Rising Tensions Over China's Monopoly on Rare Earths?

Rising Tensions Over China's Monopoly on Rare Earths?, by June Nakano
East-West Center Asia Pacific Bulletin, no. 163
Washington, DC, May 2, 2012
http://www.eastwestcenter.org/publications/rising-tensions-over-china%E2%80%99s-monopoly-rare-earths

Jane Nakano, Fellow with the Energy and National Security Program at the Center for Strategic and International Studies, explains that "the current rare earth contention should serve as a reminder of the fundamental importance of supply diversification, and the enduring value that research and development plays in meeting many of the energy and resource related challenges society faces today."

Excerpts:

The recent Chinese industry consolidation may not be a welcome development as it will most likely increase the price of many rare earth materials. However, it is probably too short-sighted to view this move as a simple measure to side-step international complaints about China’s restrictive export policies on rare earth materials. In reality, the consolidation likely has multiple objectives, such as to demonstrate to the Chinese public an effort to both curb pollution and eradicate illegal mining, to ensure an adequate level of supply to domestic consumers, and to encourage higher value exports—if the consolidation leads to an in-flow of foreign rare earth processors to China. It would be neither easy nor particularly meaningful to determine which factor is most dominant.

Too Much Finance?

Too Much Finance? By Jean-Louis Arcand, Enrico Berkes, and Ugo Panizza
IMF Working Paper No. 12/161
June, 2012
http://www.imfbookstore.org/ProdDetails.asp?ID=WPIEA2012161

Summary: This paper examines whether there is a threshold above which financial development no longer has a positive effect on economic growth. We use different empirical approaches to show that there can indeed be "too much" finance. In particular, our results suggest that finance starts having a negative effect on output growth when credit to the private sector reaches 100% of GDP. We show that our results are consistent with the "vanishing effect" of financial development and that they are not driven by output volatility, banking crises, low institutional quality, or by differences in bank regulation and supervision.

Excerpts:

Introduction

In this paper we use different datasets and empirical approaches to show that there can indeed be “too much” finance. In particular, our results show that the marginal effect of financial depth on output growth becomes negative when credit to the private sector reaches 80-100% of GDP. This result is surprisingly consistent across different types of estimators (simple cross-sectional and panel regressions as well as semi-parametric estimators) and data (country-level and industry-level). The threshold at which we find that financial depth starts having a negative effect on growth is similar to the threshold at which Easterly, Islam, and Stiglitz (2000) find that financial depth starts having a positive effect on volatility. This finding is consistent with the literature on the relationship between volatility and growth (Ramey and Ramey, 1995) and that on the persistence of negative output shocks (Cerra and Saxena, 2008). However, we show that our finding of a non-monotone relationship between financial depth and economic growth is robust to controlling for macroeconomic volatility, banking crises, and institutional quality.

Our results differ from those of Rioja and Valev (2004) who find that, even in their “high region,” finance has a positive, albeit small, effect on economic growth. This difference is probably due to the fact that they set their threshold for the "high region" at a level of financial depth which is much lower than the level for which we start finding that finance has a negative effect on growth.

Our results are instead consistent with the vanishing effect of financial depth found by Rousseau and Wachtel (2011). If the true relationship between financial depth and economic growth is non-monotone, models that do not allow for non-monotonicity will lead to a downward bias in the estimated relationship between financial depth and economic growth.

Monitoring Systemic Risk Based on Dynamic Thresholds

Monitoring Systemic Risk Based on Dynamic Thresholds. By Kasper Lund-Jensen
IMF Working Paper No. 12/159
June 2012
http://www.imfbookstore.org/ProdDetails.asp?ID=WPIEA2012159

Summary: Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.

Excerpts:

Introduction

The financial crisis in 2007–09, and the following global economic recession, has highlighted the importance of a macroprudential policy framework which seeks to limit systemic financial risk.  While there is still no consensus on how to implement macroprudential policy it is clear that successful implementation is contingent on establishing robust methods for monitoring systemic risk.3 This current paper makes a step towards achieving this goal. Systemic risk assessment in real time is a challenging task due to the intrinsically unpredictable nature of systemic financial risk. However, this study shows, in a fixed effect binary response model framework, that systemic risk does contain a component which varies in a predictable way through time and that modeling this component can potentially improve policy decisions.

In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and I am interested in modeling and forecasting this (potentially) time varying probability. If different systemic banking crises differ completely in terms of underlying causes, triggers, and economic impact the conditional crisis probability will be unpredictable. However, as illustrated in section IV, systemic banking crises appear to share many commonalities. For example, banking crises are often preceded by prolonged periods of high credit growth and tend to occur when the banking sector is highly leveraged.

Systemic risk can be characterized by both cross-sectional and time-related dimensions (e.g.  Hartmann, de Bandt, and Alcalde, 2009). The cross-sectional dimension concerns how risks are correlated across financial institutions at a given point in time due to direct and indirect linkages across institutions and prevailing default conditions. The time series dimension concerns the evolution of systemic risk over time due to changes in the macroeconomic environment. This includes changes in the default cycle, changes in financial market conditions, and the potential build-up of financial imbalances such as asset and credit market bubbles. The focus in this paper is on the time dimension of systemic risk although the empirical analysis includes a variable that proxies for the strength of interconnectedness between financial institutions.

This paper makes the following contributions to the literature on systemic risk assessment: Firstly, it employs a dynamic binary response model, based on a large panel of 68 advanced and emerging economies, to identify leading indicators of systemic risk. While Demirgüç-Kunt and Detragiache (1998a) study the determinants of banking crises the purpose of this paper is to evaluate whether systemic risk can be monitored in real time. Consequently, it employs a purely dynamic model structure such that the systemic risk forecasts are based solely on information available in real time. Furthermore, the estimation strategy employed in this paper is consistent under more general conditions than a random effect estimator used in other studies (e.g.  Demirgüç-Kunt and Detragiache (1998a) and Wong, Wong and Leung (2010)). Secondly, this paper shows how to derive risk factor thresholds in the binary response model framework. The threshold of a single risk factor is dynamic in the sense that it depends on the value of the other risk factors and it is argued that this approach has some advantages relative to static thresholds based on the signal extraction approach.4 Finally, I perform a pseudo out-of-sample analysis for the period 2001–2010 in order to assess whether the risk factors provided early-warning signals ahead of the recent financial crisis.

Based on the empirical analysis, I reach the following main conclusions:

1. Systemic risk, as defined here, does appear to be predictable in real time. In particular, the following risk factors are identified: banking sector leverage, equity price growth, the credit-to-GDP gap, real effective exchange rate appreciation, changes in the banks’ lending premium and the degree of banks interconnectedness as measured by the ratio of non-core to core bank liabilities. There is also some evidence which suggests that house price growth increases systemic risk but the effect is not statistically significant at conventional significance levels.

2. There exists a significant contagion effect between economies. When an economy with a large financial sector is experiencing a systemic banking crisis, the systemic risk forecasts in other economies increases significantly.

3. Rapid credit growth in a country is often associated with a higher level of systemic risk.  However, as highlighted in a recent IMF report (2011), a boom in credit can also reflect a healthy market response to expected future productivity gains as a result of new technology, new resources or institutional improvements. Indeed, many episodes of credit booms were not followed by a systemic banking crisis or any other material instability. It is critical that a policymaker is able to distinguish between these two scenarios when implementing economic policy. I find empirical evidence which suggests that credit growth increases systemic risk considerably more when accompanied by high equity price growth. Therefore, I argue that the evolution in equity prices can be useful for identifying a healthy credit expansion.

4. In a crisis signaling exercise, I find that the binary response model approach outperforms the popular signal extracting approach in terms of type I and type II errors.

5. Based on a model specification with credit-to-GDP growth, banking sector leverage and equity price growth I carefully evaluate the optimal credit-to-GDP growth threshold.  Contrary to the signal extraction approach the optimal threshold is not static but depends on the value of the other risk factors. For example, the threshold is around 10 percent if equity prices have decreased by 10 percent and banking sector leverage is around 130 percent but only around 0 percent if equity prices have grown by 20 percent and banking sector leverage is 160 percent. In comparison, the signal extraction method leads to a (static) credit-to-GDP growth threshold of 4.9 percent based on the same data sample.

6. In the out-of-sample analysis, I find that the systemic risk factors generally provided informative signals in many countries. Based on an in-sample calibration, around 50– 80 percent of the crises were correctly identified in real time without constructing too many false signals. In particular, a monitoring model based on credit-to-GDP growth and banking sector leverage signaled early warning signals ahead of the U.S. subprime crisis in 2007.

Obama Family Portrait

Fiscal Transparency, Fiscal Performance and Credit Ratings

Fiscal Transparency, Fiscal Performance and Credit Ratings. By Arbatli, Elif; Escolano, Julio
IMF Working Paper No. 12/156
June 2012
http://www.imf.org/external/pubs/cat/longres.aspx?sk=25996.0


Summary: This paper investigates the effect of fiscal transparency on market assessments of sovereign risk, as measured by credit ratings. It measures this effect through a direct channel (uncertainty reduction) and an indirect channel (better fiscal policies and outcomes), and it differentiates between advanced and developing economies. Fiscal transparency is measured by an index based on the IMF’s Reports on the Observance of Standards and Codes (ROSCs). We find that fiscal transparency has a positive and significant effect on ratings, but it works through different channels in advanced and developing economies. In advanced economies the indirect effect of transparency through better fiscal outcomes is more significant whereas for developing economies the direct uncertainty-reducing effect is more relevant. Our results suggest that a one standard deviation improvement in fiscal transparency index is associated with a significant increase in credit ratings: by 0.7 and 1 notches in advanced and developing economies respectively.

Bringing Africa Back to Life: The Legacy of George W. Bush

Bringing Africa Back to Life: The Legacy of George W. Bush. By Jim Landers
Dallas Morning News
June 08, 2012


LUSAKA, Zambia — On a beautiful Saturday morning, Delfi Nyankombe stood among her bracelets and necklaces at a churchyard bazaar and pondered a question: What do you think of George W. Bush?
“George Bush is a great man,” she answered. “He tried to help poor countries like Zambia when we were really hurting from AIDS. He empowered us, especially women, when the number of people dying was frightening. Now we are able to live.”

Nyankombe, 38, is a mother of three girls. She also admires the former president because of his current campaign to corral cervical cancer. Few are screened for the disease, and it now kills more Zambian women than any other cancer.

“By the time a woman knows, she may need radiation or chemotherapy that can have awful side effects, like fistula,” she said. “This is a big problem in Zambia, and he’s still helping us.”

The debate over a president’s legacy lasts many years longer than his term of office. At home, there’s still no consensus about the 2001-09 record of George W. Bush, with its wars and economic turmoil.
In Africa, he’s a hero.

“No American president has done more for Africa,” said Festus Mogae, who served as president of Botswana from 1998 to 2008. “It’s not only me saying that. All of my colleagues agree.”
AIDS was an inferno burning through sub-Saharan Africa. The American people, led by Bush, checked that fire and saved millions of lives.

People with immune systems badly weakened by HIV were given anti-retroviral drugs that stopped the progression of the disease. Mothers and newborns were given drugs that stopped the transmission of the virus from one generation to the next. Clinics were built. Doctors and nurses and lay workers were trained. A wrenching cultural conversation about sexual practices broadened, fueled by American money promoting abstinence, fidelity and the use of condoms.

“We kept this country from falling off the edge of a cliff,” said Mark Storella, the U.S. ambassador to Zambia. “We’ve saved hundreds of thousands of lives. We’ve assisted over a million orphans. We’ve created a partnership with Zambia that gives us the possibility of walking the path to an AIDS-free generation. This is an enormous achievement.”

Bush remains active in African health. Last September, he launched a new program — dubbed Pink Ribbon Red Ribbon — to tackle cervical and breast cancer among African women. The program has 14 co-sponsors, including the Obama administration.


Read the rest here: http://www.bushcenter.com/blog/2012/06/11/icymi-bringing-africa-back-to-life-the-legacy-of-george-w-bush

Systemic Risk and Asymmetric Responses in the Financial Industry

Systemic Risk and Asymmetric Responses in the Financial Industry. By López-Espinosa, Germán; Moreno, Antonio; Rubia, Antonio; and Valderrama, Laura
IMF Working Paper No. 12/152
June, 2012
http://www.imf.org/external/pubs/cat/longres.aspx?sk=25991.0

Summary: To date, an operational measure of systemic risk capturing non-linear tail comovement between system-wide and individual bank returns has not yet been developed. This paper proposes an extension of the so-called CoVaR measure that captures the asymmetric response of the banking system to positive and negative shocks to the market-valued balance sheets of individual banks. For the median of our sample of U.S. banks, the relative impact on the system of a fall in individual market value is sevenfold that of an increase. Moreover, the downward bias in systemic risk from ignoring this asymmetric pattern increases with bank size. The conditional tail comovement between the banking system and a top decile bank which is losing market value is 5.4 larger than the unconditional tail comovement versus only 2.2 for banks in the bottom decile. The asymmetric model also produces much better estimates and fitting, and thus improves the capacity to monitor systemic risk. Our results suggest that ignoring asymmetries in tail interdependence may lead to a severe underestimation of systemic risk in a downward market.

Excerpts:

In this paper, we discuss the suitability of the general modeling strategy implemented in Adrian and Brunnermeier (2011) and propose a direct extension which accounts for nonlinear tail comovements between individual bank returns and financial system returns. Like most VaR models, the CoVaR approach builds on semi-parametric assumptions that characterize the dynamics of the time series of returns. Among others, the procedure requires the specification of the functional form that relates the conditional quantile of the whole financial system to the returns of the individual firm. The model proposed by Adrian and Brunnermeier (2011) assumes that system returns depend linearly on individual returns, so changes in the latter would feed proportionally into the former. This assumption is simple, convenient, and to a large extent facilitates the estimation of the parameters involved and the generation of downside-risk comovement estimates. On the other hand, this structure imposes certain limitations, as it neglects nonlinear patterns in the propagation of volatility shocks and of perturbations to the risk factors affecting banks' exposures. Both patterns feature distinctively in downside-risk dynamics.

There are strong economic arguments that suggest that the financial system may respond nonlinearly to shocks initiated in a single institution. A sizeable, positive shock in an individual bank is unlikely to generate the same characteristic response (i.e., comovement with the system) in absolute terms than a massive negative shock of the same magnitude, particularly if dealing with large-scale financial institutions.2 The disruption to the banking system caused by the failure of a financial institution may occur through direct exposures to the failing institution, through the contraction of financial services provided by the weakening institution (clearing, settlement, custodial or collateral management services), or from a shock to investor confidence that spreads out to sound institutions under adverse selection imperfections (Nier, 2011). Indeed, an extreme idiosyncratic shock in the banking industry, will not only reduce the market value of the stocks a¤ected, but may also spread uncertainty in the system rushing depositors and lending counterparties to withdraw their holdings from performing institutions and across unrelated asset classes, precipitating widespread insolvency. Historical experience suggests that a confidence loss in the soundness of the banking sector takes time to dissipate and may generate devastating e¤ects on the real economy. Bernanke (1983) comes to the conclusion that bank runs were largely responsible of the systemic collapse of the financial industry and the subsequent contagion to the real sectors during the Great Depression. Another channel of contagion in a downward market is through the fire-sales of assets initiated by the stricken institution to restore its capital adequacy, causing valuation losses in firms holding equivalent securities. This mechanism, induced by the generalized collateral lending practices that are prevalent in the wholesale interbank market, can exacerbate price volatility in a crisis situation, as discussed by Brunnermeier and Pedersen (2009).  The increased complexity and connectedness of financial institutions can generate "Black Swan" effects, morphing small perturbations in one part of the financial system into large negative shocks on seemingly unrelated parts of the system. These arguments suggest that the financial system is more sensitive to downside losses than upside gains. In such a case, the linear assumption involved in Adrian and Brunnermeier (2011) would neglect a key aspect of downside risk modeling and lead to underestimate the extent of systemic risk contribution of an individual bank.

We propose a simple extension of this procedure that encompasses the linear functional form as a special case and which, more generally, allows us to capture asymmetric patterns in systemic spillovers. We shall refer to this specification as asymmetric CoVaR in the sequel. This approach retains the tractability of the linear model, which ensures that parameters can readily be identified by appropriate techniques, and produces CoVaR estimates which are expected to be more accurate. Furthermore, given the resultant estimates, the existence of nonlinear patterns that motivate the asymmetric model can be addressed formally through a standard Wald test statistic. In this paper, we analyze the suitability of the asymmetric CoVaR in a comprehensive sample of U.S. banks over the period 1990-2010. We find strong statistical evidence suggesting the existence of asymmetric patterns in the marginal contribution of these banks to the systemic risk. Neglecting these nonlinearities gives rise to estimates that systematically underestimate the marginal contribution to systemic risk. Remarkably, the magnitude of the bias is tied to the size of the firm, so that the bigger the company, the greater the underestimation bias. This result is consistent with the too-big-to-fail hypothesis which stresses the need to maintain continuity of the vital economic functions of a large financial institution whose disorderly failure would cause significant disruption to the wider financial system.3 Ignoring the existence of asymmetries would thus lead to conservative estimates of risk contributions, more so in large firms which are more likely to be systemic. Accounting for asymmetries in a simple extension of the model would remove that bias.

 Concluding Remarks

In this paper, we have discussed the suitability of the CoVaR procedure recently proposed by Adrian and Brunnermeier (2011). This valuable approach helps understand the drivers of systemic risk in the banking industry. Implementing this procedure in practice requires specifying the unobservable functional form that relates the dynamics of the conditional tail of system's returns to the returns of an individual bank. Adrian and Brunnermeier (2011) build on a model that assumes a simple linear representation, such that returns are proportional.

We show that this approach may provide a reasonable approximation for small-sized banks.  However, in more general terms, and particularly for large-scale banks, the linear assumption leads to a severe underestimation of the conditional comovement in a downward market and, hence, their systemic importance may be perceived to be lower than their actual contribution to systemic risk. Yet, how to measure and buttress e¤ectively the resilience of the financial system to losses crystallizing in a stress scenario is the main concern of policy makers, regulatory authorities, and financial markets alike. Witness the rally on U.S. equities and dollar on March 14, 2012 after the regulator announced favorable bank stress test results for the largest nineteen U.S. bank holding companies.

The reason is that the symmetric model implicitly assumes that positive and negative individual returns are equally strong to capture downside risk comovement. Our empirical results however, provide robust evidence that negative shocks to individual returns generate a much larger impact on the financial system than positive disturbances. For a median-sized bank, the relative impact ratio is sevenfold. We contend that this non-linear pattern should be acknowledged in the econometric modeling of systemic risk to avoid a serious misappraisal of risk. Moreover, our analysis suggests that the symmetric specification introduces systematic biases in risk assessment as a function of bank size. Specifically, the distortion caused by a linear model misspecification is more pronounced for larger banks, which are also more systemic on average. Our results show that tail interdependence between the financial system and a bottom-size decile bank which is contracting its balance sheet is 2.2 times larger than its average comovement. More strikingly, this ratio reaches 5.4 for the top-size decile bank. This result is in line with the too-big-to-fail hypothesis and lends support to recent recommendations put forth by the Financial Stability Board to require higher loss absorbency capacity on large banks. Likewise, it is consistent with the resolution plan required by the Dodd-Frank Act for bank holding companies and non-bank financial institutions with $50 billion or more in total assets. Submitting periodically a plan for rapid and orderly resolution in the event of financial distress or failure will enable the FDIC to evaluate potential loss severity and minimize the disruption that a failure may have in the rest of the system, thus performing its resolution functions more e¢ ciently. This measure will also help alleviate moral hazard concerns associated with systemic institutions and strengthen the stability of the overall financial system.

To capture the asymmetric pattern on tail comovement, we propose a simple yet e¤ective extension of the original CoVaR model. This extension preserves the tractability of the original model and its suitability can formally be tested formally through a simple Wald-type test, given the estimates of the model. We show that this simple extension is robust to more general specifications capturing non-linear patterns in returns, though at the expense of losing tractability.

The refinement of the CoVaR statistical measure presented in the paper aims at quantifying asymmetric spillover e¤ects when strains in banks' balance sheets are elevated, and thus contributes a step towards strengthening systemic risk monitoring in stress scenarios.  Furthermore, its focus on tail comovement originated from negative perturbations in the growth rate of market-valued banks' balance sheets, may yield insights into the impact on the financial system from large-scale deleveraging by banks seeking to rebuild their capital cushions. This particular concern has been recently rekindled by the continued spikes in volatility in euro area financial markets. It has been estimated that, following pressures on the European banking system as banks cope with sovereign stress, European banks may shrink their combined balance sheet significantly with the potential of unleashing shockwaves to emerging economies hurting their financial stability (IMF, 2012). The estimation of the impact on the real economy from aggregate weakness of the financial sector, and the design of optimal macroprudential policies to arrest systemic risk when tail interdependencies feed non-linearly into the financial system, are left for future research.