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This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well.

To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive (1) dynamic conditional correlation–asymmetric generalized autoregressive conditional heteroskedastic model in a multivariate framework and exponential generalized autoregressive conditional heteroskedastic model in mean with covariances as explanatory variables are used. For an in-depth analysis, Markov regime switching model and optimal hedging ratios and weights are also computed. The span of data ranges from August 10, 2010 to August 7, 2015, especially after the global financial crisis.

The Indian equity market is not completely decoupled from mature markets as well as emerging market (China), but the time-varying correlation coefficients are on a downward spree after the global financial crisis, except for the US market. The Indian and Chinese equity markets witness a highest level of correlation with each other, followed by the European, US and Japanese markets. Both the optimal portfolio hedge ratios and portfolio weights with two asset classes point out toward portfolio risk minimization through the combination of the Indian and US equity market stocks from a US investor viewpoint. A negative co-movement between the Indian and US market increases the conditional expected returns in the Indian equity market. There is an insignificant but a negative relationship between the expected risk and returns.

The study provides an insight to the international as well as domestic investors and supports the construction of cross-country portfolios and risk management especially after the occurrence of global financial crisis.

The present study contributes to the literature in three senses. First, the period relates to the events after the global financial crisis (2007-2009). Second, the study examines the co-movement of the Indian equity market with four major economic giants such as the USA, Europe, Japan and China in a multivariate framework. These economic giants are excessively following the easy money policies aftermath the financial crisis so as to wriggle out of deflationary phases. Finally, the study captures risk-return relationship in the Indian equity market, considering its covariance with the international markets.

In this paper, the authors aim to investigate the return, volatility and correlation spillover effects between the crude oil market and the various Indian industrial sectors (automobile, financial, service, energy, metal and mining, and commodities sectors) in order to investigate optimal portfolio construction and to estimate risk minimizing hedge ratios.

The authors compare bivariate generalized autoregressive conditional heteroskedasticity models (diagonal, constant conditional correlation and dynamic conditional correlation) with the vector autoregressive model as a conditional mean equation and the vector autoregressive moving average generalized autoregressive conditional heteroskedasticity model as a conditional variance equation with the error terms following the Student's t distribution so as to identify the model that would be appropriate for optimal portfolio construction and to estimate risk minimizing hedge ratios.

The authors’ results indicate that the dynamic conditional correlation bivariate generalized autoregressive conditional heteroskedasticity model is better able to capture time‐dynamics in comparison to other models, based on which the authors find evidence of return and volatility spillover effects from the crude oil market to the Indian industrial sectors. In addition, the authors find that the conditional correlations between the crude oil market and the Indian industrial sectors change dynamically over time and that they reach their highest values during the period of the global financial crisis (2008‐2009). The authors also estimate risk minimizing hedge ratios and oil‐stock optimal portfolio holdings.

This paper has empirical originality in investigating the return, volatility and correlation spillover effects from the crude oil market to the various Indian industrial sectors using BVGARCH models with the error terms assumed to follow the Student's t distribution.

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.

Existing research has theoretically modeled conditional correlations between the long-term interest rates as a function of macroeconomic variable. In line with it, the purpose of this paper is to explore whether conditional correlations can be a new signal to predict recessions. Furthermore, this paper also tries to investigate among the four factors – the time difference of the beginning and the end of recessions, financial integration (FI), and trade integration (TI) – which factors drive the direction of change in conditional correlations. Finally, this paper is to explain the implication for Korea trade.

This study uses a probit regression model for 33 country during the period from 1972 to 2015. To measure the time-varying interest rates conditional correlations, a VAR(1)-DBEKK-GARCH(1,1) model is adopted due to its statistical advantages. Furthermore, the authors also construct the four measures – time difference of the beginning of recessions (BEG), time difference of the end of recessions (END), FI, and TI. The authors first study the predictive power of correlations in both in and out-samples test, and study which factors determine the different behavior of interest rate co-movements using the four measures.

The empirical results show that the conditional correlations between the long-term interest rates of the USA and individual countries contain information about recessions a few quarters ahead which term spreads of neither individual countries nor the USA conveyed in. However, there is a heterogeneity of the significance and direction of interest rate correlations. A further research reveals that especially the heterogeneous degree of TI leads to the different overlapped recession period of individual countries with the USA, resulting in heterogeneous behavior of interest rates among countries.

As a limitation of this paper, the forecasting power of interest rate correlations is not always significant in all countries. Despite this, the study has a profound implication that for those countries where the US accounts for the high proportion of trade, increase in conditional correlations can be a signal for future recessions. Especially, given a considerable portion of trade in GDP and the more sensitive trade activity of Korea to a contagious recession than a domestic recession, the conditional correlation measure is particularly useful for Korean policy makers.

Although many papers model interest rate co-movement as a function of macroeconomic condition, this paper provides the first evidence to show interest rate co-movement precede the macro shocks empirically. Furthermore, this paper determines the precise channel through which TI affects the time-varying behavior of interest rate co-movements before recessions.

The purpose of this paper is to investigate the relationship between inflation, interest rate, and output gap in the US economy in the post Second World War period, without assuming any structure nor imposing any restriction on that relationship.

The authors apply vine copula modeling to investigate asymmetry and tail behavior on both conditional and unconditional dependence among those variables. The dependence parameter is allowed to evolve over time according to a stochastic autoregressive processes. Additionally, a conditional expectation based on vine copula is used to analyze the conditional expectation of interest rate.

The results suggest that the joint distribution, both conditional and unconditional, of the interest rate and inflation is asymmetric to the left, while the pair interest rate and output gap have symmetrical distributions coupled with low persistence and high volatility. Besides the unquestionable evidence that the US monetary policy has been mostly focused on inflation stabilization, there is also indication of nonlinearity in the conditional expected interest rate and asymmetric behavior by the Federal Reserve in the long run.

The vine copula modeling allows for several forms of asymmetries and tail dependence, which is a flexible modeling strategy for multivariate distributions. Moreover, the conditional expectation implied by vine copulas is suitable to account for nonlinearity in the interest rate conditional on inflation and output gap.

The purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context.

Sample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model.

Conditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios.

A significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking.

The study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

This paper aims to investigate the impact of cost stickiness on conditional conservatism.

The research sample consists of listed companies from 18 countries, using stock market indices of the BRICS, MIST, North Africa, USA and EU over the period ranging from 1997 to 2015. The authors use the firm-fixed effects method in the estimation of the models.

The results provide evidence of the existence of cost stickiness and conditional conservatism in the international context, using the Banker et al. (2016) model. They also argue that the conditional conservatism model (Basu, 1997) is overstated because it does not control for cost stickiness. In additional analyses, the authors conclude that the association between cost stickiness and accounting conservatism changes across country groups and across industries. The authors also document that the employee intensity and free cash-flow, as cost stickiness determinants, remain significant in the model including accounting conservatism. Moreover, the findings show that sticky cost behavior distorts inferences about standard demand drivers of conservatism such as leverage and size.

The findings are interesting and provide a better understanding of cost stickiness and conditional conservatism, and the interaction between these two phenomena in the international context, across country groups and across industries. To the best of the author’s knowledge, the study is the first one including free cash flow as a proxy for agency problem in the full model combining conservatism and cost stickiness models (Banker et al., 2016).

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies.

The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates.

The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods.

The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates.

This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.