گسترش اوراق قرضه در بازار نوظهور برآورد و تست بازگشت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13777||2012||28 صفحه PDF||سفارش دهید||13541 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Emerging Markets Review, Volume 13, Issue 4, December 2012, Pages 598–625
We estimate sovereign bond spreads of 28 emerging economies over the period January 1998–December 2011 and test the ability of the model in generating accurate in-sample predictions for bond spreads. The impact and significance of explanatory variables on spreads vary across regions and periods. During crisis times, good macroeconomic indicators are helpful in containing spreads, but less than in non-crisis times, possibly reflecting the impact of extra-economic forces on spreads when a financial crisis occurs. For some economies, in-sample predictions of the monthly changes in spreads obtained with rolling regression routines are more accurate than those obtained with random guessing.
Sovereign debt securities have become a key method of funding for many emerging market economies as well as an increasingly important asset class for investors. A relevant question for policymakers and investors is whether the difference between the yield of a given emerging market sovereign bond and the yield of a United States Treasury debt security of a comparable maturity – the sovereign bond spread – is appropriately priced in relation to the country-specific fundamentals of that particular emerging economy. If the sovereign bond spread stays at very low levels for long without reflecting the economy's fundamentals, sudden shifts in the investors' perception of risk may lead to sharp changes in the cost of external borrowing for that particular economy. Against that background, many studies propose a wealth of empirical models to estimate sovereign bond spreads using country-specific and common explanatory variables. This paper contributes to the debate of the role played by country-specific and global explanatory variables to explain emerging market sovereign bond spreads. Apart from macroeconomic and external vulnerability indicators, we consider an index for political risk among the country-specific explanatory variables. We also contribute to the empirical literature by testing the ability of our empirical model in generating accurate in-sample predictions for bond spreads. In this context, this study attempts to answer the following questions. Does the contribution of country specific variables change when the time and country dimensions of the panel change? Can an empirical model – used to estimate sovereign bond spreads – generate in-sample predictions for sovereign bond spreads which are more informative than those obtained with random guessing? Following Hartelius (2006), we estimate emerging economy sovereign bond spreads using a panel of 28 emerging economies, over the period January 1998–December 2011 and allow for the dimensions of the panel to change. After estimation, we back-test the model by generating bond spread in-sample predictions with linear predictions and rolling regression routines. We are interested to establish (i) which of the methods is more successful at correctly predicting the direction of the monthly change in bond spreads, (ii) whether the forecasting accuracy of each method changes before and after the global financial turmoil of 2008, and (iii) to test whether the forecasting methods employed are more accurate than a random walk in predicting the monthly change in bond spreads. In the first part of the paper, we find that better country-specific fundamentals are associated with lower bond spreads, although their impact on spreads varies across periods and regions. This implies that over time and across regions of emerging economies investors do not always assign the same importance to country-specific variables when investing in emerging economies' sovereign bonds. We also find that the impact of global explanatory variables, as well as their statistical significance, in explaining bond yield spreads changes with the economic period considered.1 Specifically, US short and long-term interest rates are no longer significant if the time dimension of the panel includes only the period beginning with the latest Global Financial Crisis. In addition, the model fails to explain well the rising bond spreads observed in some of the emerging economies between 2010 and 2011. This could reflect concerns that international investors might have about the potential impact of the euro area economic downturn on emerging economies and on their borrowing costs. Finally, we find that during crisis times, good macroeconomic fundamentals are helpful in containing bond yield spreads, but less than in non-crisis times. This might reflect the likely impact of extra-economic forces on bond yield spreads when a financial crisis occurs. In the second part of the paper, we assess the ability of the model to generate accurate predictions for bond spreads. For some emerging economies – Colombia, Mexico, and Poland – forecasts of the monthly changes in actual bond spreads obtained with rolling regression routines are significantly more accurate than forecasts obtained with a random walk model. The results enrich the literature because they suggest that rolling regression method can in some cases be more accurate than a random walk model in generating predictions for bond spreads, perhaps reflecting the fact that rolling regression routines allow to gradually enrich the information set available to investors. This paper is structured as follows. Section 2 contains a review of the relevant literature for this study. Section 3 describes the data while Section 4 describes the empirical methodology employed. Section 5 presents the estimation results while the back-testing exercise of the model is presented in Section 6. Concluding remarks are in Section 7.
نتیجه گیری انگلیسی
This study was divided into two parts. In the first part, in a baseline regression we estimated sovereign bond yield spreads for 28 emerging market economies using a set of country-specific and global factors, over the period January 1998–December 2011. We also ran the same regression while allowing for the dimensions of the panel to change, and calculated the improvement in the fitted bond spreads following a hypothetical improvement in the country-specific explanatory variables. The second feature of this study was to back-test the model to assess the ability of the model to generate accurate in-sample forecasts for bond spreads. We generated bond spread forecasts with three competing forecasting methods: linear prediction and two rolling regression routines. For each method used, we checked whether actual and predicted bond spreads changed in the same direction during each month of the forecasting period. Then, we compared the forecasting accuracy of both methods by running the Diebold–Mariano (1995) test. Finally, we compared the accuracy of each forecasting method against that of random walk model in predicting bond spreads. A number of conclusions can be drawn from this study. First, the results show that the coefficient estimates and statistical significance of country-specific and global explanatory variables on bond spreads may vary across time and regions. One possible reason for this finding is that over time and across different emerging economies, investors do not always assign the same weight to country-specific and global factors when selecting which sovereign bonds to hold in their portfolios. From an econometric perspective, the results imply that the coefficient estimates of the explanatory variables and their statistical significance may be sensitive to the dimensions of the panel. Changing the dimensions of the panel may lead to different coefficient estimates and may change the degree of statistical significance for the explanatory variables. However, from a policymaking perspective, despite country-specific explanatory variables may not always be significant to explain bond spreads the results show that good country-specific fundamentals tend to reduce the external cost of borrowing. Second, the model fails to fully explain the increase in sovereign bond spreads observed in 2010 and 2011 in some emerging economies. The increase in sovereign bond spreads, hence yields, could be related to concerns that international investors might have about the potential impact of the euro area economic downturn on emerging economies and on their borrowing costs. Related to this result, we also find that during crisis times, good macroeconomic fundamentals are helpful in containing bond yield spreads, but less than in non-crisis times. Perhaps this is because extra-economic forces are also responsible for the movement in bond yield spreads when a financial crisis occurs. Third, changes in the degree of external vulnerability are estimated to cause the largest changes in the cost of external borrowing for emerging economies. Improvements in the degree of external vulnerability are three times more effective than improvements in the economic risk rating and twice more effective than improvements in the political risk rating in lowering the cost of external borrowing. Improvements in the degree of political risk are estimated to be twice as more powerful than improvements in the economic risk rating to lower the cost of external borrowing. The results of these simulations imply that a low degree of external vulnerability and a high degree of political stability can substantially reduce the cost of external borrowing. From a policy perspective, the simulation results underscore the importance for emerging economies to adopt measures aiming to reduce their degree of external vulnerability such as, for example, developing local currency bond markets in order to reduce the reliance on external debt financing. In addition, the simulation results highlight the importance of having in place (or building) strong institutions to achieve and maintain political stability. Failure to do so may have negative implications for the cost of external borrowing. Finally, we generated in-sample bond spread predictions with two competing forecasting methods. We ran the Diebold–Mariano (1995) test for forecasting accuracy to rank the three competing forecasting methods. Bond spread predictions obtained with rolling regression routines tend to be more accurate than those obtained with linear prediction, possibly reflecting that rolling regression routines allow gradually enriching in every period the information set available to market participants, unlike in the case of the linear prediction forecasting method. We also tested whether the three forecasting methods used were significantly more accurate in predicting the monthly changes in bond spreads than a naïve forecasting method (e.g. a random walk model). For some countries – Colombia, Mexico and Poland – forecasts of the monthly changes in bond spreads obtained with rolling regression routines were significantly more accurate than forecasts obtained with a random walk model. The findings suggest that the rolling regression method can in some cases be more accurate than a random walk model to generate predictions for bond spreads. By contrast, the linear prediction method does not deliver more information compared to a random walk model. An implication of this finding is that rolling regression routines can be useful when forecasts for bond spreads are needed for scenario analyses to simulate the path of sovereign bond spreads and to measure the degree of fiscal distress. This study can be extended in a number of directions. As regards the estimation part of this study, we use indices for political, economic and financial risks from the International Country Risk Guide (ICRG) as country-specific controls in the regression for spreads. While these indices allow for a range of variables to be taken into account and introduced in the model in a parsimonious way, it would be interesting to check how the results look like when these indices are “unbundled” (e.g. use external debt/GDP, external debt/exports, current account/GDP and reserve adequacy indicators as controls rather than the ICRG Financial Risk Rating index). Similarly, it would be interesting to include to the country-specific explanatory variables data for real GDP growth, inflation, current account balance, and industrial production in emerging economies instead of the ICRG Economic Risk Rating index. It would also be interesting to consider other global explanatory variables such as the slope of the U.S. yield curve, as well as other panel estimation techniques that allow estimating the short-term dynamics of bond yield spreads. Another interesting extension would be running separate regressions for each country and compare the forecast performance of the panel regression (where variables such as the VIX have a common coefficient for all countries), with one where it is ran separately for each country (where all variables have a country-specific coefficient). As regards the forecasting part of the study, another line of work could be to conduct more experiments to see whether sovereign bond spread forecasts have desirable properties for being used as a leading indicator of financial crises. Finally, if would be interesting to back-test a model where the panel includes also advanced economies in addition to emerging ones.