“…Over the last two decades, a vast amount of literature has investigated the relationship between movements in interest rates and equity returns (Alaganar et al, 2003;Ballester et al, 2011;Bartram, 2002;Broome and Morley, 2000;Elyasiani and Mansur, 1998;Ferrando et al, 2017;Ferrer et al, 2010;Flannery and James, 1984;Jareño et al, 2016;Lynge and Zumwalt, 1980;Reilly et al, 2007;Sweeney and Warga, 1986, among others). Much of this research has focused on stock returns of banking firms because of the peculiar nature of the financial intermediation business of those firms, as a large part of their revenues and costs depend directly on interest rates.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The pioneering studies utilized linear OLS regression (Flannery and James, 1984;Sweeney and Warga, 1986). However, subsequent contributions have applied more sophisticated techniques, including the cointegration analysis (Broome and Morley, 2000;Chan et al, 1997), nonlinear models (Ballester et al, 2011;Bartram, 2002) or different types of GARCH (generalized autoregressive conditional heteroscedasticity) models (Dajcman, 2012;Elyasiani and Mansur, 1998;Kasman et al, 2011).…”
“…Over the last two decades, a vast amount of literature has investigated the relationship between movements in interest rates and equity returns (Alaganar et al, 2003;Ballester et al, 2011;Bartram, 2002;Broome and Morley, 2000;Elyasiani and Mansur, 1998;Ferrando et al, 2017;Ferrer et al, 2010;Flannery and James, 1984;Jareño et al, 2016;Lynge and Zumwalt, 1980;Reilly et al, 2007;Sweeney and Warga, 1986, among others). Much of this research has focused on stock returns of banking firms because of the peculiar nature of the financial intermediation business of those firms, as a large part of their revenues and costs depend directly on interest rates.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The pioneering studies utilized linear OLS regression (Flannery and James, 1984;Sweeney and Warga, 1986). However, subsequent contributions have applied more sophisticated techniques, including the cointegration analysis (Broome and Morley, 2000;Chan et al, 1997), nonlinear models (Ballester et al, 2011;Bartram, 2002) or different types of GARCH (generalized autoregressive conditional heteroscedasticity) models (Dajcman, 2012;Elyasiani and Mansur, 1998;Kasman et al, 2011).…”
“…Some other methodologies that are worth mentioning are the ones able to detect the presence, or lack of common cycles among asset prices. Broome and Morley (2000) use a cointegration technique for testing the presence of long-run common trends among stock prices and the risk free interest rate and perform dependence analyses to investigate the presence and features of short-run common cycles among the same quantities. In Chen and Wun Lin (2004) linear and non-linear Granger causality tests are used to examine the dynamical dependence relationships between spot and future prices.…”
Section: A Short Review Of the Recent Literaturementioning
“…Some other methodologies that are worth mentioning are the ones able to detect the presence, or lack of common cycles among asset prices. Broome and Morley (2000) use a cointegration technique for testing the presence of long-run common trends among stock prices and the risk free interest rate and perform dependence analyses to investigate the presence and features of short-run common cycles among the same quantities. In Chen and Wun (2004) linear and non−linear Granger causality based tests are used to examine the dynamical dependence relationships between spot and future prices.…”
Section: A Short Review Of the Recent Literaturementioning
Abstract. Comovements among asset prices have received a lot of attention for several reasons. For example, comovements are important in cross−hedging and cross−speculation; they determine capital allocation both domestically and in international mean-variance portfolios and also, they are useful in investigating the extent of integration among financial markets. In this paper we propose a new methodology for the non-linear modelling of bivariate comovements. Our approach extends the ones presented in the recent literature. In fact, our methodology outlined in three steps, allows the evaluation and the statistical testing of non−linearly driven comovements between two given random variables. Moreover, when such a bivariate dependence relationship is detected, our approach solves for a polynomial approximation. We illustrate our three-steps methodology to the time series of energy related asset prices. Finally, we exploit this dependence relationship and its polynomial approximation to obtain analytical approximations of the Greeks for the European call and put options in terms of an asset whose price comoves with the price of the underlying asset.
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