Comments by Don Hanna, on Financial Conditions Indexes and Monetary Policy in Asia

Don Hanna: Because of the rise in the relative importance of capital flows and financial development across Asia in the run up to and following the 2008—09 global financial crisis (GFC) and the more recent deceleration in trade volumes relative to growth, monetary policy, with its closer link to capital flows and finance, has taken on more prominence. But the greater burden shouldered by monetary policy has also raised the question of what factors beyond simply inflation and growth should central banks use in calibrating monetary policy—or, more precisely, what transmission channels transmit monetary policy changes to the economy. From this concern has evolved work on financial conditions indexes (FCIs)—an effort to better measure the variety of channels through which monetary policy can affect an economy. Initially an effort to include the effects of exchange rate movements and money by the Bank of Canada and the Reserve Bank of New Zealand (Freedman [1995]; RBNZ [1996]; the work has expanded to include a wider array of indicators as exemplified in Hatzius et al. [2010] or the Chicago Federal Reserve Bank [Brave and Butters 2011]). The paper by Dubuque-Gonzalez and Gochoco-Bautista is an effort to apply the methodology of Hatzius et al. (2010)—common factor analysis—to eight Asian countries. The authors’ main goals are to: Apply a common factor methodology to the construction of FCIs for eight Asian countries allowing for a broader array of variables with an unbalanced panel;Test the usefulness of the new FCIs in forecasting measures of inflation and output relative to an alternative model excluding the financial variables; andInvestigate whether the FCIs support a continuation of Asian monetary policy linkages to U.S. monetary policy.My comments will focus largely on the issues surrounding the construction of the FCIs in large part because shortcomings there make the assessment of the second two questions in the paper difficult to substantiate.Following Hatzius et al., in constructing their FCIs the authors exclude the lagged effects of macro variables so as to better isolate the effects of financial conditions to future macro performance: where X is the financial indicator, Y is the vector of macro variables, and v is an error term. Furthermore, following Hatzius et al., the authors only use output and inflation as explanatory macro variables. There is no effort to include a measure of external balance. This seems a major oversight given the open nature of most Asian economies and the apparent efforts by central banks to condition monetary policy on exchange rate outcomes (implicitly prioritizing external balance). The United States is a large, relatively closed economy where the Federal Reserve's attitude toward the exchange rate is in most years one of benign neglect. Judging from the policy statements of central bankers in Asia, this is far from the case there. In Indonesia, for example, the Central Banking Law cites maintaining a stable value of the rupiah as the Bank's key goal (Republic of Indonesia 2004). By leaving out external balance, the “financial conditions” measure is likely to be seriously flawed.Beyond the questions of cleaning the data more effectively for macroeconomic interactions not spurred by finance, there is also an issue with how one should select the financial indicators to include in the analysis. Looking at the U.S. FCI, significant variables are connected to the collateralized debt obligation and mortgage market, two areas that grew prior to the GFC on the back of financial innovation. If innovation is a source of advancement, but also stress, then in looking for variables to include in the FCI, we should focus on fast-growing new financial institutions and markets. In the Asian context, such a list might include: •measures of informal credit markets;•accounts payable/accounts receivable; and•curb market interest rates.The importance of new, fast-growing markets in driving instability is potentially problematic for an FCI that has a static composition. In looking for linkages between financial conditions and macroeconomic outcomes older relationships could be less consequential, requiring one to combine an FCI with analysis of the growth and size of new financial instruments. Although cross-country comparison can be helpful in the case of financial instruments that are simply new to the country, globally innovative instruments create a greater challenge for assessing financial conditions. Being innovative, analysts will have no historical basis to assess their effects, introducing Knightian uncertainty into the financial system. Unless one is willing to forego financial innovation, such uncertainty is unavoidable and a unfortunate limitation to FCIs.Beyond the questions of the risks associated with financial innovation, there is also the difficulties that arise, not from financial flows per se, but rather from their accumulation on balance sheets, particularly those of households and firms. Such measures, though, are absent from the analysis—in part, no doubt, because stock measures are harder to come by.In introducing the need for Asian FCIs, the authors note the importance of financial market contagion. If risks of contagion were an important component of how financial conditions affect macroeconomic conditions generally, then one would suppose that measures of capital account openness might be useful components of an FCI. Capital account openness should affect the channels and intensity of financial contagion. The authors’ FCIs, however, do not include measures of capital account openness.Related to the issues of contagion is the use by Asian central banks (and others) of macro-prudential regulations, rather than more traditional capital flow measures. Net open positions in foreign exchange limits for banks, and differentiated stamp duties on foreign and domestic buyers are measures designed to affect both capital flows and financial stability. The construction of the authors’ FCIs would be enhanced by inclusion of measures of countries’ macro-prudential regulations.Having constructed their FCIs, the authors move on to testing whether using their indexes provide better forecasts than models lacking them. The tests of the forecasting ability of the FCIs use a simple autoregressive structure as the alternative macro model. The usefulness of the new FCIs could be enhanced if they were embedded in more developed macro models.The earlier comments on a possible linkage between innovation and financial excess/distress imply that an FCI with static weights may not be a summary statistic, which is a crucial assumption used in constructing the index. The authors do address this issue indirectly through looking at the contribution of different components of the FCIs to the aggregate movement of the FCI. These subcomponents, though, could be tested for their predictive capacity.There is a curious omission in the scope of countries covered in the paper: China is not included. Given a methodology that allows for unbalanced panels, it is not clear why this is so. Certainly there are doubts about the accuracy of Chinese macroeconomic data, but much of its financial sector data is both more readily available and more reliable than production data (in part because it has traditionally been less politically sensitive). The inclusion of an FCI for China seems particularly important for Asia going forward in the aftermath of the global asset market contagion arising from China's shift in macro policy in late 2015 and 2106 and the rising trade linkages between China and the rest of the region.China's omission is especially problematic for the discussion of the linkages between U.S. and Asian monetary policy, which the authors note remain strong. Measures of capital market integration have generally strengthened and, with them, likely influences from U.S. financial markets on Asian FCIs. Again, the results would be buttressed if the macro factors included some measure of external balance and if measures of market integration/macro prudential settings were used.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.