The results showed that subjective well-being was the main predictor of life satisfaction and hedonic model also predicted a small amount of this variable. For happiness the predictors were the same but in reversed order, the main predictor was the hedonic model and a small variance was explained by subjective well-being. Contrary to our hypothesis the eudaimonic perspective of happiness was not a predictor in none of the models.
These results underline the importance of the interaction between a cognitive or appraisal perspective and the hedonic perspectives for the study of happiness. Key words: happiness, life satisfaction, subjective well-being. El estudio de la felicidad estuvo dominado con el modelo de bienestar subjetivo. Cantril introduced a measurement of life satisfaction with an eleven point single item, graphically represented as a ladder, anchored at the lower end with the phrase "Worst possible life for you" and at the top with the phrase "Best possible life for you".
Participants were asked, "Where on the ladder do you stand at the present time? Bradburn introduced a different measurement approach regarding life satisfaction called the Affect Balance Scale for the measurement of positive and negative affect. Another perspective was adopted by Andrews and Withey who created the Delighed-Terrible Scale, which asked the respondents "How do you feel about your life as a whole? A seven-point response scale was provided, ranging from "terrible" to "delighted".
Well ahead of his time, Fordyce introduced a measurement of happiness with a single eleven-point item, which also included the average percent of the time participant felt happy, unhappy, and in a neutral mood. The next development was made by Diener, Emmons, Larsen, and Griffin who developed a five item scale for the measurement of life satisfaction. These authors considered life satisfaction as the overall cognitive judgmental process of diverse areas of their life.
Even though in the past psychology was mainly concerned with the measurement and conditions that elicit and modify negative emotions and psychopathology, this perspective changed when positive psychology entered the field. Happiness, for ancient philosophers, was considered the highest good and the essential motivation for all human actions. Three main perspectives dominated the study of happiness. The first one is represented by the work Diener ; ; ; ; ; , which it was present even before the appearance of positive psychology and persisted afterwards.
It is a cognitive-emotional perspective, designed subjective well-being and considered as being composed by number of different separable components: global appraisal or judgment about one's life and fulfillment, satisfaction with work, family, capacity for experiencing positive affect, pleasant emotions and moods, and low levels of negative affect or experiencing few unpleasant emotions and moods.
The second perspective is the hedonic, related to the emotional content of happiness or how individuals feel about their lives. It was first defined by Kahneman as the study of what makes experiences and life pleasant or unpleasant. Happiness is about maximizing the rewards, optimizing the events associated with pleasure and minimizing the events associated with displeasure or pain. According to this perspective policies that improve the frequency of good experiences and reduce the incidence of the bad ones should be actively pursued.
This hedonic approach typically emphasized the importance of engaging in the pursuit of positive emotional experiences, such as pleasure and comfort, and is experienced by an increasing frequency of pleasurable moments or feelings of moment-to-moment pleasure and to get the pleasures one wants Fredrickson, ; ; Kahneman, The third perspective about happiness is the eudaimonic. It emphasizes the concepts of personal growth and meaning in life and also includes concepts as purpose, autonomy, competence, self-realization, self-acceptance, authenticity, values congruence, and social connectedness.
These three perspectives represent related and overlapped conceptions of the same phenomenon, but they also could be reliably distinguishable aspects of happiness. Whereas subjective well-being is mainly the global cognitive judgment and affective experience about of one's life and fulfillment, the hedonic perspective emphasizes pleasure, and the eudaimonic perspective emphasizes meaning and virtue. Happiness research had major societal implications and suggested a shift from a materialistic view of society to a perspective of a society based on values Fleurbaey, A commission created in the beginning of , on an initiative of the French government, recommended adding subjective well-being measures to existing indicators of societal progress such as gross domestic product.
Even though life satisfaction and happiness are sometimes used as synonyms, they have different meanings. Whereas life satisfaction refers to the person thoughts, to a cognitive judgment or evaluation of life circumstances, happiness, on the other hand, seems to refer to the hedonic tone or the quality of emotional life experience. Since the reports of life satisfaction and happiness seem to be qualitative different experiences, they should have different antecedent life circumstances. The aim of this research was to study the possible different relationships between the two global reports of the most widely used, life satisfaction or happiness, and the three models of happiness, the subjective well-being or cognitive, the hedonic and the eudemonic.
It was specifically predicted that life satisfaction will be predicted by subjective well-being and flourishing models, whereas happiness will be predicted by the hedonic model. One sample of participants, males and females, was used in this study. Males had a mean age of Females had a mean age of All measures were administered in following order: first the two one item global measures of life satisfaction and happiness, followed by the measures of subjective wellbeing, hedonic happiness and eudemonic happiness,. Life satisfaction. Participants were presented an step ladder, where the bottom step was marked with 0, the worst life possible , and the last step with a 10, the best possible life.
Participants were asked "If you imagine your own life last month, where do you stand on the ladder, from the worst possible to best possible life you can imagine? On what step of the ladder is your life? It was assessed with the Happiness Thermometer, an point scale for the assessment of happiness during the last month, graphically represented by a thermometer that ranged from 0, extremely unhappy , represented by a sad schematic face, to 5, neutral , represented by a neutral schematic face, to 10, extremely happy , represented by a happy schematic face.
A similar measure showed good test-retest reliability,. Subjective well-being. Participants indicated, for example, how satisfied they were with their lives and how close their life was to their ideal life. The SWLS consists of five items, e. A coefficient alpha of.
The scale was validated for the Portuguese population in a sample of adolescents Neto, Eudaimonic happiness. Participants were asked to rate each item referring to the last month, e. A coefficient alpha of 0. The scale was validated to the Portuguese population by Silva and Caetano Hedonic happiness.
Each item was scored on a 5-point scale, referring to the last month, ranging from 1, very rarely or never , to 5, very often or always. The positive and negative scales are computed separately, resulting on one score for positive feelings and other score for negative feelings. The two scores could also be combined by subtracting the negative score from the positive score forming the affect balance. In this study only the affect balance was computed. The coefficients alpha reports for positive feelings were. We used SPSS version 20 for all data analyses. The t tests were made to find differences between the sexes.
Correlations were reported as Pearson product moment correlations two-tailed for all continuous variables. To explore the predictive value of the life satisfaction and happiness dimensions as the independent variables, stepwise regression analysis were performed, with the subjective well-being, eudaimonic and hedonic happiness as the dependent variables. All variables were highly intercorrelated, as shown in Table 1. The correlations between happiness and subjective wellbeing, eudaimonic and hedonic happiness were respectively. Using as independent variables the subjective well-being, eudaimonic and hedonic happiness, life satisfaction and happiness were predicted in two regression equations with the stepwise method.
Based on the above results the path model tested was as follows: the subjective well-being and hedonic happiness was hypothesized to influence the life satisfaction. Obtaining the results are shown in Figure 1. In this case the path model tested was as follows: the subjective well-being and hedonic happiness was hypothesized to influence the happiness. The results are shown in Figure 2. Eudaimonic happiness was not a predictor for life satisfaction or for happiness. The global reports of life satisfaction and happiness in the last month measured by one item scales, the ladder of life satisfaction and the happiness thermometer, were highly correlated and were predicted by the same variables, but in different ways.
Even though the reports of life satisfaction and happiness were correlated with all three perspectives of happiness analyzed in this study, cognitive or subjective well-being, eudemonic and hedonic, the regression analysis showed the different strength of these predictors. In the present study the eudaimonic perspective, assessed by Flourishing Scale Diener et al. In this study, both global reports were presented to the participants in a similar way by a question and a by a graphic representation, the ladder for life satisfaction and the thermometer for happiness, which helped to the understanding of the measured concepts.
The graphical presentation of the scales increases the comprehension level, in particular for participants with lower literary qualifications. It seems that the two one-item global measures tapped two different concepts of the phenomena. Whereas life satisfaction, measured by the Cantril' ladder, is a more cognitive or an appraisal measure of life satisfaction, which also includes people's emotional responses, the happiness thermometer is the opposite, i.
We can think about this as two continuous rating scales, one cognitive and the other emotional. The Cantril' ladder can be defined as composed by scoring higher in the cognitive scale and lower on the emotional one, whereas the happiness thermometer is the reverse, scoring higher on the emotional and lower on the cognitive scale.
Helliwell suggested that life evaluations could be differentiated from mood assessments in two principal ways. Life evaluation is more stable than mood, which had more fluctuations, and life evaluations are more closely related to life circumstances than happiness. A cognitive based measure, the Cantril' ladder, should be more attuned to the circumstances of life than happiness.
For instance Kahneman and Deaton were able to report an analysis of more than , responses to the Gallup-Healthways Well-Being Index, a daily survey of 1, US residents conducted by the Gallup Organization that showed that life satisfaction, measured by the Cantril' ladder scale, and the emotional well-being, measured by the reports of positive feelings, have different correlates. They concluded that high income was related to life satisfaction but did not maintain for happiness, whereas low income was related both to low life evaluation and low emotional well-being.
As suggested also by Diener, Kahneman, Tov and Arora and Oishi and Diener the different reports of life satisfaction and happiness reflect different conditions. The cognitive report of life evaluation reflects the prevalent economic conditions; whereas the affective report of happiness reflect the conditions of life, for instance the free time.
Also it is accepted that integrating the eudaimonic and hedonic perspectives should lead to a more comprehensive understanding of well-being and of the pathways to wellbeing. The results of the present study underscore another perspective for the happiness research, the interaction between the hedonic and cognitive or appraisal models of happiness. The results of present research are limited to the young adult sample used in this study, the generalization of the findings should be investigated namely in child and older samples.
Abdel-Khalek, A. Measuring happiness with a single-item scale. Social Behavior and Personality, 34, Abbott, R. An evaluation of the precision of the measurement of Ryff psychological well-being scales in a population sample. Social Indicators Research. Aknin, L. Happiness runs in a circular motion: Evidence for a positive feedback loop between prosocial spending and happiness.
Journal of Happiness Studies, 13, Andrews, F. Social indicators of well-being. New York: Plenum Press. Argyle, M. Happiness as a function of personality and social encounters. Innes Eds. North-Holland: Elsevier. Asano, R. The Japanese Journal of Psychology, 85, Bartels, M. Born to be happy? The etiology of subjective well-being. Behavior Genetics, 39, Baird, B. Life satisfaction across the lifespan: Findings from two nationally representative panel studies.
Benjamin, D. What do you think would make you happier?
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What do you think you would choose? American Economic Review, 5, Berridge, K. Building a neuroscience of pleasure and well-being. Boyce, C.
Personality prior to disability determines adaptation: agreeable individuals recover lost life satisfaction faster and more completely. Psychological Science, 22, Bradburn, N. The structure of psychological well-being. Chicago: Aldine. Cantril, H. The pattern of human concerns. Deaton, A. Income, aging, health and wellbeing around the world: Evidence from the Gallup World Poll. Journal of Economic Perspectives, 22, Delle Fave, A.
The eudaimonic and hedonic components of happiness: Qualitative and quantitative findings. Social Indicators Research, , Diener, E. Psychological Bulletin, 95, Assessing subjective well-being: Progress and opportunities. Social Indicators Research, 31, A value based index for measuring national quality of life. Social Indicators Research, 36, Subjective well-being: The science of happiness, and a proposal for a national index. American Psychologist, 55, New findings and future directions for subjective well-being research. American Psychologist, 67, The remarkable changes in the science of subjective wellbeing.
Perspectives on Psychological Science, 8, The Satisfaction with Life Scale. Journal of Personality Assessment, 49, Benefits of accounts of wellbeing - for societies and for psychological science. Applied Psychology, 57, Wealth and happiness across the world: Material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling. Journal of Personality and Social Psychology, 99, Diener E, Inglehart, R. Theory and validity of life satisfaction scales. Beyond money: Toward an economy of well-being. Psychological Science in the Public Interest, 5, The personality structure of affect.
Journal of Personality and Social Psychology, 69, Subjective well-being and human welfare around the world as reflected in the Gallup World Poll. International Journal of Psychology, 50, Income's association with judgments of life versus feelings. Diener, J. Kahneman Eds. Social Indicators Research, 97, Di Tella, R. Happiness adaptation to income and to status in an individual panel. Dolan, P. This finding corresponds to the traditional view of banks as financial intermediaries borrowing short-term from savers and lending long-term to investors positive duration gap.
Accordingly, a rise in interest rates would adversely affect a bank's market value the present value of its assets would fall more than the present value of its liabilities and net interest income the cost of its liabilities would increase more rapidly than the yield on its assets. Second, bank stock returns tend to exhibit more sensitivity to changes in long-term interest rates than to changes in short-term rates Elyasiani and Mansur, ; Bartram, ; Saporoschenko, ; Czaja et al.
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Third, the interest rate sensitivity of stock returns of banks has declined over time mainly due to the increased availability of more advanced tools for measuring and managing IRR Faff and Howard, ; Benink and Wolff, ; Ryan and Worthington, ; Joseph and Vezos, Even though the literature on corporate exposure to IRR in the Spanish case has received a considerable boost in recent years, this field has not been fully explored.
Yet, it is possible to distinguish two lines of research. These studies demonstrate the high interest rate sensitivity of various sectors such as construction, real estate, electrical, and banking. It is worth noting that the implicit assumption underlying almost all the literature is that interest rate exposure is linear. Consequently, much less attention has been paid to other possible IRR patterns. In fact, the great majority of studies about corporate exposure to macroeconomic risks e.
Despite the above mentioned, there are some empirical papers that explore the possibility of a profile of exposure more complex than the linear one. The seminal work in this field was done by Chen and Chan , who investigate for potential asymmetry of interest rate sensitivity of U. A significant asymmetry is found during up and down cycles of interest rates, suggesting that the sensitivities of bank stock returns are highly sample-dependent.
In the same vein, Hallerbach documents that the sensitivity of the Dutch stock market to changes in interest rates is not constant over time, showing a clear pattern of asymmetry to interest rate fluctuations of different sign. He points out that the specification of a nonlinear model could partly explain the asymmetry between sensitivities to interest rate rises and falls. More recently, Verma and Jackson utilize a multivariate EGARCH exponential generalized autoregressive conditional heteroskedastic model to evaluate the presence of spillover effects and asymmetries between short- and long-term interest rates and portfolios of US bank stocks.
Their results provide evidence of response asymmetries for the portfolios of money center and other large banks, indicating that these banks are more sensitive to negative than positive interest rate changes.. In a very interesting paper, Bartram analyzes the impact of IRR on a large sample of German nonfinancial corporations at the industry level. His results support the existence of significant linear and nonlinear exposures with respect to changes in several interest rate variables. Ferrer et al.
A significant linear and nonlinear interest rate exposure is found for the construction, real estate, electrical, utility and banking industries, although the traditional linear exposure pattern is economically more important than the nonlinear one. Using smooth transition regression models, Arango et al. The sample consists of commercial banks listed on the Spanish Stock Exchange over the period from January to December whose stocks traded publicly for at least a year a total of 23 banking institutions. Due to factors such as mergers and acquisitions and IPOs, the number of firms included in the sample varies over time.
This sample selection procedure uses all the bank data available at the end of each year, hence minimizing the survivor bias and improving the efficiency of the estimation.. The period of study allows us to investigate whether the introduction of the euro in January did induce a significant alteration in the pattern of interest rate exposure of Spanish commercial banks. To this end, the total sample period is split into two subsamples, the pre-euro period, from January to December , and the post-euro period, from January to December The adoption of the euro as a common European currency is a major historical event in international financial markets.
Thus, it is likely to have a significant impact on the risks incurred by European banks in their activity. The euro may affect interest rate exposure of banks through two main channels. First, since the launch of the common currency Eurozone interest rates are set by the European Central Bank, which implements a single monetary policy for the euro area as a whole, with no national bias. Thus, the environment of more stable and historically low interest rates and greater transparency in monetary policy brought about by the European Monetary Union is expected to reduce the extent of IRR faced by European banking institutions.
Following a usual practice in the literature Flannery and James, ; Hirtle, ; Benink and Wolff, ; Soto et al. The weekly returns are calculated from Wednesday to Wednesday using closing stock prices in order to prevent the possible bias associated to the weekend effect. Weekly rather than daily data are utilized because sometimes the market takes a while to understand and reflect the effects of interest rate changes on asset prices.
Thus, the use of very short daily horizon returns can make it much more difficult to properly assess a firm's interest rate exposure. In addition, weekly data are preferred over monthly data because of the availability of a much larger number of observations that allows us to obtain more precise results. The market portfolio is proxied by the Indice General de la Bolsa de Madrid , the widest Spanish value-weighted market index. Equity market data are obtained from the Madrid Stock Exchange database.. The year Spanish government bond yield and the 3-month interbank rate are used as proxies for Spanish long- and short-term interest rates, respectively.
Long-term interest rates are those that further incorporate expectations about future prospects for the economy and determine to a greater extent the cost of borrowed funds. Therefore, it seems reasonable to assume that long-term rates have a greater influence on corporate investment decisions and the expected future profitability of firms. In addition, 3-month interbank rates may also play a critical role as the money market has become increasingly important for Spanish banks in recent years due to two main reasons.
First, interbank rates are widely used as reference rates for a great variety of variable-rate products, both on the asset and the liability side of the balance sheet. Second, banking institutions have relied heavily on the interbank market to finance the extraordinary credit expansion within the framework of the Spanish housing boom. All interest rate data are extracted from the Bank of Spain's database.. Along the lines followed by, among others, Elyasiani and Mansur , Faff et al. The advantage of forming portfolios is twofold.
First, it provides an efficient way for condensing a substantial amount of information about stock return behaviour. Second, it helps to smooth out the noisiness in the data due to transitory shocks to individual banks, hence producing more reliable results. The portfolio analysis may, however, mask the potential dissimilarities among individual firms within each portfolio.
The sample is disaggregated by size into three equally weighted portfolios large banks, medium banks and small banks. A total of seven banking institutions, representative of the Spanish mid-size banks, are included within this category. Table 1 lists the individual banks included in the analysis and their allocation among the three portfolios, along with their respective stock ticker symbol, number of observations, and average amount of total assets.
Some descriptive statistics for the returns on individual banks and size-based portfolios are also reported. Hence, the null hypothesis of normality of returns is clearly rejected in all cases.. Composition of bank portfolios and descriptive statistics of individual and portfolio weekly returns Entire sample period — This table lists the Spanish individual commercial banks considered in this study and their grouping into three portfolios based on their size: large banks portfolio portfolio L , medium banks portfolio portfolio M and small banks portfolio portfolio S.
The stock ticker symbol, number of observations and average volume of total assets for each individual bank are also reported. Descriptive statistics associated with individual and portfolio weekly returns are presented as well.. Table 2 contains summary information on the interest rate series. For both the full sample period and the two sub-periods the average yield on year government bond yields is higher than the average 3-month interbank rate. It should be also noted that the average year and 3-month rates take substantially lower values in the post-euro era.
Further, the year yield series has the lowest standard deviation regardless of the sample period considered, consistent with the idea that volatility of interest rates usually decreases as maturity increases. As expected, interest rate volatility has significantly declined following the introduction of the euro, confirming the greater stability in interest rates during the post-euro period.
Descriptive statistics of the interest rate series.. This table contains descriptive statistics for the series of interest rates considered in this study. Summary statistics are presented for the full sample period and the pre- and post-euro periods. IR10 denotes the series of yields on year Spanish government bonds and IR3 the series of 3-month interbank rates. Time evolution of the series of interest rates.. This section provides a brief description of the model specifications employed.
The two-index linear regression model proposed by Stone is the benchmark model in the literature to quantify the degree of bank interest rate exposure.follow
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The inclusion of a market index is designed to control for market-wide factors, mitigating the omitted variable bias and improving the efficiency of the estimation. Hence, it can be interpreted as a measure of exposure to IRR. Note that a negative interest rate exposure coefficient corresponds to the traditional view of banks as borrowing short-term and lending long-term.. In order to obtain a measure of the total interest rate exposure of asset i , an orthogonalization procedure is implemented.
The same strategy has been followed by, among others, Hirtle , Fraser et al. This residual series is usually called the residual market factor and represents the part of the market returns that cannot be explained by changes in interest rates. Obviously, the residual market factor is uncorrelated with interest rate fluctuations by construction.. Then, the original market return is replaced by the residual market factor estimated from Eq.
Residuals also coincide implying the same R 2 in the empirical estimations of models in Eqs. The main advantage of using the specification in Eq. As pointed out by Czaja et al. Moreover, the same orthogonalization approach is used in all the other models described below..
Early empirical studies of corporate exposure to IRR focused almost exclusively on linear exposure. Nevertheless, as noted by Bartram , the value of a firm, defined as the present value of all its expected cash flows, may depend in a very complex way on interest rates since movements in interest rates affect both discount rates and expectations about future cash flows. Further, most companies typically employ risk management instruments with linear payoffs e.
In contrast, nonlinear exposure is much less taken into account by firms when designing their hedging strategies. This implies a higher chance of finding empirically a significant nonlinear exposure, which in turn could be hedged using instruments with nonlinear payoff schedules such as interest rate options. Therefore, in order to gain a better understanding of the nature of IRR borne by firms it is interesting to examine the presence of nonlinear effects in their interest rate exposure..
Even so, it is very difficult to impose a specific functional form a priori to characterize the nonlinear exposure as the shape of the exposure may not be uniform across companies. In fact, the exact form of nonlinearity may be a complex function of different firm characteristics such as financial leverage, profitability, size, liquidity or risk management practices.
This study represents a first attempt to assess the presence of nonlinear exposure, to be completed later on with the nonparametric model. Therefore, a simplifying approach, which is based on the assumption that some generic nonlinear functions are enough to accurately capture the nonlinearities, is used. A nonlinear specification implies that the interest rate sensitivity depends on the size of the interest rate shock..
Relevant nonlinear functions in this context can be classified as concave and convex functions. Concave functions e. Accordingly, these functional forms do not seem very appropriate to provide a realistic measure of the impact of interest rate changes on bank equity prices. In contrast, convex functions e. In particular, convex functions are consistent with the idea that small changes in interest rates are probably dominated by other price relevant information and have less or even no effect on stock prices, while large interest rate shocks have a greater impact on stock prices.
Consequently, convex functions appear to be more appropriate to model a nonlinear relationship between interest rate fluctuations and stock returns.. In any case, there is no consensus about the most convenient convex function to be used in order to estimate a nonlinear interest rate exposure. Further, this function is sign-sensitive, allowing us to distinguish between the effect of interest rate rises and that of interest rate falls. Therefore, the cubic function will be used in this study.
Additionally, it is worth to point out that the parametric models are estimated for each bank portfolio applying OLS with the Newey-West procedure to correct standard errors for autocorrelation and heteroscedasticity.. On the one hand, bank asset returns may react differently to interest rate rises and falls sign asymmetry. On the other hand, large and small interest rate shocks size or magnitude asymmetry may impact differently on bank asset returns.
To allow for these asymmetries, the basic model in Eq. Specifically, the sign asymmetry can be analyzed using the following model:. Analogously, the size or magnitude asymmetry can be assessed through the following model:. Both models can be used to estimate the coefficients associated to interest rate fluctuations of different sign or size, but they do not offer a direct test of asymmetry.
Thus, in order to directly test the asymmetry hypothesis Eqs. All the model specifications discussed above require a specific functional form and assume that it does not change during the sample period. The primary advantage of this method is its flexibility, as it allows estimating the relationship between movements in interest rates and bank stock returns without adhering to a particular function. Specifically, the local linear regression developed by Stone is employed to avoid the well-known problem of misspecification in the functional form inherent to traditional parametric models.
The basic idea behind the local linear approach is to fit a linear regression locally around a neighbourhood of each data point in the sample, giving a greater weight to closer neighbours. This procedure has a higher asymptotic efficiency and allows for faster convergence at boundary points compared to other nonparametric methods Fan and Gijbels, The kernel function assigns weights to the data points improving the system of local averaging. This function assigns more importance, and so weight, to a point closer to the point of interest than to one further away.
The Gaussian kernel, which is one of the most popular kernel functions in financial applications, is used in this study. As Fan and Gijbels indicate, the choice of the bandwidth parameter may have crucial repercussions on the results of nonparametric regressions. Following a usual practice in the literature, when the standard Gaussian kernel is employed the optimal bandwidth is computed according to the Silverman's rule of thumb.
After estimating the coefficient b j for every point in the sample, the sample mean of these pointwise estimates can be used in the same way as the estimated coefficient of the parametric regression model. Rilstone shows that this estimator is consistent and asymptotically normally distributed and its standard errors are comparable to those from traditional parametric estimation. As a result, hypothesis tests can be easily conducted to compare the nonparametric estimates with their parametric counterparts..
Table 3 presents the interest rate exposure coefficients from the different models. The first four columns report the coefficient estimates of the parametric specifications, and the fifth column shows the estimates of the nonparametric approach. Panel A provides the exposure estimates for the entire sample period, and Panels B and C for the pre-euro and post-euro sub-periods, respectively.. Exposure of bank portfolios to interest rate risk.. This table contains the interest rate exposure coefficients from estimating the parametric and nonparametric models considered for the three bank portfolios over the entire sample, pre- and post-euro periods.
The year Spanish government bond yield and 3-month interbank rate are used as proxies of market interest rates. Parametric models in Eq. The cubic function is used for the estimation of the nonlinear model. Nonparametric model in Eq. The last column of this table reports the average nonparametric estimates. Standard errors are shown in parentheses. In the particular case of the nonparametric model, the statistical significance of the coefficients is given by the estimation output of the NP package and not the standard t test.
The exposure coefficients from the two-index linear model in Eq. This implies that Spanish banks are, on average, adversely impacted by rises in interest rates. The inverse relationship between movements in interest rates and bank stock returns is consistent with the typical bank balance sheet maturity structure, where long-term assets are funded with short-term liabilities positive duration gap. This negative link is also in accordance with most of the existing literature on bank IRR e.
The interest rate sensitivity of banking firms varies depending on the interest rate variable chosen. Thus, the exposure coefficients associated with changes in year government bond yields are larger in absolute value that those estimated with 3-month interbank rates. Further, the small banks portfolio shows the lowest coefficients in absolute values and R 2 , suggesting that smaller banks are the least vulnerable to linear IRR. This is consistent with the idea that Spanish small banks have a stock market behaviour hardly influenced by interest rate fluctuations, but rather dependent on idiosyncratic risk factors..
The results of the nonlinear specification broadly corroborate the findings of the linear model. As is shown in the second column of Table 3 , the cubic function permits to identify an important extent of nonlinear exposure to IRR during the entire sample period. In particular, all bank portfolios exhibit a significant nonlinear exposure irrespective of the interest rate variable under consideration. The sign of the nonlinear coefficients is negative in all cases, confirming that decreases in interest rates have a positive effect on Spanish banking firms. This result reinforces the widespread view that banks tend to maintain a positive mismatch between the maturity of their assets and liabilities.
As in the linear model, the exposure coefficients are larger in absolute value when movements in year government bond yields are used and the lowest coefficients and R 2 are observed for the small banks portfolio.. Since the independent variables in the linear and nonlinear models are different, in order to compare the economic importance of both types of exposure the product of the estimated exposure coefficient with one standard deviation of the interest rate proxy is computed for all portfolios exhibiting both significant linear and nonlinear exposure.
As argued by Bartram , this procedure makes the coefficients comparable as it standardizes the variables across regression specifications. The results displayed in Table 4 indicate that the linear exposure coefficients are always greater, in absolute value, than nonlinear coefficients during the entire sample period. This implies that, in general, the linear interest rate exposure of Spanish banks is economically more important than the nonlinear exposure measured using a cubic function.
This finding coincides with that reported by Ferrer et al.
Economic significance of linear and nonlinear exposures.. This table reports the interest rate exposure coefficients multiplied by one standard error of the interest rate proxy for the three bank portfolios. Panel A refers to the whole sample period and Panels B and C to the pre- and post-euro periods, respectively. The exposures are estimated by regressing the portfolio returns on the market return and the interest rate variable. Nonlinear exposure is estimated using the cubic function.. In line with the previous specifications, the findings of the estimation of the sign and size asymmetric models, also reported in Table 3 , show the prevalence of negative exposure for the whole sample.
With regard to the sign asymmetry, bank portfolio returns seem, in general, more sensitive to falling than to rising interest rates, especially for movements in 3-month interbank rates. Concerning the size or magnitude asymmetry, larger interest rate fluctuations appear to have a greater impact on portfolio returns than smaller interest rate changes.
Once again, the lowest explanatory power of the asymmetric models is observed for the small banks portfolio. However, the results of the direct tests of sign and magnitude asymmetry presented in Table 5 do not support the existence of significant asymmetries in interest rate exposure for the full sample period, principally for the medium and small banks portfolios. Asymmetries in interest rate exposure.. This table shows the results of the direct tests for sign and size or magnitude asymmetry in interest rate exposure using Eqs.
Panel A refers to the full sample period and Panels B and C to the pre- and post-euro periods, respectively. Numbers in parentheses are standard errors.. The interpretation of nonparametric estimates is more complicated than that of linear parametric estimates. This is because the simple linear model assumes that the response of the dependent variable to changes in any explanatory variable is constant regardless of the level of the explanatory variable, while the nonparametric methods do not place such restrictions on the data.
The estimation output of a nonparametric regression model consists of an estimate of the regression function and the marginal effect or response coefficient for each regressor at every point in the sample. Accordingly, this output can be difficult to interpret, being instructive its graphical representation.. Nonparametric response coefficients of bank portfolio returns to changes in interest rates. Note : The solid lines indicate the estimated nonparametric response coefficients. In order to facilitate comparisons with the parametric models, the fifth column of Table 3 presents the average estimates, computed as the sample mean of the pointwise estimates, of the interest rate sensitivity of each portfolio and their associated standard errors obtained with the nonparametric analysis.
As it can be seen, the mean nonparametric estimates are very similar, both in absolute value and statistical significance, to the linear estimates. Thus, the nonparametric approach supports the results of the parametric estimations in terms of the negative influence of interest rate fluctuations, the higher sensitivity to changes in year government bond yields, and the lower vulnerability of smaller banks to IRR.
Interestingly, the regression standard errors are much lower for nonparametric estimates than for their parametric counterparts, indicating that more reliable and precise results are obtained by using the nonparametric analysis.
It is also important to note that the nonparametric model produces higher R 2 than the different parametric specifications considered. These findings seem to suggest than the nonparametric approach is better able to model the impact of IRR on Spanish banks than the parametric models.. The analysis by sub-periods reveals a sharp reduction in interest rate exposure during the post-euro era for all the model specifications, portfolios and interest rate variables.
This seems to indicate that the relative importance of IRR in explaining bank stock return variability has declined since the launch of the euro. A possible explanation for this finding is closely related to the smaller variability of interest rates during the post-euro period in an environment of historically low interest rates along with the greater availability of more advanced IRR management tools.
In this regard, banking institutions may have benefited from the large-scale use of interest rate derivatives and the increasing depth and breadth of European corporate bond markets with the advent of the euro to improve their management of IRR.. In the pre-euro period all the significant exposure coefficients have negative sign irrespective of the model considered.
Further, there seems to be a size effect as the large banks portfolio always exhibits the highest interest rate exposure.
In turn, year government bond yields are found to exert the highest influence in absolute value on the stock performance of Spanish banks. Also, the absolute values of the pre-euro exposure coefficients are greater than those corresponding to the entire sample period.. The post-euro period shows, however, very different results. The number of significant exposure coefficients is considerably lower than that obtained in the pre-euro era regardless. Moreover, the few significant coefficients are almost all positive, suggesting that for this period decreases in interest rates would adversely affect Spanish banks.
This result is in conflict with the negative relationship between bank stock returns and interest rate fluctuations typically documented in the literature e. Nevertheless, this evidence is in line with the results of Ferrer et al. Two key reasons may help to explain this apparently anomalous finding positive exposure. On the one hand, the massive use of adjustable rate products tied to interbank rates since the mids, mainly in the mortgage segment. On the other hand, the extraordinary expansion of asset securitization along with the increased use of interest rate derivatives for hedging purposes may also have played a crucial role in this context.
Thus, when interest rates are very low banking firms face to a narrowing of the lending-deposit rate spread since a positive interest rate on their deposit accounts is required by customers. This argument is consistent with the gradual compression in bank margins within an environment of pronounced decline in interest rates and fierce competition as the occurred in the Spanish banking industry over the past decade..
The response plots of bank portfolio returns to changes in interest rates under the nonparametric approach presented in Fig. Furthermore, it is worth noting a substantial increase in the standard errors of the estimates for the post-euro period regardless of the model under consideration.
This loss of precision implies that more caution in the interpretation of the results for the post-euro era is required. In addition, the R 2 are much higher for the first sub-period irrespective of the model specification, interest rate proxy, and bank portfolio considered, so indicating a better model fit. These findings suggest that both the parametric and nonparametric models work reasonably well in the pre-euro period, but not equally well in the post-euro period, supporting, therefore, the notion that the degree of interest rate exposure faced by Spanish banks has significantly been reduced after the adoption of the euro.
Lastly, the tests of asymmetry in Table 5 show the almost total absence of significant sign and size asymmetries in interest rate exposure during the pre- and post-euro periods.. To check the robustness of the results, the analysis at the portfolio level is complemented with a firm-level analysis. Overall, the findings at the individual bank level, reported in Table 6 , are broadly consistent with those from the portfolios.
Thus, the negative interest rate exposure prevails for individual banks during the whole sample and pre-euro periods irrespective of the model under consideration. In contrast, the post-euro period exhibits a substantially lower degree of exposure and the impact of IRR is predominantly positive, indicating that banking firms now would take advantage from rises in interest rates.
Further, the results of this robustness analysis confirm that changes in year government bond yields have a larger influence on the stock market performance of Spanish banks than movements in 3-month interbank rates.. Percentage of individual banks with significant interest rate exposure.. This paper presents a comprehensive analysis of the interest rate exposure of the Spanish banking industry employing both parametric and nonparametric estimation methods. Its main contribution is to use, for the first time in the field of the measurement of IRR faced by banks, a nonparametric regression model that avoids the prior specification of a specific functional form..
The empirical analysis sheds light on several important issues. Overall, the Spanish banking sector shows a significant degree of interest rate exposure, although the introduction of the euro has led to a substantial decline of the impact of interest rate changes on bank stock returns. This lower interest rate sensitivity during the post-euro period may be a result of factors such as the remarkable stability in interest rates in the historically low interest rate environment associated with the European monetary union, or the increased availability of improved tools for managing IRR in recent years.
Contrary to the evidence typically documented in the literature, a pattern of positive exposure seems to emerge in the post-euro era, which can be attributed to two main reasons. Second, the positive exposure may also reflect the downward pressure on bank margins arising from increased competition in a scenario of marked downward trend in interest rates in force over the last years.
A significant nonlinear exposure to IRR, measured through a cubic function, is also found, but the traditional linear exposure profile prevails in terms of economic magnitude over the nonlinear one. Further, there is no evidence of significant sign and size asymmetries during the full sample period and the pre- and post-euro periods..
The results of the nonparametric estimation in terms of both absolute value and statistical significance of exposure coefficients are similar to those from the parametric specifications. However, the standard errors of the nonparametric estimates are much lower, and the nonparametric model has greater explanatory power than the parametric models. These findings support the reliability of the nonparametric approach to assess the extent of IRR faced by Spanish banks.
The better performance of the nonparametric model may be attributed to its high flexibility to capture nonlinear effects in the link between bank stock returns and interest rate fluctuations, and supports the expansion of the conventional linear model to gain a better insight into the degree of exposure to IRR. Moreover, the results by sub-periods indicate that the fit of the models is substantially better in the pre-euro period. Another interesting result is that the lowest interest rate sensitivity is observed for the small banks portfolio regardless of the model under consideration, suggesting that Spanish smaller banks, because of their idiosyncratic nature, have a market stock performance less vulnerable to IRR..
The authors are also grateful to the two anonymous referees for their comments that have contributed to improve this paper..
Aspectos clínicos y epidemiológicos de las leucemias
ISSN: Indexed in: Scopus See more Follow us:. Discontinued publication For more information click here. Previous article Next article. Issue 2. Pages July - December More article options. Linear and nonlinear interest rate sensitivity of Spanish banks. Download PDF. Laura Ballester?? Corresponding author. Ballester uv.
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