findings of these results have economic policy implications in that output contains information about the sources of shock that affects the economy and hence output would be useful in predicting the future growth of the economy.

Gold, and other precious metals, are among the oldest and most widely held commodities used as a hedge against the risk of disruptions in financial markets. The prices of such metals fluctuate substantially, introducing a risk of its own. This paper’s goal is to analyze the risk of investment in gold, silver, and platinum by applying Extreme Value Theory to historical daily data for changes in their prices. The risk measures adopted in this paper are Value at Risk and Expected Shortfall. Estimates of these measures are obtained by fitting the Generalized Pareto Distribution, using the Peaks-Over-Threshold method, to the extreme daily price changes. The robustness of the results to changes in the sample period is discussed. Our results show that silver is the most risky metal among the three considered. For negative daily returns, platinum is riskier than gold; while the converse is true for positive returns.

The purpose of this paper is to analyze the possible portfolio diversification opportunities between Asian Islamic Market and other regions‟ Islamic Markets; namely USA, Europe and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets - based on the 6 years‟ daily data, from 04/2008 to 03/2014 - to identify the correlations among those selected markets by employing the recent econometric methodologies such as MGARCH-DCC, MODWT and the Continuous Wavelet Transform (CWT). By utilizing the MGARCH-DCC, this paper tries to identify the strength of the correlation among the markets. On the other hand, to see the time-varying nature of these mentioned correlations, we utilized CWT. For robustness, we have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have a better portfolio diversification opportunities with the US markets followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to 8 days only) can benefit through portfolio diversification, especially in the USA and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g. Portfolio Management, Strategic Investment Management).

This paper attempts to examine whether a long-run theoretical relationship does indeed exist between the level of inflation in South Africa and the amount of FDI eventually received by the country. It also attempts to provide insight into the purported macroeconomic benefits of the policy of ‘inflation targeting’, by ascertaining whether any causality exists between stable inflation levels and improved FDI inflows from a South African perspective. Utilising annual data ranging from 1970 to 2012, we employ time series techniques to answer our research objectives. Our results indicate that there is a long-run inverse relationship between the level of inflation and FDI inflow in South Africa, implying that a rise in the level of inflation would have a negative impact on the amount of FDI received by South Africa. Furthermore, the paper successfully demonstrates that a degree of causality does exist between stable inflation levels and improved FDI inflows from a South African perspective, suggesting that the policy change that occurred with the adoption of ‘inflation targeting’ by the South African authorities did have a significant impact on the average level of FDI inflow to the country. Consequently, one of the implications of our findings is that the policy of ‘inflation targeting’, if well-implemented, actively managed and consistently applied, could represent a vital organ of the policy toolkit available to governmental authorities and policymakers in South Africa and indeed all developing countries, in their bid to enhance the inflow of FDI to their respective countries.

This paper investigates the time-varying relationship between the oil price and disaggregated stock market of India using DCC-MGARCH and Continuous Wavelet Transformation methodologies. Our findings reveal the evolving relationship between the oil price and disaggregated stock market. The correlations are generally volatile before the 2007-08 crisis but since then the correlations are positive implying no diversification benefits for the investors during rising oil prices. Since, emerging markets in general, and India in particular, is expected to increase its share of oil consumption in the world’s energy market (due to rapid expansion), therefore for the stock market to grow, especially the oil-intensive industries, we recommend the government should increase its reliance on alternative energy resources such as coal and renewables. Furthermore, as rising oil prices can also have its adverse effect through exchange rate channel, we suggest the monetary policies should be time varying to manage the oil inflationary pressures arising out of extreme volatility in the oil prices.

This paper attempts to analyse the extent of financial integration of two developed (the U.S. and Japan) and two emerging Islamic stock markets (China and India) with the Malaysian Islamic stock market in order for the Malaysian financial traders to make decision about their portfolio diversification, risk management and asset allocation. Despite the growing interest in Islamic finance, there are few empirical studies investigating integration of Islamic stock markets. As a result there is a certain gap in the literature pertaining to this area of research. By applying recent related techniques, this paper examines the correlations among these Islamic stock markets in a timevariant manner to indicate the degree of financial integration among them. It is found that Strong financial integration exists between the Chinese and Malaysian Islamic stock markets. Furthermore, the study suggests that in the long run, investors in Malaysia could gain by diversifying their portfolios in Japan and in the short run the US market is a better option to consider. Overall, the developed Islamic stock markets are better for the Malaysian financial traders rather than the emerging markets.

Recently, financial engineering has brought a significant number of interest rate derivative products. Amongst the variables used in pricing these derivative products is the short-term interest rate. This research article examines various short-term interest rate models in continuous time in order to determine which model best fits the South African short-term interest rates. Both the linear and nonlinear short-term interest rate models were estimated. The methodology adopted in estimating the models was parametric approach using Quasi Maximum Likelihood Estimation (QMLE). The findings indicate that nonlinear models seem to fit the South African short-term interest rate data better than the linear models

Most of previous researches have only focused on the effect of export expansion on economic growth while ignoring the potential of import in developing economic growth. This study makes an attempt to examine the relationship between trade and economic growth in Malaysia with emphasis on both the role of exports and imports. This study treats exports and imports separately to allow for the possibility that their influence toward economic growth is asymmetric and adopts recent advances in time series modeling. This study used Granger causality test and impulse response functions to examine whether growth in trade stimulates economic growth. It is important to examine the linkage between trade and economic growth for Malaysia in order to provide evidence whether rapid economic growth in the region is driven by trade or whether there is reciprocal impact between growth and trade. The results tend to suggest that the singular focus of past studies on exports as engine of growth may be misleading. The results confirm the bidirectional long run relationships between the economic growth and exports, economic growth and imports and exports and imports. From a policy point of view, investigating the causal links between trade and economic growth generates important implications for the development strategies of developing countries. If exports drive economic growth, policy should promote exports, and likewise for imports.

This study addresses the question of whether the adaptive market hypothesis provides a better description of the behaviour of emerging stock market like India. We employed linear and nonlinear methods to evaluate the hypothesis empirically. The linear tests show a cyclical pattern in linear dependence suggesting that the Indian stock market switched between periods of efficiency and inefficiency. In contrast, the results from nonlinear tests reveal a strong evidence of nonlinearity in returns throughout the sample period with a sign of tapering magnitude of nonlinear dependence in the recent period. The findings suggest that Indian stock market is moving towards efficiency. The results provide additional insights on association between financial crises, foreign portfolio investments and inefficiency. JEL codes G14; G12; C12

The increase in household debts in Malaysia which has escalated to about 86% of total GDP is deemed to be at worrying stage as it may in turn trigger another financial crisis. Thus, the aim of this study is to examine the increase in household debts and its relation to GDP, interest rate and house price via time series techniques. Data collected from Datastream and monthly statistical bulletin span from 1999 to 2014 on quarterly basis. The results show that there is a cointegrating long run relation between household debt, house prices, GDP and interest rate. The analysis indicates that although household debts could not be influenced by the changes in GDP, lending rate and house price in the short run, it could be affected by house price movement in the long run. As there is a positive significant relationship between house price and household debts, it implies that, in the long run horizon, the increase in household debts is due to the increase in house price. Although both GDP and lending rate are found to be endogenous, we still believe that the movement in lending rate and GDP (as a proxy to income) may affect the household debts. Thus, extra care shall be taken by the policy maker for any decision to increase the lending rate in particular as the lending rate is deemed to be one of the macroeconomic policy instruments which may have significant influence on household income. As the lending rate is deemed endogenous, the policy maker should strengthen prudential measure in order to curb the increase in household debts. Shortening the loan tenure, tightening credit policy by implementing responsible and selective lending, higher debt service ratio, strengthening the risk management of banking institutions are amongst the measures that might facilitate the policy maker to combat the rising household debts. Additionally, as the result found that the house price is the main indicator that affects the household debt in the long horizon, the policy maker should take an initiative to control the property price in order to mitigate any bubble in asset price.