Decoding the Chinese financial cycle and its effects on the global economy and markets

 

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Authors

Carolina Martinez

Abigail Watt

Jeremy Lawson

Rong Fu

 

Unconventional measures
require a broader
measurement lens

Chapter 1

Since the global financial crisis (GFC) there has been a proliferation of studies considering the measurement and importance of changes in financial conditions2. However, studies of Chinese financial conditions have been much more limited, despite the central role they now play in global economic and financial developments.

Indeed, it is arguably even more important to have a robust measure of financial conditions in China. Unlike most advanced economies, where monetary policy decisions are relatively transparent and conducted through a small number of conventional and unconventional instruments, Chinese policy and the financial system within which it operates is more complex and opaque. It is also subject to rapid cyclical and structural changes. This makes it more challenging both to assess the stance of policy and to analyse its transmission channels.

‘There are significant benefits from constructing a quantitative measure of Chinese financial conditions that captures the nuances of the stance, direction and transmission of Chinese monetary policy.’

Figure 1 illustrates the ways in which Chinese monetary policy, as conducted by the People’s Bank of China (PBoC), does not conform to developed country norms. Firstly, the policy instruments used by the PBoC differ from those of developed markets (DM) central banks. Despite some liberalisation of money market rates over recent years, and the recently announced reform to switch the main reference rate for bank lending rates to the Loan Prime Rate (LPR), many of the lending rates that influence the real economy are still directly controlled by the PBoC. And even the LPR will continue to be partly dictated by the medium-term lending facility rate set by the PBoC.

Figure 1: China’s monetary policy framework

Figure 1: China’s monetary policy framework Source: Huang et al (2018)

In addition, the PBoC makes more use of quantity-based instruments than most other central banks. Price instruments are becoming a more important part of the toolkit. And in 2018, the State Council did not set an explicit target for M2 growth for the first time since monetary base targeting began in 1994. But reserve requirement ratios are still a key operational instrument while the money base and broader monetary aggregates are importation operational and intermediate targets. This partially reflects the still highly aggregate nature of the banking and broader financial system.

Second, the mandate of the PBoC is far-reaching with maintaining the stability of the currency, the balance of payments, and the financial system and prices. On top of this, the promotion of growth and employment are among the policy objectives of the authorities. The PBoC is also constrained in its independence, both in terms of decision-making power and operationally. Whilst some policy changes such as open market operations and medium-term lending facilities can be made independently, other changes need to be approved by the State Council. These include changes to reserve requirements and the annual setting of monetary aggregates

Third, the self-described stance of policy itself can also be rather opaque, with the PBOC making use of five different classifications of the stance of policy: moderately loose, moderately tight, tight, prudent, and prudent and neutral. Currently, Chinese policy is described as being ‘prudent and neutral’ in light of the balancing act between tighter credit regulations to ensure financial stability and the need to support the slowing economy. More generally, the PBoC's communications about the stance of policy, including its state contingency, lack transparency. This makes it harder to anticipate policy changes.

The upshot is that there are likely to be significant benefits to constructing a quantitative measure of Chinese financial conditions that captures the nuances of the stance, direction and transmission of Chinese monetary policy.


2

See Hatzius et al (2010) and Kliesen et al (2012) for a discussion of existing literature for developed markets, Wacker et al (2014) for some discussion of indices for emerging markets.

 
‘There are significant benefits from constructing a quantitative measure of Chinese financial conditions that captures the nuances of the stance, direction and transmission of Chinese monetary policy.’
 

Measuring Chinese
financial conditions

Chapter 2

Given the complexities of Chinese monetary policy itself and its transmission to the economy and financial markets, quantifying financial conditions in a single measure is no easy task. Not only do we need to consider both the quantity-based and price-based instruments used by the PBoC at different times, we also need to incorporate a wide array of indicators affecting the broader stance of financial conditions.

In addition, the choice of which particular series to include for the construction of our index was dictated by a number of aspects including:

‘By combining the results of our index with our judgement and knowledge of the Chinese economy, we have gained a more comprehensive understanding of how financial conditions have evolved since 2007.’
  • How relevant has the measure been to Chinese financial conditions historically;
  • Whether the indicator is released in a timely manner or not;
  • How important we expect it to be in shaping future financial conditions in the future; and
  • Whether the series has a long enough history to pin down the relationships overtime.

With this in mind we incorporated the information from 21 series3 into our index going back to 2007. These series can be comprised into five broad categories, representing the different dimensions of financial conditions:

Policy & Duration:

This captures the interest rates that summarise the official stance of monetary policy, the base financing rate in the market and developments in the Chinese government bond market. Whilst the Chinese bond market is currently largely domestic, foreign participation is increasing. Thus, it is likely to become a more important transmission channel for policy and influence on the real economy over time.

Money & Credit:

In China’s heavily regulated financial system, monetary and credit aggregates have historically been key target and transmission channels for policy. We cast our net widely to make sure that all aspects of lending activity are captured, including shadow banking and information from survey data.

Risk premia:

The prices of risk assets are a core feature of most developed economy financial conditions indices. Although China’s credit and equity markets are still deepening, we want to allow for some effects while future-proofing the index as the financial system continues to develop.

Volatility:

The volatility of an asset price is a measure of both its absolute and perceived risk. It can also affect financial conditions by amplifying liquidity effects through the financial cycle.

Foreign exchange (FX):

The exchange rate has been a target of policy for the PBoC for much of its history, either in terms of a level or a path, or against the US dollar or a basket of currencies. Since 2015 it has also become more heavily influenced by market forces, though it is still managed by the authorities to prevent rapid destabilising changes. We use the nominal effective exchange rate in our index because it is closer to the basket currently being targeted.

Before combining this information into a single series we impose sign restrictions on to the data so that higher values of the index represent looser financial conditions and vice versa. For example, a cut in the Reserve Requirement Ratio (RRR) is a loosening in policy, and thus should feed into looser overall financial conditions and a higher positive value for the index.

We combine the dataset information into a single index using a data dimensionality reduction technique called principal component analysis (PCA). The idea of using such a technique is to capture as much of the common variation in the dataset as possible into a smaller number of series. Therefore the index we construct accounts for the highest proportion of the variation in the total dataset.

Using this index allows for the characterisation of past cycles in Chinese financial conditions in terms of looser or tighter relative to historical average (see Chart 1). Additionally, PCA provides information on the extent to which these deviations are driven by the five different dimensions of financial conditions we have identified.

Chart 1: Chinese Financial Conditions Index (CFCI)

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

By combining the results of our index with our qualitative judgement and knowledge of the Chinese economy, we have developed a more comprehensive understanding of how Chinese financial conditions have evolved over the past 12 years. In particular, we identify eight main phases of the cycle over this period:

  1. Pre-GFC policy tightening lasting from January 2007 to May 2008;
  2. The 4-trillion stimulus lasting from June 2008 to July 2009;
  3. Post-crisis policy normalisation lasting from August 2009 to December 2011;
  4. Total social financing boom lasting from January 2012 to February 2013;
  5. Liquidity crunch lasting from March 2013 to September 2014;
  6. The second rise of shadow banking lasting from October 2014 to March 2016;
  7. Financial deleveraging lasting from April 2016 to December 2017; and
  8. Cautious stimulus lasting from January 2018 to July 2019

Below, we outline the drivers and contours of some of these episodes in more depth.

The ‘4 trillion’ stimulus

In the wake of the stark tightening in financial conditions in the lead-up to and amidst the global financial crisis (GFC), the Chinese authorities enacted the largest stimulus package in China’s history. Despite the announced stimulus being 4trn Yuan in size, the reality was th at this included previously planned spending. Estimates from the International Monetary Fund (IMF) therefore put the total size of new investment at 2trn Yuan, or around 3.1% of GDP in 2009 and 2.7% in 2010.

Chart 2: Credit impulse booms but deficit contained

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*measured as the 12m change in the Chinese credit impulse which is the 12m rolling sum of Chinese credit over the 4q rolling sum of nominal GDP.
Source: Haver and Bloomberg (as of August 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

The funding was split between central and local governments, with around 30% pledged centrally, and then through a combination of loosening regulatory constraints and increased local government borrowing. This made credit expansion a key aspect of the stimulatory measures. This was reflected in the sharp rise of the Chinese credit impulse and a relatively minor change in the government's fiscal position (see Chart 2). However, given the off-balance sheet nature of the funding of the stimulus the overall deficit was probably nearer 9% of GDP by the end of 2009 (Absolute Strategy Research, 2019).

During the period of stimulus our Chinese Financial Conditions index (CFCI) increased by around 6.5 standard deviations, the largest cumulative easing during our sample and taking the aggregate index to a sample peak in March 2009. Around 37% of the loosening in conditions was attributable to Money & Credit factors alone (see Chart 3). The contribution of Policy & Duration was even larger, accounting for 60% of the total loosening in financial conditions. The PBoC shifted its policy stance to moderately loose and lowered the benchmark deposit rate and loan rates five times, as well as cutting the RRR four times between 2008 and 2009.

Chart 3: Decomposing the drivers of financial conditions in China

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

The total social financing boom and then the liquidity crunch

With domestic economic growth slowing more sharply than expected through 2011 in the face of tighter domestic financial conditions and a deteriorating external environment, policymakers reversed the policy stance through 2012. However, in contrast to the earlier episode, this easing cycle was largely driven by Money & Credit factors, amplified by domestic lenders and borrowers arbitraging around regulations through the rapid growth of shadow banking products like entrusted loans.

Then, as the authorities once again tried to clamp down on the excesses, a series of liquidity shocks pushed the CFCI back into tight territory (see Chart 4) through 2013 and into 2014. These shocks made it difficult for banks to source funds, with interbank lending rates rising rapidly. This was exemplified by the Shibor 3m-rate spiking to around 6% in June 2013, almost double its long-term average. Instead of injecting more funds into the system the PBoC chose to not intervene, in part because shadow banking flows initially remained healthy and the credit-to-GDP ratio continued to rise. This amounted to the authorities' first attempt to improve the quality of lending. Most of the tightening in financial conditions recorded during this period was therefore driven by the Policy & Duration variables.

Chart 4: Liquidity shocks amidst the entrusted loan boom

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Source: Haver and Bloomberg (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

The second rise of shadow banking

Shadow banking can be defined as the actions of institutions outside the formal banking sector taking on similar functions to that of regulated financial intermediaries. In China at least they faced higher operational risk than regulated banks. However, this was without the backing of the PBoC's lender of last resort capabilities or access to deposit insurance. Moreover, regulated banks themselves used the sector as a means of avoiding regulations.

The main components of the Chinese shadow banking sector are entrusted loans, trust products and wealth management products4. Ehlers et al. (2018) at the Bank for International Settlements (BIS) estimated that as the Chinese authorities re-stimulated the economy in the wake of the global commodity price shock and another domestic growth slowdown, the size of the shadow banking sector reached 71% of GDP and 46% of total bank deposits by the end of 2016 (see Chart 5).

Chart 5: Size and composition of China's shadow banking sector

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Source: Ehlers et al (as of 2018)

Although shadow banking first took off in 2012, it re-emerged in 2015. PBoC caps on total lending and loan-to-deposit ratios, reserve requirements at the PBoC, sectoral lending constraints, and low capital and liquidity requirements on shadow banks themselves all amplified the shadow banking upswing.

Simultaneously, previously-dormant household credit growth also picked up strongly. During this period our financial conditions index loosened aggressively, driven mostly by Money & Credit factors, with the cumulative stimulus falling only moderately short of the 2008/09 policy loosening.

Financial deleveraging and cautious stimulus

With fiscal policy becoming much more accommodative from the middle of 2015, the Chinese authorities once again became concerned about overheating and excesses in the financial system, commencing a new wave of policy tightening from the middle of 2016. Although credit growth across all components began to decelerate, shadow banking bore the brunt of the policy shift (see Chart 6).

Chart 6: Shadow banking begins to shrink as proportion of total social financing

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*summed total of entrusted loans, trust loans and bankers' acceptance bills
Source: Aberdeen Standard Investments and Haver (as of July 2019)

This renewed focus on financial stability particularly affected those sectors most reliant on shadow banking. Many private enterprises engaged in infrastructure projects found it hard to obtain funding and this began to pass through into aggregate economic activity. By the middle of 2018 the extent of this slowdown began to be felt in not just investment activity but also consumption growth, with underlying growth slowing towards its lowest rates of the post-crisis expansion.

Amidst this slowdown, which is still ongoing, the policy cycle has loosened once again. However, in contrast to both the 2012 and 2015 loosening cycles, the authorities have continued to hold back the shadow banking sector. With credit demand also subdued because of excess leverage in the system, and credit not flowing to the private sector companies that need it, our aggregate CFCI has loosened much less than the previous three easing cycles while the Money & Credit component has contributed only 9 percentage points (ppts) of the aggregate easing (see Chart 7).

Chart 7: Composition of loosening in 2015 versus 2018/19

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.


3 4

See Table 1 in the appendix for a detailed description of the data series used.

See Table 2 in appendix for a brief description of the various metrics.

 
‘By combining the results of our index with our judgement and knowledge of the Chinese economy, we have gained a more comprehensive understanding of how financial conditions have evolved since 2007.’
 

Modelling the spillover effects
of changes in Chinese
financial conditions

Chapter 3

Putting Chinese financial conditions in the global economic and market context.

As recently as 2014, foreign private sector investors who wanted to access the A-share market had few options. They had to get a Qualified Foreign Institutional Investor (QFII) or a Renminbi Qualified Foreign Institutional Investor (RQFII) licence.

'We find compelling evidence that shocks to Chinese financial conditions have had significant impacts on economic and market variables.’

Since the financial crisis, the composition and swings in the Chinese financial cycle appear to correspond closely with the mini-cycles in domestic and foreign activity, as well as asset prices. However, to investigate these effects more rigorously we need to build a model that captures their complex and changing interrelationships. We do this within what is called a Bayesian Vector Auto-regression (BVAR) framework.

BVARs have a number of attractive features. They allow each variable in the system to be explained by itself and by past values of all the other variables in the system. Assumptions about the contemporaneous relationships between the variables allow the user to identify exogenous 'shocks'. Each variable than can in turn be used to investigate how the other variables respond to unanticipated events (called impulse responses). The Bayesian approach also allows for the inclusion of a larger number of variables to be included in the model than in standard VAR.

The latter feature is especially important to us, because we want to examine how shocks to Chinese financial conditions affect a large number of domestic and external economic and financial variables. We also need to capture the economic and financial factors that influence Chinese financial conditions themselves. Table 2 sets out all the variables we included in our model while the accompanying working paper explains the technical details of our approach.

Table 2: Description of the variables used in our global model

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Source: Aberdeen Standard Investments (AS OF August 2019)

Measuring the effects of Chinese policy loosening

To assess the spillover effects of changes in Chinese financial conditions we use as reference the change in our ASI CFCI between December 2017 and March 2019 of roughly units (see figure 2D). We then use our model of the global economy to better understand the implications of a shock of this magnitude. We measure the effect of the shock to Chinese financial conditions over a 60-month period.

Assessing the transmission mechanism: From financial conditions to economic growth

We find compelling evidence that shocks to Chinese financial conditions have, on average, had significant impacts on both domestic and external macroeconomic activity variables since 2007 (see figure 2A). Looser financial conditions lead to a significant increase in the Chinese composite purchasing managers' index (PMI), with the peak response of 1.3 units occurring after around eight months. EM and DM industrial activity also respond positively to looser Chinese financial conditions and though the impact is smaller than for domestic activity, the effects are felt more quickly.

Figure 2: Impact of a Shock to Chinese Financial Conditions

(2A) Response of Activity Variables

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(2B) Response of Equity Markets and Commodity Prices

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(2C) Response of US FSI and EMBIG Spread

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(2D) Shock to Chinese Financial Conditions

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These charts show the impact of the 3 unit shock to Chinese financial conditions on the variables within our system. The unanticipated shock occurs in period 1 and we trace the response over 60 months.
Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

Intriguingly, we find evidence that looser Chinese financial conditions have a larger effect on our US Nowcast, which measures the underlying growth rate of the aggregate economy, than on global industrial activity. This highlights that the industrial complex is not the only or even main conduit for cross-border financial shocks. That may be due to the way looser Chinese financial conditions also lower stress levels in US financial markets (see figure 2C). This also underlines the importance of monitoring and evaluating Chinese and US financial developments jointly.

Cross-border asset price effects

While the economic implications of changes in Chinese financial conditions are important, as an asset manager we care mostly about how they influence asset prices. Figures 2B and 2C show the effect of positive financial conditions shocks on DM and EM equities (ex-China), EM sovereign credit spreads and commodity prices. We also modelled the effect on offshore Chinese equities, but while the direction of the effect was positive, it was not statistically different from zero, perhaps because it remains a highly sentiment-driven market, and one also prone to interventions by regulators.

Our results suggest that both EM equities (ex-China) and DM equities respond positively to looser financial conditions. However, the effect on EM equities is both larger and has a more rapid pass-through. Meanwhile, EM sovereign credit spreads narrow in response to looser Chinese financial conditions, though the full effects take longer to be transmitted than for equities.

How important are changes to Chinese financial conditions compared with other factors?

Our impulse responses show the size of the responses to Chinese financial conditions shocks but say nothing about their relative importance compared with the other variables in the system. We can perform this comparison using what is called variance decomposition.

Table 3 implies that the Chinese PMI, DM industrial production, and the emerging markets bond index global (EMBIG) spread are the variables for which shocks to Chinese financial conditions explain the largest proportion of their variance. However, in no case is it the most important explanatory variable; in most cases Chinese economic activity (as proxied by the PMI) and US economic activity (as proxied by the Nowcast) in particular, explain the largest proportions of the other variables. Put differently, one's view on the outlook for US economic growth should be the starting point for one's global economic forecast and investment decisions.

Table 3: Variance Decomposition after 5 years

Table 3: Variance Decomposition after 5 years Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

No two financial cycles are the same

In Chapter 2 we illustrated how the compositional drivers of our CFCI have varied significantly across the different financial cycles since 2007. Given this, and the fact that aggregate leverage in the Chinese economy and financial system has been on a rising trend, it is worth considering whether the spillover effects from China themselves have been changing over time. To answer we make use of what is called a time-varying Bayesian vector autoregressive model (VAR). This is similar to a standard BVAR, except that it allows the estimated model to be dynamic and change throughout time. The companion working paper again contains the technical details.

Our results show that although the sign of the responses of economic and asset prices to changes in Chinese financial conditions has been consistent over time, the magnitude of the responses has changed5. In particular, the magnitude of the effects on Chinese activity, advanced economy industrial activity, Chinese equities, EMBIG spreads and US financial stress have all become more muted.

To examine this more closely we focus on two specific historical episodes where financial conditions were becoming significantly easier: the period leading up to June 2009 in the wake of the GFC; and the period leading up to May 2016 when lending from the shadow banking sector was allowed to increase rapidly (see Table 4). The study of both of these episodes corroborates the earlier evidence; the peak response of Chinese activity, advanced economy industrial activity, Chinese equities, EMBIG spreads and US financial stress were all significantly lower in 2016 than 2009, and no variable displayed a stronger response.

Table 4: Assessing the difference in impact in June 2009 versus May 2016

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

Although this is consistent with the hypothesis that the multiplier effect of policy easing has been declining as the country’s debt burden has increased, our methodology does not account for the possibility that ‘neutral’ financial conditions have also been declining over time. As a result, financial conditions may have been tighter than our index implies in recent years. We leave investigating this question for further work.

Nevertheless, our results are consistent with the Chinese authorities needing to provide more stimulus than in the past if they want to achieve similar economic results. Given how little the Money and Credit factor has contributed to the net loosening of financial conditions over the past 18 months, that component would seem to be one viable target. However, aggressively pushing up credit growth against the backdrop of already excess leverage would also increase financial stability risks. A better approach would be to focus on fiscal policy, and social infrastructure spending in particular.


5

See Figure 3 in the appendix for the time-varying impulse responses.

 
'We find compelling evidence that shocks to Chinese financial conditions have had significant impacts on economic and market variables.’
 

Implications and the practical
application of our research

Chapter 4

Our proprietary China Financial Conditions Index (CFCI) is an important new tool for extracting the signal from a wide array of policy and financial indicators, identifying the major turning points in the financial cycle in real time, and analysing the effects of changes in conditions on key economic and market variables. Using a rigorously calibrated model we are able to show that the Chinese financial cycle has important spillover effects to the rest of the world, though those effects appear to have been diminishing over time. The final chapter of our paper explores how we are using this research in our forecasting and investing processes.

Economic forecasts based on rigorous research and experienced judgment

When policy and financial indicators are changing rapidly and sometimes in contrary ways, a single indicator allows us to efficiently combine the signals, compare the trend with our forecast assumptions, and make adjustments accordingly.’

At any given point in time our forecasts for Chinese and broader global GDP growth embed assumptions about whether and how Chinese monetary policy is most likely to evolve over the forecast horizon. From this, we predict how this will be transmitted to the real economy via credit demand and supply, broader sentiment effects and asset prices. In turn, portfolio managers use this information, together with assessments about valuations and positioning in the market, to make asset allocation decisions.

The development of the CFCI and the BVAR models enhances this process in a number of ways. First, when policy and financial indicators are changing rapidly and by different amounts and sometimes in contrary ways, we are now able to more efficiently combine the signals, compare the realisations with what we were assuming in our forecasts and portfolio decisions, and make adjustments accordingly.

For example, if our forecasts for the next year assumed that the reserve requirement ratio (RRR) would be cut by 200 basis points (bps), total social financing (TSF) growth would pick up by 2 ppts and the currency would remain stable, but the authorities cut the RRR by only 100bps, total social financing growth instead increased by 4 ppts, and the currency appreciated by 3%, our framework translates this into a CFCI index change relative to the previous baseline. Holding everything else equal we can then use the BVAR to analyse how much to adjust our growth and asset price forecasts.

Of course, in practice we are rarely in a world where all else is really equal, so this process can never be deterministic and will involve using other complementary models, as well as our expert judgement, to make the necessary syntheses.

A recent case study

A recent case study helps to illustrate these points more forcefully. In the first half of 2018, amid signs that the domestic and global economies were slowing, the Chinese authorities began to cautiously reverse the policy tightening put in place during 2017. This involved cutting RRRs, allowing other market interest rates to fall, while also cutting taxes for corporations.

However, while the Policy & Duration component of CFCI increased in response to these measures, the Money & Credit component of the index did not respond, and in fact initially deteriorated further. That was both because the authorities remained reluctant to allow financial imbalances to increase again, and because credit demand remained soft in the sectors that were able to access lending.

The upshot is that although there has been a net easing of financial conditions since the start of 2018, it has been modest compared with previous episodes. The level of the index remains lower than at the peak of past episodes, and Money & Credit has participated relatively little in the improvement.

How have we used this information in practice? As it became clear that the Chinese and global economies were slowing, and the trade war between the US and China was heating up, we took the view through the second half of 2018 that the authorities would continue to ease monetary policy settings. However, because we expected the authorities to continue to restrain the credit impulse, our judgment was that a moderate easing in financial conditions was the most likely outcome.

For the purposes of our forecasts, we also assumed that the trade war would remain a headwind to growth and that the efficacy of policy would be weaker than in the past. Thus, with financial conditions easing along the broadly expected path, and other headwinds to take into account, we continued to take the view that Chinese growth would continue to trend down through 2019 and 2020.

Chart 8: Expectations for Chinese growth 2019-2021

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Source: Aberdeen Standard Investments and Bloomberg (as of Q3 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

More recently, however, we have drawn on the index to make changes to our forecasts. When we compiled our forecasts in March, we expected the CFCI to either increase or at least hold at its level through the rest of 2019. However, over the subsequent six months the CFCI has declined, driven by Money & Credit factors. In short, the pass through of easing through the Policy & Duration component has proved to be even weaker than we expected.

As a result, and with the trade war escalating beyond our already pessimistic expectations, we have been downgrading our Chinese and broader global economic forecasts (see Chart 8). Simultaneously, and with the aid of this analysis, our tactical asset allocators have been reducing their overall risk exposure, and to emerging market assets in particular.

Looking forward, although we expect the Chinese authorities to further loosen monetary policy, which should push the CFCI higher, we continue to expect only a modest pass through, with looser policy cushioning rather than lifting growth. However, we will continue to use the CFCI to monitor whether the pass through is either stronger or weaker than we have built into our forecasts and make adjustments accordingly.

Taking a longer-term view, our findings imply that a larger loosening in financial conditions is required to stimulate a given amount of growth in China than in the past. The nature of policy objectives also appears to be changing, with the excess leverage in the system and the desire to avoid a building of financial risks forcing a greater reliance on Policy & Duration measures. This, together with the government's decision to make it easier for foreigners to access the onshore bond market, reinforces our positive view on Chinese government bonds.


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Ehlers, T., Kong, S. & Zhu, F., 2018. Mapping shadow banking in China: structure and dynamics. Bank for International Settlements Working Papers, Volume 701.

Elliot, D. J. & Qiao, Y., 2015. Reforming shadow banking in China. Economic Studies at Brookings.

Elliot, D., Kroeber, A. & Qiao, Y., 2015. Shadow banking in China: A primer. Economic Studies at Brookings.

Hatzius, J. et al., 2010. Financial Conditions Indexes: A Fresh Look After the Financial Crisis. NBER Working Paper Series, Volume 16150.

Huang, Y., Ge, T. & Wang, C., 2018. Monetary policy framework and transmission mechanism. In: G. S. W. X. Marlene Amstad, ed. Handbook of China's Financial Sysyem. s.l.:s.n.

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Kliesen, K. L., Owyang, M. T. & Vermann, K. E., 2012. Disentangling Diverse Measures: A Survey of Financial Stess Indexes. Ferderal Reserves Bank of St. Louis Review, 94(5), pp. 369-97.

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McMahon, M., Schipke, A. & Li, X., 2018. China's monetary policy communication: frameworks, impact and recommendations. IMF Working Paper, Volume 244.

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Appendices

Table 1: Data Table for Chinese Financial Conditions Index

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

Table 2: Instruments of the shadow banking sector

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Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

Figure 3: Time-varying impact of a shock to Chinese financial conditions

Chinese PMI

Chinese PMI

US Nowcast

US Nowcast

Commodity prices

Commodity prices

Advanced IP

Advanced IP

Chinese Offshore Equities

Chinese Offshore Equities

EM ex China Equity

EM ex China Equity

CFCI

CFCI

DM Equity

DM Equity

EM ex China IP

EM ex China IP

EM bond spread

EM bond spread

US FSI

US FSI
Source: Aberdeen Standard Investments, Haver, Bloomberg, Thomson Reuters Datastream (as of September 2019)

For illustrative purposes only. No assumptions regarding future performance should be made.

 
When policy and financial indicators are changing rapidly and sometimes in contrary ways, a single indicator allows us to efficiently combine the signals, compare the trend with our forecast assumptions, and make adjustments accordingly.’

The views and conclusions expressed in this communication are for general interest only and should not be taken as investment advice or as an invitation to purchase or sell any specific security.

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