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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 chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

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.

This paper analyzes alternative performance evaluation models applied to equity mutual funds under conditional and unconditional approaches in the Brazilian market.

The analysis is conducted using CAPM's single factor, Fama–French three and five factors, under their conditional and unconditional versions in a sample of 896 equity mutual funds from 2008 to 2019.

The results suggest that the use of three- or five-factor models is especially relevant to reduce the effect of market anomalies in performance assessment. Additionally, results show that conditional approaches, adding time-varying alphas and betas with macroeconomic variables, provide higher explanatory power than their unconditional peers.

The results are relevant in the unique economic environment characterized by historically high interest rate and high market volatility.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.

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.