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This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

This study aims to determine the mutual association between the volatility of macroeconomic indicators (MIs) and India’s tourism demand.

Bivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are applied to estimate the volatility spillover effect (VSE) from one market to another. Compared to the other methods, bivariate GARCH has wide acceptance for estimating the VSE. The monthly MIs and tourism demand data (2012–2021) are gathered for empirical analysis.

The evidence of the growth-led tourism (GLT) demand is seen. In the short term, tourism-led growth (TLG) is indicated. However, this TLG does not sustain itself in the long run. There is significant evidence in favour of the VSE from the MIs to the tourism demand ensuring GLT in India.

The main implication of the current study is to ignore the short-term influence of tourism demand on the economy because it does not sustain itself in the long run. However, the long-term influence of macroeconomic indicators on tourism demand should be seen with caution. Hedging, if possible, may be considered to protect the tourism sector’s interests from adverse economic fallouts.

There is a lack of studies on the volatility (especially on the VSE) between MIs and tourism demand. Hence, this study fills the research gap and presents a novel and unique contribution to the extent of the knowledge body on the topic and significantly contributes.

双变量GARCH模型用于估计从一个市场到另一个市场的波动溢出效应(VSE)。与其他方法相比, 双变量GARCH在估计波动溢出效应时得到了广泛的接受。收集2012-2021年的月度管理信息系统和旅游需求数据进行实证分析。

该研究旨在确定宏观经济指标(MIs)的波动与印度旅游需求之间的相互关系。

GLT(增长主导的旅游需求)的证据显而易见。从短期来看, 旅游导向型增长(TLG)可行。然而, 这种旅游导向型增长并不能长期维持下去。有重要的证据支持印度管理信息系统到旅游导向型增长的旅游需求波动溢出效应。

当前研究的主要启示是忽略了旅游需求对经济的短期影响, 因为从长远来看, 它无法自我维持。然而, 宏观经济指标对旅游需求的长期影响应谨慎看待。如有可能, 可考虑对冲, 以保护旅游业的利益不受不利的经济影响。

目前对管理信息需求与旅游需求之间的波动（尤其是波动溢出效应）的研究较少。因此, 本研究填补了这个研究空白, 并对该主题知识体系的内容呈现新颖而独特的促进作用, 有显著的贡献作用。

Los modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021).

El estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India.

Se observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India.

La principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas.

Existe una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.

The nexus of commodity prices with inflation is one of the main concerns for a nation's economy like India. The literature does not have enough volatility-based study, especially using the multivariate GRACH family of models to find a link between these two. It is the main reason for the conduct of this study. This paper aims to estimate the volatility effects of commodity prices on inflation.

For ten years (2011–2022), future prices of selected seven agriculture commodities and inflation indices (wholesale price index [WPI] and consumer price index [CPI]) are gathered every month. BEKK GARCH model (BGM) and DCC GARCH model (DGM) are employed to determine the volatility effect of commodity prices (CPs) on inflation.

The authors find that volatility's short-term (shock) impact on agricultural CPs to inflation does not exist. However, the long-term volatility spillover effect (VSE) is significant from commodities to inflation.

The study's findings have a significant implication for the policymakers to take a long-term view on inflation management regarding commodity prices. The findings can facilitate policy on the choice of commodities and the flexibility of their trading on the commodities derivatives market.

The findings of the study are unique. The authors do not observe any study on the volatility effect of agri-commodities (agricultural commodities) prices on inflation in India. This paper applies advanced techniques to provide novel and reliable evidence. Hence, this research is believed to contribute significantly to the knowledge body through its novel evidence and advanced approach.

This paper aims to investigate the volatility transmission and dynamics in China Securities Index (CSI) 300 index futures market.

This paper applies the bivariate Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models using high frequency data. Estimates for the bivariate GARCH models are obtained by maximising the log-likelihood of the probability density function of a conditional Student’s t distribution.

This empirical analysis yields a few interesting results: there is a one-way feedback of volatility transmission from the CSI 300 index futures to spot returns, suggesting index futures market leads the spot market; volatility response to past bad news is asymmetric for both markets; volatility can be intensified by the disequilibrium between spot and futures prices; and trading volume has significant impact on volatility for both markets. These results reveal new evidence on the informational efficiency of the CSI 300 index futures market compared to earlier studies.

This paper shows that the CSI 300 index futures market has improved in terms of price discovery one year after its existence compared to its early days. This is an important finding for market participants and regulators. Further, this study considers the volatility response to news, market disequilibrium and trading volume. The findings are thus useful for financial risk management.

The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process.

A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR‐MGARCH) model is used to construct a time‐varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two‐step approach, which allows for a large system of related prices to be estimated.

In‐sample and out‐of‐sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers' and commercial consumers' risks in futures market participation.

The application is limited to an examination of Montana wheat markets.

Agricultural producers who use futures markets to reduce market risk will have a better method for determining hedging positions, because MGARCH estimated hedge ratios incorporate more information than hedge ratios estimated using existing practices.

Portfolio variance reduction is analogous to utility improvement for agricultural producers. More efficient hedging strategies can lead to better implementation of futures markets and increased social welfare.

This research substantially extends current literature on agricultural hedge strategies by illustrating the advantages of using an hedge ratio estimation approach that incorporates important information about prices at linked markets and prices of other commodities. Providing evidence that market portfolio variance can be lowered using the multivariate estimation approach, the research offers commercial agricultural producers and consumers a practical tool for improving futures market strategies.

Reviews previous research based on event study methodology, pointing out that events can influence returns in many ways, and applies the method to a sample of mergers and acquisitions in the thinly traded Norwegian market 1983‐1994. Explains how the classic market model can be adjusted to control for non‐synchronous trading and changing/asymmetric volatility; and how the event and non‐event periods can be combined into a single model. Applies two different models to the data, compares the results and finds the ARMA‐GARCH approach superior to the OLS. Discusses the implications of this for researchers.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

This study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.

Daily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.

The empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.

This paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.

The authors analyze the relationship between exchange rate fluctuations of a number of major currencies and its impact on US stock market returns, as proxied by the S&P 500. Many studies have explored this topic since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This study takes a different look at this hypothesis and investigates the pairwise relationship between various exchange rates and the United States stock market returns (S&P 500 INDEX) from January 2000 to December 2019.

The authors test the data for unit roots using Phillip-Perron method. They use Johansen cointegration model to determine whether returns on S&P 500 are integrated with S&P 500. They use the VAR/VECM analysis to test whether there are any interdependencies between exchange rates and stock market return. Finally, they use various GARCH models, including the EGARCH and TGARCH models, to determine whether there exist volatility spillovers from exchange rate fluctuations in various markets to the volatility in the US stock market.

Using GARCH modeling, the authors find volatility in Australian dollar, Canadian dollar and the euro impact market return, and the volatility of Australian dollars and euro spills over to the volatility of S&P 500. They also find that the spillover is asymmetric for Australian dollars.

One of the limitations could be that the authors use different bivariate GARCH models rather than the MV-GARCH models. For future project(s), they plan to do this analysis from the perspective of a European Union or a British investor and use returns in those markets to see the impact of exchange rates on those markets. It would be interesting to know how the relationship will change during periods of financial crises. This could be achieved by employing structural break methodology.

Many studies have explored the relation between stock market returns and exchange rates since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This paper contributes by adding to the existing literature on impact of exchange rate on stock returns and by providing a detailed and different empirical analysis to support the results.