Economic Significance Analysis Research Articles

Liquidity premiums, interest rate differentials, and nominal exchange rate prediction

AbstractWith the use of daily panel data for industrial countries, we study the essentialness of liquidity premiums and interest rate differentials in predicting nominal exchange rates at short horizons. We find that information about higher‐order moments of exchange rates helps predict nominal exchange rates. Before the global financial crisis, interest rate differentials were crucial for forecasting exchange rates. Interest rate differentials and liquidity premia provide additional information for predicting nominal exchange rates in the post‐crisis period even if we have controlled for latent fundamental exchange rates. Moreover, the economic significance analysis results echo those of the statistical significance analysis.

The Performance of Stock Portfolios: Evidence from Analysing Malaysia Case, and Implication for Open Innovation

This research examines the performance of the Islamic stock portfolio (ISP) and conventional stock portfolio (CSP) for the five industrial sectors and market in Malaysia. The capital asset pricing model statistics indicate that the ISP provides a higher return with a lower systematic risk compared to the CSP in different sectors; however, the ISP and CSP perform equally in the market. The non-parametric stochastic dominance approach reveals that the ISP is better than the CSP for portfolio return without considering the riskiness for all sectors except properties; further, the ISP outperforms the CSP under the market condition. Economic significance analysis identifies that the expected financial loss of the ISP is lower than that of the CSP in all sectors other than properties; the anticipated financial loss of the ISP is significantly less than that of the CSP in the market situation. The overall findings imply that the risk-sharing ISP is superior to the risk-bearing CSP for better returns at the sector as well as the market level.

Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis

Based on the heterogeneous autoregressive (HAR) model, the study proposes the frameworks (HAR-AddRS and HAR-AddRS-J) to incorporate the impact of heterogeneity and volatility jumps in modeling the AddRS volatility estimator (Kumar & Maheswaran, 2014a, 2014b). The forecasting performance of the HAR-AddRS and HAR-AddRS-J models in comparison to the returns-based conditional volatility models is evaluated using the error statistic approach, Hansen (2005) superior predictive ability (SPA) test, and Hansen, Lunde et al. (2011) model confidence set (MCS) approach. The findings suggest that more accurate forecasts of daily volatility are obtained based on the HAR-AddRS and HAR-AddRS-J models than based on the returns-based conditional volatility models. The economic significance analysis results show that a substantial economic gain is achieved when the volatility forecasts based on the HAR-AddRS-J model are used to implement the trading strategies, however, the same is not true when the volatility forecasts are based on the returns-based conditional volatility models.

Modelling and forecasting unbiased extreme value volatility estimator: A study based on exchange rates with economic significance analysis

This study proposes the use of an alternate approach to generate more accurate forecasts of an unbiased AddRS estimator. The study also devises and implements trading strategies based on the volatility forecasts to highlight its economic significance. The findings indicate that the proposed framework provides more accurate forecasts of daily volatility in comparison to returns based and range based alternative volatility models. The findings based on economic significance analysis indicate that the risk averse investor can earn substantial gain by using the volatility forecasts of the proposed framework than by using the volatility forecasts of the alternative models.

P.359 Subjective and objective emotional expression to different taste of food among patients with major depressive disorder

The study examines the realized volatility transmission from crude oil to various equity sectors (Automobiles, Financials, Industrial, Telecom and Pharmaceuticals) using the Heterogeneous Autoregressive Distributed Lag (HAR-DL) framework. We also consider factors representing orthogonalized realized volatility components of S&P 500 in the HAR-DL framework. The full sample analysis provides evidence of significant short-term realized volatility transmission from crude oil to the given equity sectors. The findings based on the time-varying analysis support the evidence that volatility transmission from crude oil to equity sectors is structurally unstable and exhibits structural breaks. Incorporating structural breaks in the realized volatility partially explains the structural breaks in realized volatility transmission from crude oil to equity sectors. We also examine the influence of conditional heteroskedasticity in volatility series on the measured volatility transmission and find that conditional heteroskedasticity plays a significant role in explaining the measured volatility transmission from crude oil to equity sectors. The economic significance analysis indicates that the information from crude oil market can be used to earn substantial economic gain in returns by investing in portfolios representing the given equity sectors.

Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis

This paper explores the role of heterogeneity and leverage effect on the predictability of the AddRS volatility estimator (Kumar & Maheswaran, 2014a) using daily, weekly and monthly volatility components. Similar to the model setting of heterogeneous autoregressive (HAR) model (Corsi, 2009), we introduce the frameworks (HAR – AddRS and HAR – AddRS – L) to incorporate the impact of heterogeneity and leverage effect in modeling the AddRS volatility estimator. We find that the heterogeneity and leverage effect significantly impact the volatility prediction and when taken together produce a better in-sample fit. To evaluate the out-of-sample performance of our new volatility models, we compare the forecasting performance of our models with that of other traditional benchmark models forecasts using the error statistic approach and Hansen (2005) superior predictive ability (SPA) test. The results show that the HAR – AddRS and HAR – AddRS – L models provide more accurate volatility forecasts for the out-of-sample. We also undertake the economic significance analysis to highlight that a substantial economic gain is achieved when the volatility forecasts based on the HAR – AddRS – L model are used to implement various trading strategies, however, the same is not true when the volatility forecasts are based on the traditional returns-based conditional volatility models.

Modeling volatility of Indian exchange rates under the impact of regime shifts: A study with economic significance analysis

In this paper, we assess the impact of regime shifts on the long memory properties of the Indian exchange rates. We make use of Sanso, Arago and Carrion (2004) Iterated Cumulative Sum of Squares (hereafter referred as AIT-ICSS) algorithm to detect the points of structural breaks in volatility series. Our findings indicate that incorporating the impact of sudden changes in volatility in the model indeed reduces the magnitude of long memory parameter. In the case of INR/JPY, we observe a shift in characteristics from long memory to mean reversion when the impact of regime shifts is included in the volatility model. Our findings also highlight that incorporating the impact of regime shifts in the model also improves the volatility forecast accuracy. Moreover, we implement a trading strategy based on risk-averse investor and find that the volatility forecasts based on the model which incorporate the impact of structural breaks provide substantial gains in return in comparison to volatility models with no structural breaks. These findings have important policy implications for financial market participants, investors and policy makers.

Time-consistent mean-variance reinsurance-investment in a jump-diffusion financial market

This paper study an optimal time-consistent reinsurance-investment strategy selection problem in a financial market with jump-diffusion risky asset, where the insurance risk model is modulated by a compound Poisson process. The aggregate claim process and the price process of risky asset are correlated by a common Poisson process. The objective of the insurer is to choose an optimal time-consistent reinsurance-investment strategy so as to maximize the expected terminal surplus while minimizing the variance of the terminal surplus. Since this problem is time-inconsistent, we attack it by placing the problem within a game theoretic framework and looking for subgame perfect Nash equilibrium strategy. We investigate the problem using the extended Hamilton–Jacobi–Bellman dynamic programming approach. Closed-form solutions for the optimal reinsurance-investment strategy and the corresponding value functions are obtained. Numerical examples and economic significance analysis are also provided to illustrate how the optimal reinsurance-investment strategy changes when some model parameters vary.

Realized volatility transmission from crude oil to equity sectors: A study with economic significance analysis

The study examines the realized volatility transmission from crude oil to various equity sectors (Automobiles, Financials, Industrial, Telecom and Pharmaceuticals) using the Heterogeneous Autoregressive Distributed Lag (HAR-DL) framework. We also consider factors representing orthogonalized realized volatility components of S&P 500 in the HAR-DL framework. The full sample analysis provides evidence of significant short-term realized volatility transmission from crude oil to the given equity sectors. The findings based on the time-varying analysis support the evidence that volatility transmission from crude oil to equity sectors is structurally unstable and exhibits structural breaks. Incorporating structural breaks in the realized volatility partially explains the structural breaks in realized volatility transmission from crude oil to equity sectors. We also examine the influence of conditional heteroskedasticity in volatility series on the measured volatility transmission and find that conditional heteroskedasticity plays a significant role in explaining the measured volatility transmission from crude oil to equity sectors. The economic significance analysis indicates that the information from crude oil market can be used to earn substantial economic gain in returns by investing in portfolios representing the given equity sectors.

A GARCH model for testing market efficiency

In this paper we propose a generalised autoregressive conditional heteroskedasticity (GARCH) model-based test for a unit root. The model allows for two endogenous structural breaks. We test for unit roots in 156 US stocks listed on the NYSE over the period 1980 to 2007. We find that the unit root null hypothesis is rejected in 40% of the stocks, and only in four out of the nine sectors the null is rejected for over 50% of stocks. We conclude with an economic significance analysis, showing that mostly stocks with mean reverting prices tend to outperform stocks with non-stationary prices.

Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis

This study provides a framework based on an extension of the conditional autoregressive range (CARR) model which incorporates the impact of sudden changes in the unconditional volatility. This study proposes to use the RS estimator in the CARR model (called henceforth the CARRS model) instead of using the range. The results of the CARRS models with and without volatility breaks are compared with the results of the GARCH models with and without volatility breaks. We also compare the forecasting performance of CARRS models with the forecasting performance of EGARCH, TGARCH and FIGARCH models based on error statistics and regression approach. The findings indicate that the CARRS model with volatility breaks effectively captures the dynamics of volatility and provides better out-of-sample forecasts when compared with GARCH, EGARCH, TGARCH and FIGARCH models. We also devise a trading strategy to examine the economic significance of the proposed framework which indicates that the investor can make substantial gains (approximately 6%–10%) in return for most of cases based on volatility forecasts of CARRS model with volatility breaks. Results based on robustness check are consistent with our main findings.

An analysis of price discovery from panel data models of CDS and equity returns

We propose a panel data model of price discovery. We find that the stock market contributes to price discovery in most sectors while the Credit Default Swap (CDS) market contributes to price discovery in only a few sectors. We discover that in sectors where both the stock market and the CDS market contribute to price discovery, it is the stock market that dominates the price discovery process. When we consider investment grade stocks, the importance of the CDS market in price discovery improves but the stock market still dominates the price discovery process. The results for different sizes of stocks generally suggest that both markets are important for price discovery but it is the stock market that dominates. We also find that while the price discovery process was affected by the 2007 global financial crisis, the stock market still dominated the price discovery process. Finally, in an economic significance analysis, we show that investors in the CDS market are able to make relatively more profits from a forecasting model that takes into account price discovery compared to a model that simply ignores the role of price discovery.

National culture and capital structure decisions: Evidence from foreign joint ventures in China

We investigate the role of firms’ country of origin in financial leverage decisions. Foreign joint ventures in China face a common set of country-level formal institutional constraints, but vary in the cultural values they bring from their countries of origin. We hypothesize that national culture enters the joint optimization process, leading to foreign joint ventures’ leverage decisions, and that it affects leverage decisions both directly and indirectly. Employing a hierarchical linear model distinguishing firm-level from country-level variables, and a data set covering over 8000 foreign joint ventures in China from 32 different countries and regions in the year 2002, we find that mastery has negative and significant direct effects on foreign joint ventures’ leverage and short-term debt decisions, and a positive and significant direct effect on the likelihood of foreign joint ventures having long-term debt. The indirect effects of mastery on leverage decisions sometimes reinforce and sometimes offset the direct effects. Embeddedness has no significant direct effect on foreign joint ventures’ leverage decisions, but exerts its influence entirely through indirect effects. Finally, the economic significance analysis of the total effects suggests that national culture has significant explanatory power in the leverage decisions of foreign joint ventures in China.

National Culture and Capital Structure Decisions: Evidence from Foreign Joint Ventures in China

We investigate the role of firms’ country of origin in financial leverage decisions using data on foreign joint ventures in China. We hypothesize that national culture enters the joint optimization process leading to foreign joint ventures’ leverage decisions and that it affects leverage decisions both directly and indirectly. Using cultural values of mastery and embeddedness to explain country of origin effects, we find that mastery has negative and significant direct effects on foreign joint ventures’ leverage and short-term debt decisions, and a positive and significant direct effect on the likelihood of foreign joint ventures’ having long-term debt. The indirect effects of mastery on leverage decisions sometimes reinforce and sometimes offset the direct effects. Embeddedness has no significant direct effect on foreign joint ventures’ leverage decisions, but exerts its influence entirely through indirect effects. Finally, the economic significance analysis of the total effects suggests that national culture has significant explanatory power in the leverage decisions of foreign joint ventures in China.

Disclosure of allegedly illegal corporate activities and information risk

Abstract We investigate how the disclosure of allegedly illegal corporate activities such as the backdating of stock option grants affects the “information risk” of accused companies. This research provides evidence about the consequences of this risk change on equity values. We compare the earnings' response coefficients (ERC) for companies charged with backdating before and after it was revealed that these companies allegedly engaged in these activities. Our results show a U-shape pattern in ERC across post-backdating periods, suggesting a temporary decline in information risk. We also estimate the amount of company valuation losses that are based on the changes in the ERC. We find, on average, that a firm suffered approximately $26.2 million in valuation losses for four quarterly earnings announcements in the post-backdating period. Our economic significance analysis suggests that valuation losses related to changes in “information risk” provide some explanations for the subsequent market valuation consequences.