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Start: policy makers’ views on macro -finance 1. Framing: why study financial cycles? ◦ Financial cycles as part of macro-financial linkages “Theories” on why and how financial cycles arise 2. How to Identify Financial (and Business) Cycles Different approaches for measuring ◦ Level vs. trend cycles: technical and policy differences ◦ 3. How to increase use of financial cycles in policy ◦ Link with forecasting and macro-financial linkages ◦ Financial stability analyses, vulnerabilities, and cycles Link with policies (macroprudential, e.g., CCyB) ◦ Macro-financial linkages tools at Federal Reserve Board
“I wasn’t used to thinking of the banking and financial sector as having such a critical role…Floating exchange rates, tightening budgets, liberalizing markets and worrying about wages were all according to the book…Tesobonos were not” Stanley Fischer, First Deputy MD, IMF, Euromoney, September 1997 “In the interest of full disclosure: This is a first pass by an economist who, until recently, thought of financial intermediation as an issue of relatively little importance for economic fluctuations…” Olivier Blanchard, Chief Economist, IMF, WP/09/80, April 2009 3
Before e the cris isis is ◦ Long shadow of two separate strands in literature Efficient financial markets. And RBC, new-Keynesian ◦ Limited evidence on supply-side channels “Finance is a veil” The cr Th cris isis is highlighted many questions-old, new ◦ Financial markets less than efficient. Finance matters in originating and propagating shocks Importance of macro-financial linkages After r the cris isis is more evid idenc ence ◦ Empirical research on macro-financial linkages Much on demand-side; some on supply-side channels Still l man any y (more) e) ques estions, tions, no unifie fied d frame amework work 4
Part of question: “Why Macro - Financial Linkages”? • Theories suggest financial cycles can arise, related to, but also independent of business cycle ◦ Demand: financial accelerator mechanism ◦ Supply: also accelerator, and intra-financial system Mechanisms matter for measurement and policy!! • Empirical approach for measuring will (thus) vary ◦ Demand side Overall/Sectoral, to identify accelerator effects ◦ Supply side Study (intra-) financial system, to get miss- pricing/allocation
Endogenous developments in financial markets propagat gate and amplify fy shocks ks in real economy Corporate finance foundation Financial positions of agents (asset prices, debt, ◦ wealth) affect access to finance → real outcomes Variations built into D(S)GE models 1. External finance premium (Bernanke-Gertler, 1989) 2. Collateral prices (Kiyotaki and Moore, 1997) 3. Debt deflation (Irving Fisher, 1933), more recently 4. Open economy models (Krugman, 1999, etc.)
Various other frictions (cash-flow, working capital, trade finance, default probabilities, etc.) Frictions related to heterogeneity, project and technological choices, productivity, governance Financial frictions operating via labor markets ◦ Demand: firms’ financing constraints affect hiring ◦ Supply: housing/mortgages Information, uncertainty ◦ More volatility, less precision in signals/credit standards ZLB/ELB can aggravate effects/externalities 7
Net worth, asset prices decl cline ine and collateral less valuable → final borrowers (corporations, households, sovereigns) have less access to finance, worsens real economy, adverse erse feedba backs cks – “Busts:” Asset prices drops, credit declines, external financing crunches with adverse real consequences Net worth, asset prices incr crease ase and collateral more valuable → final borrowers have more access to finance, strengthens real economy, positiv itive e feedbac backs ks ◦ “Booms:” Asset price booms, credit increases, capital inflows, lead to amplified positive real consequences
Financial system (supply side) can be a source of shocks, amplification and propagation. Several channels: Bank nk lend endin ing g and d bank nk capital pital chan hannels els ◦ Some borrowers (households, SMEs) are bank dependent. Traditional channels of transmission of monetary, regulatory policies. New work on unconventional policies, QE, etc. Leverage erage and d liquidi quidity ty chan annels ls ◦ Like capital channel, but not just for banks. Plus indirect, asset price effects through (common) exposures. And (complex) interactions among financial intermediaries and markets, through traded securities, etc. Newer area of research. All show how financial sector create, amplify shocks Cros oss s secti tiona nal, l, intercon erconnec ectio ions, s, SIFIs, FIs, TBTF TF ◦ Many externalities, market failures within financial system leading to system risk. Interacts with time-series dimension 9
Financial Accelerator again: shocks to assets or equity affect bank → amplify, propagate shocks 1. Capital shocks 2. Asset price shocks 3. Liquidity shocks, fire sales 4. Leverage cycles Interact with “standard” FA → double whammy → General equilibrium → virtuous+vicious cycles ◦ Intra-system dynamics: not (ye yet) well understood erstood 10
increase in value of increase in Final securities equity balance sheet equity equity equity assets assets assets debt debt debt Initial After positive balance asset price new new sheet shock purchase of borrowing securities 11
Demand mand + Su + Supply pply Sid ides es Combined ombined Mean ans: s: Many linkages, no unified framework Financia ncial exposur ures es (st stock cks and flows) ) betwe ween en se secto tors rs Household Corporate sector sector HH ROW OFI NFC MFI GOVT INS Financial Banking system markets Other financial intermediaries External Public sector sector
Two main (complementary) approaches in the business and financial cycle literatures 1. “Classic cycle” approach (turning -point analysis) Identify significant turning points (peaks and troughs) 2. “Growth cycle” approach Detrend: define cycle relative to a trend ⇒ Business Cycles (BC) and Financial Cycles (FC) 13
NBER-based algorithm to determine peaks, troughs Based on Burns, Mitchell (1946) and Bry, Boschan (1971) for GDP, typically Quarterly frequency (BBQ) ◦ Popularized for business cycles by Harding and Pagan ◦ Can include Markov Switching (MS) models here (Hamilton, 89) Simple measures, as input for further analyses: ◦ Average duration/typical pattern of cycle: relevant for policy ◦ Explain duration/amplitude/slope by various macro-financial variables and (monetary and fiscal) policy ◦ Analyze boosts and busts (“financial crisis”) as extremes FCs ◦ Analyze degree of BC/FC synchronization within, across countries Concordance index (Harding, Pagan 2002b): fraction of time any two series are in the same phase in their respective cycles Amplification across FCs and across countries 14
Hodrick-Prescott (HP) filter, with frequency range ◦ Idea: measure “excessive credit” (new debt contributing less to growth (lower productivity, for assets, e.g., housing) ◦ Typical: one-sided HP filter with λ =400,000) for long FC E.g., Basel committee For credit-to-GDP:, as in CCyB Can augment end points w. forecasts before de-trending ◦ Performs well as early warning indicator of future crises Latent Dynamic Factor, Frequency Domain Analysis, Spectrum, Multivariate (MVF), etc. ◦ Latent factors, relate to many variables, including financial Use state-space, spectrum representation, Kalman filters, etc. Detect commonalities, obtain unique cycle, w/o assumptions ◦ MVF variations much used (IMF, BIS, Norges bank, etc.) Can robustly add variables (i.e., output gap est. Borio et al (2013)), improve forecasting (Liu, Matheson, Romeu, 1998) ◦ Frequency Domain Analysis, Spectrum: more recent 15
Claessens et al. (2011, 2012): analyze FC and BC in 21 (+23) AM (+EM) countries; 1960-2007. Use turning point analysis ◦ FC (credit, house, equity prices): long, severe, highly synchronized within countries ◦ Strong linkages between BC and FC, accentuate each other. Recessions with financial disruptions longer and deeper ◦ Now in Claessens et al. 2016 for 75 countries 1960-2015 Drehmann et al. (2012), Borio (2012): for 7 AMs. Use pre-specified frequency-based filter method at which FC is assumed to operate ◦ FC best captured by house prices and credit, not by equity ◦ Peaks strongly associated with systemic financial crises ◦ Duration and amplitude of FC increased since 1985 (from 11 to 20 years) 16
“Fundamentals - based” models ◦ Dynamic-panel estimates with country-specific intercepts. e.g., Debt=f(GDP, real rate, country-specifics, lagged debt) ◦ “Long - run equilibrium” models. e.g., house price = f(income, interest rates, credit, equity prices, demographics), + affordability, risk-taking, standards Intra-financial sector models with time/cycle ◦ Regime-shifting models; Joint Probability of Default (JPoD); Systemic contingent claims analysis (SCCA); Distance-to- Default; Expected Default Probabilities (EDF); SRISK; DIP; CoVaR: Diebold-Yilmaz, etc. etc. Some market price, some accounting-based. Most use interdependency, “ jointness ” of distress probability 17
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