Financial shocks and inflation dynamics

Abstract

We assess the effects of financial shocks on inflation, and to what extent financial shocks can account for the “missing disinflation” during the Great Recession. We apply a Bayesian vector autoregressive model to US data and identify financial shocks through a combination of narrative and short-run sign restrictions. Our main finding is that contractionary financial shocks temporarily increase inflation. This result withstands a large battery of robustness checks. Negative financial shocks help therefore to explain why inflation did not drop more sharply in the aftermath of the financial crisis. Our analysis suggests that higher borrowing costs after negative financial shocks can account for the modest decrease in inflation after the financial crisis. A policy implication is that financial shocks act as supply-type shocks, moving output and inflation in opposite directions, thereby worsening the trade-off for a central bank with a dual mandate.

1 Introduction

Despite a massive fall in demand during the Global Financial Crisis (GFC), the US economy experienced only a modest disinflation. In this paper, we provide evidence that inflation declined by less than many would have expected because the fall in demand was accompanied by a contractionary financial shock, which created inflationary pressures.¹

Our finding that a contractionary financial shock results in temporarily higher inflation provides useful empirical evidence to an ongoing theoretical debate about the response of inflation to financial shocks. Macroeconomic models featuring financial frictions are in fact compatible with financial shocks acting both as demand and cost-push shocks. This depends on whether aggregate demand or aggregate supply effects dominate in the transmission mechanism. Papers that model financial shocks as demand shocks include Curdia and Woodford (2010), Gertler and Karadi (2011), and Del Negro et al. (2015). In these models, higher borrowing costs following contractionary financial shocks lead to a fall in consumption and investment. Both imply a decline in labor demand, which in turn reduces marginal costs and inflation. By contrast, papers that model financial shocks as cost-push shocks include Gilchrist et al. (2017), De Fiore and Tristani (2013), Christiano et al. (2014) and Meh and Moran (2010). In the first three papers, financial shocks influence the pricing decision of firms, either because firms prefer to hedge against the risk of relying on costly external finance, or because borrowing costs are actually part of firms’ marginal costs. An increase in the cost of external finance leads therefore to an increase in inflation, either via higher markups or via higher marginal costs. Table 1 summarizes the implications of these and other models for the behavior of inflation after financial shocks. Overall, financial shocks propagate through the economy through various aggregate supply- and demand-type channels, and their impact on inflation depends on which channel dominates. ² Hence, whether financial shocks raise or lower aggregate inflation is ultimately an empirical question, which we try to answer in this paper.

Table 1. Theoretical responses of inflation to contractionary financial shocks implied by different models: An increase (decrease) is denoted with a + (-) sign. ft refers to De Fiore and Tristani (2013); mm refers to Meh and Moran (2010); gnss refers to Gerali et al. (2010); gssz refers to Gilchrist et al. (2017); gk refers to Gertler and Karadi (2011); cw refers to Curdia and Woodford (2010); hh refers to Hristov and Huelsewig (2017); gln refers to Gelain et al. (2017); cmr refers to Christiano et al. (2010)

We apply a vector autoregressive model (VAR) estimated with Bayesian methods to a set of US macroeconomic and financial data. We identify financial shocks by imposing short-run sign restrictions on impulse response functions, as well as narrative restrictions reflecting common knowledge about the size and relevance of financial shocks during the GFC. Contractionary financial shocks in our model increase funding costs and decrease credit growth and stock prices, thereby matching the characterization of financial shocks in standard macroeconomic models. Most importantly, our identification strategy is fully agnostic about the response of inflation after financial shocks (and therefore about the contribution of financial shocks for inflation dynamics), while still disentangling financial from other structural shocks such as aggregate supply and demand shocks.

Our first key result is that contractionary financial shocks that decrease output and credit growth tend to increase inflation. The effect arises on impact and is significant over about a year. Based on a historical decomposition, we find that contractionary financial shocks contributed positively to inflation during and after the GFC. Absent financial shocks, the fall in inflation between 2008 and 2009 would have been on average 0.6 percentage points larger. Hence, negative financial shocks compensated to some extent deflationary pressures from other developments. ³ Toward the end of the sample, expansionary financial shocks prevented a more pronounced pick-up in inflation. ⁴

We then explore the transmission channels that can account for the inflation response after financial shocks. Contractionary financial shocks raise borrowing costs by increasing credit spreads. This can in turn increase overall marginal costs, which may partly explain the positive inflation response. Our result is therefore consistent with models where aggregate supply effects dominate in the transmission mechanism of financial shocks such as De Fiore and Tristani (2013) and Gilchrist et al. (2017).

Our main finding is robust against a battery of checks. We show that financial shocks affect banking variables and survey measures of credit supply in the expected way, which supports our identification scheme. Moreover, our key result does not change if we exclude the GFC and post-GFC (zero lower bound) period from the estimation sample. Our key finding is therefore not driven by a special role of the GFC, nor by the limited ability of monetary policy to reduce the policy rate in response to contractionary financial shocks. In a counterfactual experiment, we further show that one could have predicted less of a decline in inflation in the aftermath of the GFC based on pre-GFC parameter estimates and correctly observed financial shocks during the GFC. We also show that the identified financial shock is not contaminated by other shocks including shocks to aggregate supply, demand, technology news, monetary policy, uncertainty, oil, and housing. Finally, we experiment with alternative measures of interest rates, inflation and credit, but our key finding remains unaffected.

Our approach offers two contributions relative to the existing literature. First, we propose an identification scheme for financial shocks that leaves the inflation response unrestricted. Previous work based on sign restrictions, which focuses on the effects of financial shocks on real economic activity, imposes either a positive comovement between output and inflation, or a positive comovement between output and the policy rate after financial shocks, which also biases the inflation response (Busch et al. (2010), Hristov et al. (2012), Conti et al. (2015), De Santis and Darracq-Paries (2015), Furlanetto et al. (2017), Gambetti and Musso (2017)). Other papers restrict the impact effect on inflation and output to be zero, but leave the sign of their responses beyond impact unrestricted (Peersman (2011), Gilchrist and ZakrajŠek (2012)). In these papers, contractionary financial shocks decrease inflation, though in Gilchrist and ZakrajŠek (2012), the response is not significant. ⁵ Also, only few papers have applied narrative restrictions so far, and we combine them with sign restrictions.

Our second contribution is to explore various transmission channels of financial shocks, and their implications for inflation in an aggregate time series setup. Previous work by Antoun de Almeida (2015), Balleer et al. (2015), and Gilchrist et al. (2017) relies on data at the product or firm level. These studies focus on the price-setting behavior of financially constrained firms relative to unconstrained ones, and show that borrowing constraints are an important determinant for price-setting behavior. While these studies are well suited to cleanly identify the effect of borrowing constraints on the price-setting behavior of firms, they lack clear-cut implications for the behavior of aggregate inflation after financial shocks. ⁶ Our study fills this gap by showing that contractionary financial shocks might increase aggregate inflation.

The results from our analysis have two policy implications. First, financial shocks which lower output and increase inflation and, hence, move these two variables in different directions, may worsen the trade-off faced by a central bank which seeks to stabilize both output and inflation. Second, a monetary policy designed to stimulate credit supply may have unintended disinflationary effects if it lowers borrowing costs and through that marginal costs. This would be undesirable in an environment where inflation is already low.

The rest of the paper is structured as follows. In Section 2, we provide details on data, methodology, and the financial shock identification strategy. We present results from the baseline model and investigate the transmission channels of financial shocks in Section 3. In Section 4, we discuss several robustness checks and explore the reaction of banking variables and survey measures to financial shocks. Section 5 concludes.

2 Data and modeling approach

2.1 Data

Our baseline analysis departs from an n-dimensional vector $X_{t}$ of seasonally adjusted quarterly series: real GDP, core inflation, a policy interest rate, credit growth, the EBP, and stock prices. These are standard variables in empirical macro-financial studies.

We use the core CPI, excluding energy and food, to measure inflation. We choose core, instead of headline inflation, in order to disentangle financial shocks from commodity price shocks, such as oil shocks. We further investigate this issue in Section 4 by including the oil price in our baseline VAR model. ⁷ The excess bond premium (EBP), which is taken from Simon Gilchrist’s website, is a risk premium that reflects systematic deviations in the pricing of US corporate bonds relative to the issuers’ expected default risk. It thus constitutes a proxy for the effective risk-bearing capacity of the financial sector, and is a direct price measure of credit supply. We measure credit growth using total credit to the private nonfinancial sector, taken from the Financial Accounts of the USA. In the robustness analysis, we also consider alternative credit measures. Nominal stock prices are taken from Robert Shiller’s website. We deflate the credit and stock price series by the CPI. Finally, we use the federal funds rate as the main policy interest rate. From 2008Q4 onwards, we replace the federal funds rate with the shadow short rate (SSR) from Krippner (2015), which allows to account for unconventional monetary policy. The SSR reflects in fact changes in the term structure of interest rates, which is affected by quantitative easing and forward guidance policies (Gertler and Karadi (2015), Krippner (2015)).

GDP and stock prices enter the model in logarithms, the policy rate and the EBP in levels, the CPI in year-on-year differences of logarithms. ⁸ We filter outstanding credit using log year-on-year differences. Financial intermediaries’ balance sheet variables are subject to secular trends due to changes in the structure of the financial system and regulatory changes. Following Adrian et al. (2016), we deal with this issue by using detrended credit. ⁹ While the year-on-year change is a convenient way of detrending credit, we show below that our results are not affected when using a one-sided HP filter applied to outstanding credit. ¹⁰ If not stated otherwise, we take the series from the FRED database of the Federal Reserve Bank of St. Louis.

The sample period ranges from 1988Q1 to 2019Q2. By choosing 1988Q1 as a starting point, we exclude any monetary regime prior to the Greenspan one. Moreover, the period since the mid-1980s until the beginning of the GFC is associated with the Great Moderation. Hence, our sample period excludes the Great Inflation period. We show below that, even though our key finding is not affected when we extent the sample period backwards to 1973, magnitudes are somewhat different, which indicates parameter instability over a longer sample. Moreover, we check to what extent parameters have changed with the GFC in Section 4.

2.2 Vector autoregression

We assume that the $n \times 1$ vector of endogenous variables $\mathbf{y}_t$ follows a VAR model of order p:

(1) \begin{equation}\mathbf{y}_t^{\prime} = \mathbf{x}_t^{\prime}\mathbf{B} + \mathbf{w}_t^{\prime},\quad\mathbb{E}(\mathbf{w}_t^{\prime})=0,\,\mathbb{E}(\mathbf{w}_t\mathbf{w}_t^{\prime})=\mathbf{\Sigma},\end{equation}

where the regressors’ matrix is defined as $\mathbf{x}_t^{\prime} = [\mathbf{y}_{t-1}^{\prime}, \dots, \mathbf{y}_{t-p}^{\prime}, 1]$ and $\mathbf{B}$ is a $(np+1) \times n$ matrix of reduced-form coefficients. The $n\times 1$ vector $\mathbf{w}_t$ denotes reduced-form innovations which are assumed to be Gaussian with mean zero and positive definite covariance matrix $\mathbf{\Sigma}$ . The lag length p is set to 2, as suggested by the Akaike and Hannan–Quinn information criteria. We estimate the VAR using Bayesian methods. We specify diffuse/uninformative priors so that the information in the likelihood is dominant. These priors lead to a normal-(inverted-) Wishart posterior with mean and variance parameters corresponding to OLS estimates.

2.3 Identifying financial shocks

Identifying financial shocks in the data is not trivial. Different structural macroeconomic models with a financial sector have different implications for key features of financial shocks, including their effect on important variables such as inflation (cf. Table 1). One reason is the different transmission channels embedded in the model. Consequently, there is no consensus in the literature on how to best identify financial shocks.

To identify financial shocks, we propose a strategy that combines short-run sign restrictions on impulse response functions, and narrative restrictions on the shock size and the historical decomposition. These identifying restrictions are summarized in Table 2. Our identifying assumptions are economically reasonable and consistent with a large body of theoretical and empirical work on the dynamic propagation of financial shocks. At the same time, we remain fully agnostic about the effects of financial shocks on inflation dynamics. We discuss sign and narrative restrictions in turn.

Table 2. Restrictions to identify financial, aggregate demand and aggregate supply shocks: This table shows the restrictions imposed on the impulse response functions of the endogenous variables in the baseline VAR to identify financial, aggregate demand and aggregate supply shocks. All restrictions are imposed on impact and are implemented as $\geq 0$ ( $\uparrow$ ) or $\leq 0$ ( $\downarrow$ ). See text for details

Sign restrictions

Throughout the paper, we focus on contractionary financial shocks which lead to a decrease in GDP. Furthermore, we restrict the EBP to increase and credit growth and stock prices to fall. ¹¹ These three restrictions are standard, and match key features of financial shocks in structural macroeconomic models.

We disentangle financial shocks from aggregate supply and aggregate demand shocks by assuming that contractionary financial shocks decrease credit growth by more than GDP. This relative restriction on credit growth and GDP is intuitively plausible. After contractionary aggregate supply and demand shocks, investors might increase risk premia and reduce credit in response to the economic downturn. However, it is reasonable to assume that investors adjust their lending and investment strategies only gradually in response to the shock. Hence, we should first observe a deterioration in economic activity, while credit growth should respond initially only mildly. Financial shocks, by contrast, originate in the financial sector (as seen in the GFC) and exogenously decrease credit supply. Lower credit supply, however, should transmit to the overall economy initially only weakly: entrepreneurs and households need some to time to fully adjust their production processes and demand decisions in response to the lower supply of credit. Hence, we should observe a fall in credit growth that exceeds the fall in output growth for some time. Note that this is similar to the intuition underlying the use of a zero contemporaneous restriction on output. However, our relative restriction is more agnostic than such a recursiveness assumption, which other empirical studies employ.

The relative restriction on credit growth and GDP is consistent with DSGE models featuring an active financial sector and shocks that directly affect banks’ balance sheets. Examples of these models include Covas and Driscoll (2013), Curdia and Woodford (2010), Gelain et al. (2017), Gerali et al. (2010), Gertler and Karadi (2011), Hristov and Huelsewig (2017), Iacoviello (2015), Meh and Moran (2010), and Villa (2016). ¹² Key to this credit-to-GDP response is a realistic banking sector that is subject to endogenous bank balance-sheet constraints due to financial frictions. By contrast, standard financial accelerator models along the lines of Bernanke et al. (1999) do not necessarily imply that credit relative to GDP decreases after a contractionary financial shock (see, e.g. Christiano et al. (2010), De Fiore and Tristani (2013)). ¹³ The reason is that, in these models, firms effectively borrow directly from households while financial intermediaries are simply a veil. Given that we are interested in shocks and dynamics similar to those observed during the GFC—when the financial sector has been at the center stage—we choose to focus on restrictions consistent with models featuring a more complex financial sector.

Within the set of empirical studies, Eickmeier and Ng (2015) impose the same restriction on the credit-to-GDP ratio as we do. Furthermore, our credit-to-GDP restriction is also consistent with empirical findings by Fornari and Stracca (2012) and De Santis and Darracq-Paries (2015), who have credit and GDP included in their model and identify financial shocks with sign restrictions, but leave the credit-to-GDP response unrestricted.

While we need the credit-to-GDP restriction to ex ante disentangle financial shocks from other shocks (especially when we re-estimate the model over the pre-GFC sample), this restriction is not crucial for our key findings. When the restriction is dropped, credit relative to GDP still declines after contractionary financial shocks, and our key results remains unchanged. ¹⁴

Finally, we disentangle financial shocks from monetary policy shocks by using a conditional sign restriction. ¹⁵ Specifically, we impose the restrictions described above, and then check the inflation response for each model which yields financial shocks satisfying the restrictions. If inflation falls on impact, the response of a central bank following a Taylor rule is unambiguously negative. In this case, we restrict the policy rate to decrease on impact. If inflation increases, we do not restrict the policy rate. In this way, we disentangle financial from monetary policy shocks. In standard New Keynesian and structural VAR models, monetary policy shocks move the interest rate in one direction, and the price level and output in the other direction (e.g. Peersman (2005)). Given that we use the SSR, which translates (at least parts of) unconventional monetary policy measures into short-term interest rate movements, we apply the identification scheme to the full sample estimate of the model without distinguishing between non-ZLB and ZLB periods. In Section 4, we check whether this is valid by re-estimating the model over the pre-GFC period, by more explicitly accounting for monetary policy shocks, and by discussing the relationship between financial and monetary policy shocks. ¹⁶

We note that similar restrictions have been used in previous empirical work, which focuses on the real effect of financial shocks. However, this literature either restricts inflation to be positive or not to react on impact, or it restricts the policy rate to increase after expansionary financial shocks, which might also bias the inflation response. ¹⁷ The novelty of our identification scheme is that it disentangles financial shocks from other structural shocks, while being fully agnostic on the response of inflation and of the policy rate.

Paustian (2007) shows that one can sharpen the shock identification by identifying additional shocks. Hence, in addition to financial shocks, we also explicitly identify two key standard macroeconomic shocks which are typically found to be important drivers of the economy: aggregate supply and demand shocks. Contractionary aggregate demand shocks lead to a decline in real GDP, credit, inflation and the policy rate, and raise the credit-to-GDP ratio. This last restriction disentangles financial and aggregate demand shocks, and is for instance consistent with the effects of a preference shock in Iacoviello (2015), Hristov and Huelsewig (2017) and Gelain et al. (2017), or with the effects of a fiscal shock in Hristov and Huelsewig (2017). Contractionary aggregate supply shocks lead to a decline in GDP and stock prices, raise the credit-to-GDP ratio, and lead to a higher inflation.

Finally, we restrict the remaining $n-3=3$ shocks not to have the same characteristics as the identified shocks. Hence, the identified financial, aggregate demand and aggregate supply shocks are the only shocks that satisfy the restrictions. We impose all sign restrictions only on impact, and hence remain relatively agnostic.

Narrative restrictions

Beside sign restrictions, we employ two narrative restrictions in our identification scheme. These are based on the assumption, now widely accepted, that financial shocks were extremely important during the GFC. ¹⁸ The first narrative restriction is imposed on the financial shock series: we impose that the largest contractionary financial shock over our sample period occurs in 2008Q3. The second narrative restriction is imposed on the historical decomposition: the absolute value of the contribution of the financial shock to the dynamics of the EBP in 2008Q3 is larger than the sum of the absolute value of the contributions of all other structural shocks. These restrictions deserve some discussion.

Our approach closely follows Ludvigson et al. (2020) and Ludvigson et al. (forthcoming) who impose restrictions on shocks, as well as AntolÍn-DÍaz and Rubio-RamÍrez (2018) who restrain the historical decomposition. We impose one restriction on the shock and one on the historical decomposition, and hence exploit knowledge about realizations of financial shocks over time, and in comparison with realizations of other shocks in 2008Q3.

We choose 2008Q3 because it includes the month (September) when Lehman Brothers filed for bankruptcy, which is commonly associated as the major trigger of the Great Recession (see also Ludvigson et al. (forthcoming)). While there had been financial turmoil before that date, with the burst of the subprime mortgage bubble in 2007, it is widely agreed that the largest shock hit the US economy (and probably also the rest of the world) in 2008Q3, with the collapse of Lehman Brothers in September 2008. The government had previously bailed out other major financial institutions (Bear Sterns, Fannie Mae, and Freddie Mac), and it came as a surprise that it decided not to rescue Lehman Brothers as well. This triggered financial market panic: stock prices tanked, and investors fled money market mutual funds. The shock spread through 2008Q4 and subsequent quarters. By 2008Q4, policy had already reacted: the US Congress passed a bailout bill in October 2008 and central banks tried to restore liquidity, for example, by directly issuing short-term loans for businesses, lowering interest rates to basically zero, and coordinating actions of central banks throughout the globe. Our timing choice is in line with Ludvigson et al. (forthcoming) who focus on uncertainty shocks and find evidence of a large positive financial uncertainty shock in September 2008. It is also not inconsistent with Ludvigson et al. (2020) who, in one of their applications, restrict the EBP credit spread shock to be particularly large either in September 2008 or in October 2008 or in both. In our view 2008Q4 was mostly characterized by the reaction to the Lehman shock, rather than by another financial shock, which is why we focus on 2008Q3 here. We have also checked whether any other shock (demand or supply shocks as well as the three other rotated orthogonalized residuals) has its extremum in 2008Q3 and found this not to be the case. Hence, financial shocks are the only shocks that have their extremum in 2008Q3.

Beside imposing that a particularly large contractionary financial shock occurred in 2008Q3, we also impose that the contribution of this shock to the EBP is very large. Given that the EBP is constructed as systematic deviations in the pricing of US corporate bonds relative to the issuers’ expected default risk, it should be relatively exogenous with respect to the rest of the economy, and it is plausible to assume an “overwhelming” contribution of financial shocks (as AntolÍn-DÍaz and Rubio-RamÍrez (2018) put it) to the EBP in 2008Q3. To what extent the EBP is really largely exogenous to the rest of the economy is somewhat controversial in the literature. On the one hand, Furlanetto et al. (2017) find that credit shocks identified with sign restrictions explain 44% of the EBP’s forecast error variance at the 1-quarter horizon, and only 16% at the 20-quarter horizon. Gertler and Karadi (2015) find, moreover, a significant reaction of the EBP to monetary policy shocks. On the other hand, Gilchrist and ZakrajŠek (2012) recursively identify financial shocks and find that they explain a very large share of the EBP (median estimate: over 80%). Moreover, Caldara et al. (2016) impose that financial shocks maximize the response of the EBP over a predefined horizon. We are more agnostic than Caldara et al. (2016) and impose that financial shocks explain a large fraction of the EBP only in 2008Q3. We will show that there is room left for other shocks to explain EBP dynamics during the Great Recession.

In Section 4, we explore the robustness of our key results against alternative identification schemes. Importantly, while our baseline model and identification scheme allow us to disentangle financial from standard (macro and conventional monetary policy) shocks, we modify the model to explicitly separate financial shocks from other shocks such as technology news shocks or oil shocks. We also illustrate the effects of imposing the narrative restrictions in addition to the sign restrictions.

To implement the restrictions, we follow the algorithm in AntolÍn-DÍaz and Rubio-RamÍrez (2018), described in Appendix B. We obtain 1000 draws from the posterior of the reduced-form coefficients that satisfy the baseline restrictions. We approximate their weights in the importance step by using 1000 draws.

3 Results

3.1 Results from the baseline model

Impulse response analysis

In Figure 1, we present impulse responses to a contractionary financial shock. The solid red lines are median impulse response. The shaded areas are 68% credible sets. ¹⁹ As imposed by the restrictions, the EBP increases and credit growth as well as the stock price decrease. The negative effects on credit growth, GDP, and the stock price are very persistent, with the peak effects occurring after 1–2 years following the shock. The interest rate falls, in line with the fall in GDP.

Figure 1. Impulse responses to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (solid lines) and 68% credible sets (shaded areas). The responses of GDP and the stock price are in percent; those of the other variables are in percentage points.

Most importantly, inflation increases on impact by 0.1 percentage points and remains positive for around 1 year after the shock. We re-emphasize here that we are agnostic about the response of inflation after the financial shock, leaving its sign unrestricted. Hence, our result suggests that aggregate supply effects dominate demand effects in the transmission of financial shocks.²⁰

Variance and historical decomposition

Table 3 shows the forecast error variance decomposition for selected variables at the 4-year horizon. The financial shock accounts for more than half of the forecast error variance of credit growth, and for 45% of that of the EBP. Moreover, financial shocks explain 44% of GDP fluctuations, and 15% of inflation fluctuations. ²¹ Hence, on average over the sample period, financial shocks made notable, although not very large contributions to inflation dynamics.

Table 3. Forecast error variance decomposition: This table shows the median forecast error variance shares explained by financial shocks, for selected variables in the baseline VAR, at a 4-year horizon

Figure 2 shows the historical decomposition of the EBP, credit growth, inflation, and GDP over the period 1999–2019. Expansionary financial shocks contributed to the pre-crisis credit and output boom by holding the EBP down over most of this period and pushing credit growth and GDP up. Contractionary financial shocks over the GFC accounted for large parts of the drop in credit growth and, as imposed, the rise in the EBP. Consistent with Furlanetto et al. (2017) and Mumtaz et al. (2018), the financial shock does not, however, fully explain the increase in the EBP, suggesting that the EBP is also affected by other shocks. Most importantly, contractionary financial shocks made positive contributions to inflation around and after the GFC. Between 2008 and 2009, contractionary financial shocks increased core inflation by around 0.6 percentage points. Put differently, core CPI inflation fell from 2.5% to 1.5% in 2008–2009, which is the period typically associated with the “missing disinflation puzzle”, ²² and our results suggest that the fall in inflation would have been 0.6 percentage points larger in the absence of financial shocks. Financial shocks also explain a substantial fraction of the GDP decline after the 2007–2009 recession. Our results for the GFC corroborate the results in Del Negro et al. (2015). They show that a DSGE model needs to be augmented with financial frictions and a credit spread to successfully predict the moderate decline in inflation and the strong decline in output during the GFC.

Figure 2. Historical decomposition of key variables: Plot shows the contribution of all shocks to explaining the deviation of key variables from their deterministic component (lines) and the contribution of the financial shocks (bars). Contributions to GDP are in percent; those to the other variables are in percentage points.

We also assess the contribution of our other two identified, aggregate demand and aggregate supply, shocks, in order to understand what has brought inflation down (even though not by too much). We find that the initial decline of inflation, until 2009, is almost entirely explained by negative demand shocks. From 2010 onwards, negative demand shocks and positive supply shocks account for about a third of the decline in inflation each. The remaining third is explained by the unidentified shocks.

Lastly, we note that contractionary financial shocks also accounted for the financial headwinds in the late 1980s/early 1990s, and kept inflation somewhat up over much of that period as well (not shown). Moreover, expansionary financial shocks held down inflation since late 2016, and can therefore partly explain the “missing inflation” at the end of the sample.

Transmission channels of financial shocks relevant for inflation

Why does inflation increase after contractionary financial shocks? In Figure 3, we present impulse responses to the financial shock of variables that capture key transmission channels. We include these variables one by one in the baseline VAR. We treat the endogenous variables from our baseline model as block exogenous for the added variable, and restrict the residual covariance matrix so that shocks to the additional variables do not affect the endogenous variables nor the financial shock estimates.

Figure 3. Transmission channels of financial shocks relevant for inflation: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of investment, consumption, employment, and the real wage are in percent; those of the remaining variables are in percentage points. See text for a description of the variables.

The first row of Figure 3 shows that contractionary financial shocks lead to a strong and hump-shaped fall in real private investment, consumption and employment. The fall in consumption is potentially the result of wealth effects following the decline in asset prices after the financial shock. Investment decreases by more than consumption, consistent with the restriction imposed by Furlanetto et al. (2017) to identify financial shocks. The gradual decrease in demand is likely to be the reason behind the return of inflation to baseline after its initial increase.

The key determinant of firms’ pricing decisions are current and expected future marginal costs. We therefore explore their behavior by looking at the response of the labor income share, a proxy for marginal costs used for instance in Nekarda and Ramey (2013). Figure 2 shows that the labor share does not move significantly in the first few quarters after the shock, and decreases afterwards as both real wages and employment decrease. Hence, the movement in the labor share cannot explain the early increase in inflation. However, it potentially contributes to the decline in inflation 1 year after the shock.

Models where firms have to borrow in advance to finance production, that is featuring a cost, credit, or working capital channel such as De Fiore and Tristani (2013) and Christiano et al. (2014), imply that financing costs are part of firms’ marginal costs and therefore matter for price-setting decisions. ²³ Accordingly, we assess the responses of different borrowing spreads after the financial shock, capturing different segments of financial markets. The spreads are defined as the Baa corporate bond yield over the 10-year government bond yield; the 3-month commercial paper rate over the 3-month T-bill rate; the C&I loan rate over the 2-year government bond yield; the 30-year mortgage rate over the 10-year government bond yield. ²⁴ All spreads increase significantly over the first few quarters, and the shapes of the responses roughly match the shape of the inflation response.

To summarize, results suggest that a mechanism related to the credit or working capital channel might in part be able to explain the increase in inflation after contractionary financial shocks. These shocks raise borrowing costs on a broad scale, causing an overall increase in firms’ marginal costs, which may contribute to the increase in inflation. Nevertheless, other supply-type mechanisms, such as the one emphasized in Gilchrist et al. (2017), may have played a role as well.

4 Validation and robustness

4.1 Responses of credit supply and banking variables to the financial shock

To better understand the characteristics of our financial shock, and to validate that we are indeed identifying a financial shock, we explore the behavior of credit supply measures and banking variables. The series are again added one by one to the VAR, treating the endogenous variables from our baseline model as block exogenous for the added variable. We present the results in Figure 4.

Figure 4. Response of credit supply and banking variables to a one-standard deviation contractionary financial shock: Plot shows median impulse responses (lines) and 68% credible sets (shaded areas). Responses of the willingness to lend to consumers, lending standards, the bank return on assets, the nonperforming loans ratio and the bank capital ratio in percentage points. See text for a description of the variables.

First, we add survey measures of credit supply in order to verify whether our identified shock is consistent with the answers of banking sector survey participants. We use data from the Senior Loan Officer Opinion Survey on Bank Lending Practices: the net percentage of domestic respondents tightening standards for C&I loans (“tightening standards”) and the net percentage of domestic respondents reporting increased willingness to make consumer installment loans (“willingness to lend to consumers”). ²⁵ The willingness to lend to consumers decreases on impact after the contractionary financial shock, and remains negative for 1 year. Similarly, banks tighten their credit standards markedly on impact, and keep them above baseline for more than 1 year. Hence, both survey measures move in the expected direction.

Second, we include data on the return on assets of commercial banks, the ratio of nonperforming loans to total loans, the ratio of bank capital to total assets and the volatility of bank stock prices. Bank return on assets, a measure of bank profitability, decreases significantly and remains negative for about 2 years. This decrease is reflected in the response of the bank capital ratio, which also drops significantly on impact and remains negative for almost a year. The lower profitability translates into lower net worth, for example, bank capital, thus generating a weaker capital position. The ratio of nonperforming loans to total loans also rises significantly. The response is hump shaped, reaching its maximum after more than a year. The behavior of the nonperforming loans ratio follows closely the movement of GDP. The fall in economic activity after the financial shock worsens the balance sheets of entrepreneurs and households, which in turn delays scheduled loan payments (higher nonperforming loans). In addition, the volatility of bank stock prices increases significantly after the financial shock.

Overall, the response of these credit supply and banking variables are consistent with a financial shock.

4.2 Is the Great Recession period different?

One potentially critical point is that the Great Recession period might be different from more “normal” times; for example, because financial frictions are larger in crisis times or because the policy rate was constrained at its zero lower bound. To address this point, we re-estimate the model over the pre-GFC period, that is over the period 1988Q1–2007Q2. Figure 5 (left panel) shows that the positive inflation response after a contractionary financial shock is slightly weaker in the pre-GFC subsample, but the credible sets overlap suggesting that differences are probably not significant. ²⁶

Figure 5. Does the GFC make a difference? Left graph: Impulse response functions of inflation to contractionary financial shocks, median baseline estimate (red solid line), and 68% credible sets (shaded areas), and pre-GFC estimate (blue dotted lines). Right graph: Historical decomposition of inflation, baseline model (all shocks: black solid line, financial shocks: red bars), and counterfactual contributions of financial shocks (based on structural shocks estimated over the full sample and reduced-form parameters estimated over the pre-GFC sample: blue dotted line). See text for details.

We also conduct a counterfactual experiment as follows. ²⁷ We combine the structural shocks from the full model and the reduced-form parameters from the pre-GFC model in order to assess whether one could have predicted positive contributions of financial shocks (assuming the shocks had been observed) to inflation dynamics after the GFC. The answer is yes, even though the counterfactual contributions of financial shocks are slightly smaller compared to those resulting from the baseline model (blue dotted line in Figure 5, right panel). In particular, using the parameter estimates from the pre-GFC subsample, we find that the fall in inflation between 2008 and 2009 would have been 0.4 percentage points larger absent the negative financial shocks.

Overall, we can conclude that our main finding that contractionary financial shocks increase inflation also holds for less turbulent times. It is interesting to note that this key finding cannot be explained by the zero lower bound and monetary policy not being able to respond to negative financial shocks by lowering the interest rate. Also, based on pre-crisis parameters and assuming that financial shocks had been observed, one could have predicted that inflation would not fall as much as implied by a simple Phillips curve relationship. This corroborates the extended Phillips curve estimation results presented in the Appendix.

4.3 Understanding the role of the narrative restrictions

We assess next the importance of narrative restrictions by removing them from the set of identifying restrictions. Figure 6 compares the baseline impulse responses with those obtained from a specification without the two narrative restrictions (blue lines). When narrative restrictions are dropped, our key result remains the same: inflation increases temporarily after contractionary financial shocks. However, the responses of the EBP, GDP and credit growth to financial shocks are slightly weaker. Moreover, inference for EBP and inflation dynamics after the financial shock worsens. Figure 7 shows the corresponding historical decomposition. When the narrative restrictions are dropped, the contribution of financial shocks to the EBP, credit growth and GDP during the GFC is much smaller. In addition, the contribution to inflation is also smaller, both during and after the GFC.

Figure 6. Impulse responses to a one-standard deviation contractionary financial shock—role of the narrative restrictions: Plot shows median impulse responses and 68% credible sets in the baseline model (solid red lines and shaded areas), and in the model without narrative restrictions (solid and dotted blue lines). The response of GDP is in percent; those of the other variables are in percentage points.

Figure 7. Historical decomposition of key variables—effects of the narrative restrictions: Plot shows contribution of all shocks to explaining the deviation of key variables from their deterministic component in the baseline model (solid black lines), contribution of financial shocks in the baseline (red bars) and in the model without narrative restrictions (blue dotted lines). Contributions to GDP are in percent; those to the other variables are in percentage points.

Figures C2–C4 in the Appendix further show the effects of removing the narrative restrictions on either the shocks or the historical decomposition. The figures reveal that the restriction on the shock size improves inference for inflation, while the restriction on the historical decomposition leads to more plausible contributions of the financial shock during the GFC. ²⁸

4.4 (Unconventional) monetary policy shocks

In Section 2.3, we have argued that our identification scheme disentangles financial shocks from monetary policy shocks typically identified in the literature. Recall that monetary policy shocks move interest rates in one direction, and prices and output in opposite directions. By contrast, our financial shocks either move output, inflation and the policy rate in the same direction or move output and inflation in opposite directions.

However, the aggregate supply channels that may influence the transmission of financial shocks could also play a role in the transmission of monetary policy shocks. The cost channel of monetary policy is one specific example of a transmission channel that could cause inflation to fall after expansionary monetary policy shocks. The cost channel of monetary policy has been in fact brought forward as one explanation of the “price puzzle”, for instance, by Castelnuovo (2012) and Gaiotti and Secchi (2006a). If the cost channel is a relevant transmission mechanism, our financial shocks may not be disentangled from monetary policy shocks. Put differently, a monetary policy shock that affects funding costs is a specific type of financial shocks.

This concern should be even more relevant for unconventional monetary policy, which is at least in part designed to explicitly stimulate credit supply and lower funding costs and, through that, to stimulate economic activity and inflation. ²⁹ Existing work already emphasizes the link between unconventional monetary policy and credit supply or financial shocks. De Santis and Darracq-Paries (2015) explicitly identify unconventional monetary policy shocks in the euro area in a VAR model as shocks that increase credit supply. Moreover, in a time-varying parameter VAR for the USA, Prieto et al. (2016a) find that credit spread shocks have contributed positively to GDP growth from 2010 onwards. They argue that those credit spread shocks probably capture unconventional monetary policy actions.

We investigate here to what extent monetary policy actions lie behind our financial shocks. Figure 7 plots our financial shock estimates (solid line) against the monetary “policy news shock” measure taken from Nakamura and Steinsson (2018) (dashed line) in the last two decades of the sample. ³⁰ This “policy news shock” is based on high-frequency responses of current and expected future interest rates in a 30-minute window surrounding scheduled Federal Reserve announcements, and captures the effects of forward guidance. ³¹ We normalize the shock series in Figure 8 so that negative values represent a monetary policy easing (which can be expected to raise credit growth and lower funding costs) and expansionary financial conditions and that the standard deviation is one over the period over which we plot the shocks.

Figure 8. Financial and monetary policy shocks: Identified financial shocks (solid line) and Nakamura and Steinsson (2018) monetary policy shocks (dashed line). The shocks are standardized so that negative values represent expansionary monetary policy and financial shocks. Both series have standard deviation of 1 over the considered sample. See text for details.

Figure 8 reveals that the two shocks are basically uncorrelated over the sample (correlation coefficient: $-0.03$ ). Financial shocks seem, however, somewhat correlated with monetary policy shocks around the 2001 recession and the GFC. In both episodes, the Federal Reserve strongly lowered the federal funds rate and provided liquidity to financial institutions in order to stabilize the financial system. ³² In addition, in the course of the GFC the Federal Reserve lent directly to borrowers and investors in credit markets, and purchased longer-term securities. ³³ This finding is consistent with Eickmeier et al. (2016) who find a price puzzle after an expansionary monetary policy shock in high-volatility periods. Similarly, Fry-McKibbin and Zheng (2016) find that prices decline after expansionary monetary policy shocks in financial stress periods, but not in low stress periods. They argue that this is consistent with cost channel effects during financial stress periods.

We conduct two further checks. First, we include the Nakamura and Steinsson (2018) monetary policy shock as an explanatory variable in the VAR, and rerun the estimation and shock identification. ³⁴ The idea is that the monetary policy shock should capture all remaining uncontrolled effects of monetary policy shocks. Second, we identify a monetary policy shock in addition to the baseline shocks, by imposing that the policy rate increases and inflation, output and credit growth decrease on impact after a contractionary monetary policy shock. Figure 13 in the Appendix shows that, even after explicitly accounting for monetary policy shocks, the contractionary financial shock continues to have inflationary effects.

To sum up, results suggest that our financial shock is barely contaminated by monetary policy shocks, and that contractionary financial shocks cleaned from monetary policy shocks still produce positive effects on inflation. We find, if anything, some correlation between financial and monetary policy shocks in financial stress periods. One implication is that a monetary policy measure targeting credit supply and borrowing costs may act as an expansionary financial shock and thus exert downward pressure on inflation through the mechanisms discussed above. This would not be desirable in an environment of already low inflation. However, our results do not imply that the overall effects of expansionary monetary policy on inflation are negative. Although expansionary monetary policy might put a downward pressure on inflation through credit supply effects, demand-side effects leading to an increase inflation could predominate. Analyzing those would go beyond the scope of this paper.

4.5 Further robustness checks: other shocks and alternative measures of interest rates, inflation, and credit

We carry out a large battery of further robustness checks. We test whether we disentangle financial from other shocks including housing shocks, technology news shocks, oil shocks, and uncertainty shocks. This is achieved by adding additional variables one by one to the baseline model, and imposing additional restrictions to identify one additional shock at a time, as discussed in Appendix C. We note that, for computational reasons, it is not feasible to identify all shocks simultaneously. Moreover, we replace our measures of interest rates, inflation and credit with alternative measures and add inflation expectations to the model. Finally, we re-estimate the model over a longer sample period starting in 1973 and a monthly version of our model. None of these alterations changes our results, and we refer to Appendix C for details.

5 Conclusion

While there exists a large literature on the relationship between financial shocks and the real economy, the literature on the link between financial shocks and inflation dynamics is still in its infancy. However, understanding the link between financial and price stability is of key importance to monetary policy makers.

In this paper, we use a Bayesian VAR analysis and propose a novel identification scheme for financial shocks based on sign and narrative restrictions, which remains fully agnostic about the effects of financial shocks on inflation. Our main finding is that contractionary financial shocks tend to have temporary inflationary effects. We also show that this inflationary effect prevented a much stronger fall in inflation during the financial crisis. Hence, our results suggest that financial shocks might be an additional explanation for the “missing disinflation puzzle” during the financial crisis, as well as for the “missing inflation puzzle” over the subsequent recovery phase. A key implication of our finding is that financial shocks which simultaneously lower output and rise inflation might worsen the policy trade-off for a central bank which seeks to stabilize both.

We also explore different transmission channels of financial shocks and their implications for inflation dynamics. Our findings suggest that higher borrowing costs could explain the observed short-term inflationary effects of financial shocks.

Our key result that contractionary financial shocks have inflationary effects is robust against a battery of robustness checks, such as the use of alternative or additional variables, alternative shock identification schemes and variations of the sample period. Moreover, the identified financial shocks affect banking variables and survey measures of credit supply in the expected way. Finally, although our identified financial shock series is basically uncorrelated with the Nakamura and Steinsson (2018) monetary policy shock measure over the entire sample period, we detect some mild correlation between them in times of financial stress. One implication is that a monetary policy that stimulates credit supply and lowers funding costs may act as an expansionary financial shock with potentially disinflationary effects, which could be undesirable in a low inflation environment.

We note that we focus here on a broad financial (credit supply) shock. One promising future avenue could be to assess the effects of specific types of financial shocks, such as those emphasized in Table 1.