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Narwal, Karam Pal
- Spillovers and Transmission in Emerging and Mature Markets Implied Volatility Indices
Abstract Views :285 |
PDF Views:0
Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March. Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets.
Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Authors
Affiliations
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
Source
International Journal of Financial Management, Vol 2, No 4 (2012), Pagination: 33-46Abstract
Purpose: The present study examine implied volatility spillover and transmission between emerging (India) and mature stock markets (US, France, Germany and Switzerland), measured by their respective implied volatility indices i.e. IVIX, VIX, VCAC, VDAX and VSMI.Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March. Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets.
Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Keywords
Implied Volatility Index, Indian Stock Market, BEKK-GARCH, DCC, VIX, VSMI
References
- Aboura, S. (2003). International Transmission of Volatility: A Study on the Volatility Indexes VX1, VDAX and VIX. Working Paper: ESSEC Business School.
- Ahoniemi, K. (2008). Modelling and Forecasting the VIX Index. Retrieved 25 August, 2010 from http://ssrn. com/sol3/papers.cfm?abstract_id=1033812
- Äjiö, J. (2007). Implied Volatility Term Structure Linkages between VDAX, VSMI and VSTOXX Volatility Indices. Global Finance Journal, 18, pp. 290-302.
- Badshah, I. U. (2010). Asymmetric Return-Volatility Relation, Volatility Transmission and Implied Volatility Indices. Working Paper New Zealand: Auckland University of Technology.
- Blair, B., Poon, S. H. & Taylor, S. (2001). Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns. Journal of Econometrics, 105, pp. 5-27.
- Bollerslev, T. (1990). Modeling the Coherence in Short- Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, pp. 498-505.
- Brenner, M. & Galai, D. (1993). Hedging Volatility in Foreign Currencies. The Journal of Derivatives, 1, pp. 53-59.
- Carr, P. & Wu, L. (2006). A Tale of Two Indices. Journal of Derivatives, 13, pp. 13-29.
- Christenssen, B. & Prabbala, N. (1998). The Relation between Implied and Realized Volatility. Journal of Financial Economics, 50, pp. 125-150.
- Cohen, G. & Qadan, M. (2010). Is Gold Still a Shelter to Fear? American Journal of Social and Management Sciences, 1, pp. 39-43.
- D.Börse. (2005). Guide to The Volatility Indices of Deutche Börse, Version 2.0.
- Degiannakis, S. A. (2008). Forecasting VIX. Journal of Money, Investment and Banking, 4, pp. 5-19.
- Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with A Unit Root. Econometrica, 49, pp. 1057-72.
- Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics, 20, pp. 339-350.
- Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Economic Theory, 11, pp. 122-150.
- Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp. 1749-1801.
- Engle, R. F. & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Paper Series, 8554.
- Fernandes, M., Medeiros, M. C. & Scharth, M. (2007). Modelling and Predicting the CBOE Market Volatility Index. Retrieved 25 August, 2011 from http://www.econ.puc-rio.br/PDF/td548.pdf
- Fleming, J., Ostdiek, B. & Whaley, R. E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Fleming, J., Ostdiek, B. & Whaley, R. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Galai, D. (1979). A Proposal for Indexes for Traded Call Options. Journal of Finance, 34, pp. 1157-72.
- Gastineau, G. (1977). An Index of Listed Option Premiums. Financial Analyst’s Journal, 33, pp. 70-75.
- Gazda, V. & Výrost, T. (2003). Application Of GARCH Models In Forecasting The Volatility of The Slovak Share Index (Sax). BIATEC, pp. 11, 17-20.
- Giot, P. (2005). Relationships between Implied Volatility Indices and Stock Index Returns. Journal of Portfolio Management, 31, pp. 92-100.
- Gonz´alez, M. T. & Novales, A. (2009). Are Volatility Indices in International Stock Markets Forward Looking? RACSAM Rev. R. Acad. Cien. Serie A. Mat. (Applied Mathematics), pp. 103, pp. 339-352.
- Hamao, Y., Masulis, R. & Ng, V. (1990). Correlation in Price Changes and Volatility across International Stock Markets. Review of Financial Studies, 3, pp. 281-307.
- Koutmos, G. (1996). Modeling the Dynamic Interdependence of Major European Stock Markets. Journal of Business Finance and Accounting, 23, pp. 975-988.
- Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992). Testing the Null Hypothesis of Trend Stationarity. Journal of Econometrics, 54, pp. 159- 178.
- Maghrebi, N., Kim, M. S. & Nishina, K. (2007). The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations. Asia- Pacific Journal of Financial Studies, 36, pp. 163- 187.
- Mayhew, S. (1995). Implied Volatility. Financial Analyst Journal, July-August, pp. 8-20.
- Moraux, F., Navatte, P. & Villa, C. (1999). The Predictive Power of the French Market Volatility Index: A Multi Horzon Study. European Finance Review, 2, pp. 303-320.
- Nikkinen, J. & Sahlström, P. (2004). International Transmission of Uncertainty Implicit in Stock Index Option Prices. Global Finance Journal, 15, pp. 1-15. Phillips, R. C. B. & Perron. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, pp. 335-346.
- Sarwar, G. (2010). The VIX Market Volatility Index and U.S. Stock Index Returns. International Journal of Business Research, 10, pp. 166-176.
- SIX Swiss Exchange Ltd. (2010). Guide Governing Volatility Index VSMI®. .
- Skiadopoulos, G. (2004). The Greek Implied Volatility Index: Construction and Properties. Applied Financial Economics, 14, pp. 187-1196.
- Tse, Y. K. & Tsui, A. K. C. (2002). A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, pp. 351-362.
- Wagner, N. & Szimayer, A. (2004). Local and Spillover Shocks in Implied Market Volatility: Evidence for the U.S. and Germany. Research in International Business and Finance, 18, pp. 237-251.
- Whaley, R. E. (1993). Derivatives on Market Volatility: Hedging Tools Long Overdue. Journal of Derivatives, 1, pp. 71-84.
- Whaley, R. E. (2000). The Investor Fear Gauge. Journal of Portfolio Management, 26, pp. 12-17.
- Spillovers and Transmission in Emerging and Mature Markets Implied Volatility Indices
Abstract Views :303 |
PDF Views:0
Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March.
Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets. Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Authors
Affiliations
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
Source
International Journal of Financial Management, Vol 2, No 4 (2012), Pagination: 47-59Abstract
Purpose: The present study examine implied volatility spillover and transmission between emerging (India) and mature stock markets (US, France, Germany and Switzerland), measured by their respective implied volatility indices i.e. IVIX, VIX, VCAC, VDAX and VSMI.Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March.
Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets. Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Keywords
Implied Volatility Index, Indian Stock Market, BEKK-GARCH, DCC, VIX, VSMIReferences
- Aboura, S. (2003). International Transmission of Volatility: A Study on the Volatility Indexes VX1, VDAX and VIX. Working Paper: ESSEC Business School.
- Ahoniemi, K. (2008). Modelling and Forecasting the VIX Index. Retrieved 25 August, 2010 from http://ssrn. com/sol3/papers.cfm?abstract_id=1033812
- Äjiö, J. (2007). Implied Volatility Term Structure Linkages between VDAX, VSMI and VSTOXX Volatility Indices. Global Finance Journal, 18, pp. 290-302.
- Badshah, I. U. (2010). Asymmetric Return-Volatility Relation, Volatility Transmission and Implied Volatility Indices. Working Paper New Zealand: Auckland University of Technology.
- Blair, B., Poon, S. H. & Taylor, S. (2001). Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns. Journal of Econometrics, 105, pp. 5-27.
- Bollerslev, T. (1990). Modeling the Coherence in Short- Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, pp. 498-505.
- Brenner, M. & Galai, D. (1993). Hedging Volatility in Foreign Currencies. The Journal of Derivatives, 1, pp. 53-59.
- Carr, P. & Wu, L. (2006). A Tale of Two Indices. Journal of Derivatives, 13, pp. 13-29.
- Christenssen, B. & Prabbala, N. (1998). The Relation between Implied and Realized Volatility. Journal of Financial Economics, 50, pp. 125-150.
- Cohen, G. & Qadan, M. (2010). Is Gold Still a Shelter to Fear? American Journal of Social and Management Sciences, 1, pp. 39-43.
- D.Börse. (2005). Guide to The Volatility Indices of Deutche Börse, Version 2.0.
- Degiannakis, S. A. (2008). Forecasting VIX. Journal of Money, Investment and Banking, 4, pp. 5-19.
- Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with A Unit Root. Econometrica, 49, pp. 1057-72.
- Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics, 20, pp. 339-350.
- Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Economic Theory, 11, pp. 122-150.
- Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp. 1749-1801.
- Engle, R. F. & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Paper Series, 8554.
- Fernandes, M., Medeiros, M. C. & Scharth, M. (2007). Modelling and Predicting the CBOE Market Volatility Index. Retrieved 25 August, 2011 from http://www.econ.puc-rio.br/PDF/td548.pdf
- Fleming, J., Ostdiek, B. & Whaley, R. E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Fleming, J., Ostdiek, B. & Whaley, R. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Galai, D. (1979). A Proposal for Indexes for Traded Call Options. Journal of Finance, 34, pp. 1157-72.
- Gastineau, G. (1977). An Index of Listed Option Premiums. Financial Analyst’s Journal, 33, pp. 70-75.
- Gazda, V. & Výrost, T. (2003). Application Of GARCH Models In Forecasting The Volatility of The Slovak Share Index (Sax). BIATEC, pp. 11, 17-20.
- Giot, P. (2005). Relationships between Implied Volatility Indices and Stock Index Returns. Journal of Portfolio Management, 31, pp. 92-100.
- Gonz´alez, M. T. & Novales, A. (2009). Are Volatility Indices in International Stock Markets Forward Looking? RACSAM Rev. R. Acad. Cien. Serie A. Mat. (Applied Mathematics), pp. 103, pp. 339-352.
- Hamao, Y., Masulis, R. & Ng, V. (1990). Correlation in Price Changes and Volatility across International Stock Markets. Review of Financial Studies, 3, pp. 281-307.
- Koutmos, G. (1996). Modeling the Dynamic Interdependence of Major European Stock Markets. Journal of Business Finance and Accounting, 23, pp. 975-988.
- Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992). Testing the Null Hypothesis of Trend Stationarity. Journal of Econometrics, 54, pp. 159- 178.
- Maghrebi, N., Kim, M. S. & Nishina, K. (2007). The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations. Asia- Pacific Journal of Financial Studies, 36, pp. 163- 187.
- Mayhew, S. (1995). Implied Volatility. Financial Analyst Journal, July-August, pp. 8-20.
- Moraux, F., Navatte, P. & Villa, C. (1999). The Predictive Power of the French Market Volatility Index: A Multi Horzon Study. European Finance Review, 2, pp. 303-320.
- Nikkinen, J. & Sahlström, P. (2004). International Transmission of Uncertainty Implicit in Stock Index Option Prices. Global Finance Journal, 15, pp. 1-15.
- Phillips, R. C. B. & Perron. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, pp. 335-346.
- Sarwar, G. (2010). The VIX Market Volatility Index and U.S. Stock Index Returns. International Journal of Business Research, 10, pp. 166-176.
- SIX Swiss Exchange Ltd. (2010). Guide Governing Volatility Index VSMI®. .
- Skiadopoulos, G. (2004). The Greek Implied Volatility Index: Construction and Properties. Applied Financial Economics, 14, pp. 187-1196.
- Tse, Y. K. & Tsui, A. K. C. (2002). A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, pp. 351-362.
- Wagner, N. & Szimayer, A. (2004). Local and Spillover Shocks in Implied Market Volatility: Evidence for the U.S. and Germany. Research in International Business and Finance, 18, pp. 237-251.
- Whaley, R. E. (1993). Derivatives on Market Volatility: Hedging Tools Long Overdue. Journal of Derivatives, 1, pp. 71-84.
- Whaley, R. E. (2000). The Investor Fear Gauge. Journal of Portfolio Management, 26, pp. 12-17.
- Impact of Characteristics on Outreach and Profitability of Microfinance Institution in India
Abstract Views :231 |
PDF Views:2
Authors
Affiliations
1 Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, IN
1 Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, IN
Source
International Journal of Financial Management, Vol 4, No 3 (2014), Pagination: 50-57Abstract
Purpose: This paper aims to examine how selected characteristics indicators impact on performance measures of outreach and profitability in microfinance institutions (MFIs).
Design/Methodology/Approach: The study is an exploratory nature. The data is taken from 42 microfinance institutions. A panel data technique is employed as the key analytical framework.
Findings: It is shown that the characteristics play a critical role in the performance of MFIs. Find that size and number of offices has positive impact on microfinance institution.
Research limitation/implications: Data availability and accessibility is a limitation. Microfinance institutions in India do not provide their annual report on regular basis.
Originality/value: Society may get the benefits because outreach level will increase and poor people access to credit and their standard of living will increase.
Keywords
Characteristics, Financial Performance, Performance Measurement, MFI, India.References
- Ananda, S., & Colaco, X. F. (2012). Micro finance in India: An Overview of performance and prospects. International Conference on Advances in Computing and Management.
- Ayayi, G. A. (2012). Credit risk assessment in the microfinance industry. Economics of Transition, 20(1), 37-72.
- Churchill, G. A., & Iacobucci, D. (2005). Marketing Research: Methodological Foundations, 9th edition, USA: Thomson South-Western.
- Coleman, K. A., & Oesi, A. K. (2008). Outreach and profitability of microfinance institutions: The role of governance. Journal of Economic Studies, 35(3), 236-248.
- Coleman, K. A. (2007). The impact of capital structure on the performance of microfinance institutions. The Journal of Risk Finance, 8(1), 56-71.
- Das, K. S. (2012). Social impact assessment on microfinance institutions: A review of existing literature. Asian Journal of Research in Business Economics and Management, 2(6).
- Dissanayake, M. D. (2012). The determinants of return on equity: evidences from Sri Lankan microfinance institutions. Journal of Arts, Science & Commerce.
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis. 6th edition, Pearson Education.
- Hartarska, V. (2009). The impact of outside control in microfinance. Managerial Finance, 35(12), 975-989.
- Hartungi, R. (2007). Understanding the success factors of micro-finance institution in a developing country. Impact of Characteristics on Outreach and Profitability of Microfinance Institution in India 57 International Journal of Social Economics, 34(6), 388-401.
- Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-71.
- Hulme, D., & Mosley, P. (1996). Finance against poverty, Routledge London.
- Kennedy, P. (1985). A Guide to Econometrics, (2nd ed.), MIT Press, Cambridge, MA.
- Kereta, B. B. (2007). Outreach and financial performance analysis of microfinance institutions in Ethiopia. African Economic Conference United Nations Conference Center (UNCC), Addis Ababa, Ethiopia.
- Levin, A., Lin, C., & Chu, J. (2002). Unit ischolar_main tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.
- Meyer, R. J. (2002). Track record of financial institutions in assessing the poor in Asia. Retrieved from http://www.esocialsciences.com/data/articles/Document113112009240.6883051.pdf
- Nuredin, M. (2012). Determinants of dividend policy of insurance companies in Ethiopia. Retrieved from etd.aau.edu.et/dspace/bitstream/.../4665/1/Muhammed%20nuredin.pdf
- Osotimehin, O. K., Jegede, A. C., & Akinlabi, H. B. (2011). Determinants of microfinance outreach in South-Western Nigeria: An empirical analysis. Interdisciplinary Journal of Contemporary Research in Business, 3(8).
- Pal, K., & Soriya, S. (2012). IC performance of Indian pharmaceutical and textile Industry. Journal of Intellectual Capital, 13(1), 120-137.
- Pinkowitz, L., Stulz, R., & Williamson, R. (2006). Does the contribution of corporate cash holdings and dividends to firm value depend on governance? A cross-country analysis. Journal of Finance, 61(6), 2725-51.
- Rai, A. A., & Rai, S. (2012). Factors Affecting Financial Sustainability of Microfinance Institutions. Journal of Economics and Sustainable Development, 3(6).
- Rauf, A. S., & Mahmood, T. (2009). Growth and performance of microfinance in Pakistan. Pakistan Economic and Social Review, 47(1), 99-122.
- Roy, A. (2011). Managing performance of MFIs–A look into their microfinance delivery process & profitability. International Journal for Business, Strategy & Management, 1(1).
- Shastri, K. R. (2009). Microfinance and poverty reduction in India (A comparative study with Asian Countries). African Journal of Business Management, 3(4), 136-140.
- Vichore, S., & Deshpande, S. (2012). Microfinance in India - A comprehensive analysis of the growth and performance of MFI's. The International Journal's Research Journal of Social Science & Management, 2(1), 51-56.
- Impact of Corporate Governance on the Cash Holding of the Firms:An Empirical Study of Indian Manufacturing Sector
Abstract Views :312 |
PDF Views:2
Authors
Affiliations
1 Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, IN
1 Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, IN
Source
International Journal of Financial Management, Vol 7, No 1 (2017), Pagination: 47-52Abstract
The paper empirically examines the impact of corporate governance on the cash holding of the firms. The components of corporate governance are measured by board size, board meeting, audit committee members, director's remuneration and non executive directors and the cash holding is measured with the log of average cash and size is taken as control variable for the control effect on the dependent variables. Moreover, correlation and panel regression model were employed to examine the relationship between the corporate governance and cash holding. Empirical data was collected from 96 firms over the period of 2004-05 to 2013-14. The results show that directors' remuneration and the number of audit committee members positively influence the cash holding and the board size also positively influences the cash holding whereas, the non executive directors and the board meetings do not play any role in enhancing the cash holding.Keywords
Corporate Governance, Cash Holding, India, Manufacturing Sector, Board Size.References
- Achchuthan, S., Kajananthan, R., & Sivathaasan, N. (2013). Corporate governance practices and working capital management efficiency: Special reference to listed manufacturing companies in Sri Lanka. Information and Knowledge Management, 3(2), 216-226.
- Anjum, S., & Malik, Q. A. (2013). Determinants of corporate liquidity - An analysis of cash holdings. Journal of Business and Management, 7(2), 94-100.
- Basheer, M. F. (2014). Impact of corporate governance on corporate cash holdings: An empirical study of firms in manufacturing industry of Pakistan. International Journal of Innovation and Applied Studies, 7(4), 1371-1383.
- Chaudhry, A., & Ahmad, N. (2015). Does corporate governance affect working capital management efficiency of firms? Evidence from manufacturing sector of Pakistan. A Multi-Disciplinary Journal, 27(6), 6255-6260.
- Das, S. (2014). Cash Management in Indian corporate sector. Unpublished doctoral dissertation, The University of Burdwan. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/48802 (27 April, 2016).
- Das, S. C. (2009). Corporate governance in India, (2nd ed.). New Delhi: PHI Learning Private Limited.
- Gill, A. S., & Biger, N. (2013). The impact of corporate governance on working capital management efficiency of American manufacturing firms. Managerial Finance, 39(2), 116-132.
- Gill, A., Biger, N., & Obradovich, J. (2015). The impact of independent directors on the cash conversion cycle of American manufacturing firms. International Journal of Economics and Finance, 7(1), 87-96.
- Hannan, R. Z. U., & Asghar, N. (2013). Impact of Corporate governance on corporate cash holding: Evidence from non-financial firms in Pakistan. IOSR Journal of Business and Management, 8(1), 122-125.
- Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-1271.
- Isshaq, Z., Bokpin, G. A., & Onumah, J. M. (2009). Corporate governance, ownership structure, cash holdings, and firm value on the Ghana Stock Exchange. The Journal of Risk Finance, 10(5), 488-499.
- Kamau, S. M., & Basweti, K. A. (2013). The relationship between corporate governance and working capital management efficiency of firms listed at the Nairobi securities exchange. Research Journal of Finance and Accounting, 4(19), 190-199.
- Kennedy, P. (1985). A guide to econometrics (2nd ed.). MIT Press, Cambridge, MA.
- Najjar, B. A. (2015). The Effect of governance mechanisms on small and medium-sized enterprise cash holdings: Evidence from the United Kingdom. Journal of Small Business Management, 53(2), 303-320.
- *Non executive director. (n.d.) Retrieved from http://www.investopedia.com/terms/n/non-executive-director.asp (28 April, 2016).
- Paskelian, O. G., Bell, S. B., & Neguyen, C. V. (2010). Corporate governance and cash holdings: A comparative analysis of Chinese and Indian firms. The International Journal of Business and Finance Research, 4(4), 59-73.
- Thomson, L. M., & Bureau, E. (2009). Corporate governance meaning. Retrieved from http://articles.economictimes.indiatimes.com/2009-01-18/news/28462497_1_corporate-governance-satyambooksfraud-by-satyam-founder (27 April, 2016).
- Tsai, C. C. (2012). Cash holdings and corporate governance in business group affiliated firms. International Conference on Economics Marketing and Management, 28, 83-87.
- Zhou, T. (2014). Financial crisis, excess cash holding and corporate investment: Based on the Perspective of Governance and Risk Perception. Journal of Chinese Economics, 2(2), 1-20.
- An Examination of Asymmetric Relation Between Implied Volatility Index and its Underlying Asset
Abstract Views :234 |
PDF Views:0
Authors
Affiliations
1 Finance at Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, IN
2 Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, IN
1 Finance at Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, IN
2 Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, IN
Source
International Journal of Financial Management, Vol 7, No 4 (2017), Pagination: 10-22Abstract
The volatility index is the measure of 30-day expected volatility. Its association with stock index returns provides an insight to the volatility traders to launch derivatives products so that it can be used as a hedging tool. The aim of the present study is to empirically examine the relationship between the implied volatility indices and its underlying asset in context of developed and developing markets (like U.S., Japan, Germany, and China). The empirical findings report the asymmetric behaviour which indicates that a larger impact on implied volatility indices are from negative return shocks as compared to positive returns. This evinced that the investors and traders respond highly to negative returns in low volatile period by demanding more options at high premium which makes the implied volatility high. Therefore, the negative relationship between IVIX and stock index returns makes the index relevant for investors to diversifying their portfolio so that they can mitigate the investment risk associated with the volatility.Keywords
Indian Implied Volatility Index, Informational Content, Hedging, Derivatives, Asymmetric Relationship, Day of the Week Effect.References
- Badshah, I. U. (2009). Asymmetric return-volatility relation, volatility transmission and implied volatility indexes, Working Paper, Hanken School of Business.
- Banerjee, P. S., Doran, J. S., & Peterson, D. R. (2007). Implied volatility and future portfolio returns. Journal of Banking and Finance, 31, 3183-3199.
- Bates, D. S. (2000). Post-’87 crash fears in the S&P 500 futures option market. Journal of Econometrics, 94, 181-238.
- Bollen, N. P., & Whaley, R. E. (2004). Does net buying pressure affect the shape of implied volatility functions? The Journal of Finance, 59, 711-753.
- Bollerslev, T., & Zhou, H. (2006). Volatility puzzles: A simple framework for gauging return-volatility regressions. Journal of Econometrics, 131, 123-150.
- Brenner, M., Ou, E. Y., & Zhang, J. E. (2006). Hedging volatility risk. Journal of Banking & Finance, 30, 811-821.
- Carr, P., & Wu, L. (2006). A tale of two indices. Journal of Derivatives, 13, 13-29.
- Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of financial Economics, 10, 407-432.
- Christensen, B., & Prabhala, N. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50, 125-150.
- Dennis, P., Mayhew, S., & Stivers, C. (2006). Stock returns, implied volatility innovations, and the asymmetric volatility phenomenon. Journal of Financial and Quantitative Analysis, 41, 381-406.
- Dowling, S., & Muthuswamy, J. (2005). The implied volatility of Australian index options. The Review of Futures Markets, 14, 1-37.
- Ederington, L. H., & Guan, W. (2010). How asymmetric is US stock market volatility. Journal of Financial Markets, 13, 225-248.
- Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Trading costs and the relative rates of price discovery in stock, futures, and option markets. The Journal of Futures Markets, 16, 353-387.
- French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19, 3-29.
- Frijns, B., Tallau, C., & Tourani‐Rad, A. (2010). The information content of implied volatility: Evidence from Australia. Journal of Futures Markets, 30, 134-155.
- Giner, J., & Morini, S. (2004). The VIX Index for the Prediction of Volatility: An International Study. Working Paper, Department of Financial Economics and Accounting, University of La Laguna.
- Giot, P. (2005). Relationships between implied volatility indices and stock index returns. Journal of Portfolio Management, 31, 92-100.
- Gonzalez, M. T., & Novales, A. (2007). Why a volatility index can be useful in the Spanish financial market? Working Paper, Universidad Computense de Madrid.
- Gonzalez, M. T., & Novales, A. (2009). Are volatility indices in international stock markets forward looking. RACSAM- Journal of the Real Academy of Exact, Physical and Natural Sciences. Serie A. Mathematics, 103, 339-352.
- Hibbert, A. M., Daigler, R. T., & Dupoyet, B. (2008). A behavioral explanation for the negative asymmetric returvolatility relation. Journal of Banking & Finance, 32, 2254-2266.
- Kumar, S. S. S. (2012). A first look at the properties of India’s volatility index. International Journal of Emerging Markets, 7, 160-176.
- Latane, H. A., & Rendleman, R. J. (1976). Standard deviations of stock price ratios implied in option prices. The Journal of Finance, 31, 369-381.
- Low, C. (2004). The fear and exuberance from implied volatility of S&P 100 index options. Journal of Business, 77, 527-546.
- McAleer, M., & Wiphatthanananthakul, C. (2010). A simple expected volatility (SEV) index: Application to SET50 index options. Mathematics and Computers in Simulation, 80(12), 2079-2090.
- Narwal, K. P., Mittal, R., & Chhabra, P. (2017). Volatility contagion between Indian and world stock markets: empirical evidences. Retrieved from https://www.researchgate.net/publication/320101176_Volatility_Contagion_between_Indian_and_World_Stock_Markets_Empirical_Evidences?ev=prf_high (Accessed on 30 September , 2017).
- NSE (2010). White paper India VIX, Retrieved from www.nseindia.com/content/vix/white_paper_IndiaVIX.pdf. (Accessed on 20 January, 2105).
- Pan, J. (2002). The jump-risk premia implicit in options: Evidence from an integrated time-series study. Journal of financial economics, 63, 3-50.
- Poteshman, A. M. (2001). Under reaction, overreaction, and increasing misreaction to information in the options market, The Journal of Finance, 56(3), 851-876.
- Ryu, D. (2012). Implied volatility index of KOSPI200: Information contents and properties. Emerging Markets Finance and Trade, 48, 24-39.
- Schwert, G. W. (1989). Why does stock market volatility change over time?, The Journal of Finance, 44(5), 1115-1153.
- Schwert, G. W. (1990). Stock volatility and the crash of’87. Review of financial Studies, 3, 77-102.
- Shaikh I., & Padhi, P. (2016). On the relationship between implied volatility index and equity index returns. Journal of Economic Studies, 43, 27-47.
- Simlai, P. (2010). What drives the implied volatility of index options? Journal of Derivatives & Hedge Funds, 16, 85-99.
- Simon, D. P. (2003). The Nasdaq volatility index during and after the bubble. The Journal of Derivatives, 11, 9-24.
- Siriopoulos, C., & Fassas, A. (2009). Implied volatility indices: A review. Retrieved from http://ssrn.com/abstract=1421202 (accessed 15 June, 2015).
- Skiadopoulos, G. (2004). The Greek implied volatility index: construction and properties. Applied Financial Economics, 14(16), 1187-1196.
- Szakmary, A., Ors, E., Kim, J. K., & Davidson, W. N. (2003). The predictive power of implied volatility: Evidence from 35 futures markets. Journal of Banking and Finance, 27(11), 2151-2175.
- Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management, 26, 12-17.