## _wp1176

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---

### Overview and motivation
- Gyrations in financial markets have greatly influenced real activity around the world over the past two decades, including Japan’s massive asset market crash in the early 1990s, deep financial crises in many emerging economies in Asia in the second half of the 1990s, simultaneous equity market busts and recessions in many advanced economies after the late 1990s equity booms, and the 2008-2009 crisis as the latest severe financial disruption.
- Objective: provide a comprehensive empirical overview of financial cycles using objective dating methods analogous to those used for business cycles.

### Data and scope
- Sample coverage:
  - 21 “advanced” OECD countries.
  - Quarterly data covering 1960:1-2007:4.
  - Sample split into two periods: the pre-globalization period (1960-1985) and the globalization period (1986-2007).
  - House price series start in 1970 for all countries except: Austria (1986:3), Belgium (1985:1), Greece (1993:4), and Portugal (1988:1).
- Variables studied:
  - Credit: aggregate claims on the private sector by deposit money banks.
  - House prices: indices of house or land prices (country-dependent).
  - Equity prices: share price indices weighted with market value of outstanding shares.
- Data sources and treatment:
  - Credit: IFS.
  - House prices: mostly OECD.
  - Equity prices: IFS and DATASTREAM.
  - All series seasonally adjusted when necessary and in constant prices.
- Cycle identification methodology:
  - Classical cycle definition (Burns and Mitchell, 1946) applied to log-levels.
  - Harding and Pagan (2002a) algorithm (BBQ) used to identify turning points.
  - Censoring rules: complete cycle duration ≥ five quarters; each phase ≥ two quarters.
  - Concordance index from Harding and Pagan (2002b) used to measure synchronization.
- Definitions of features:
  - Downturn duration Dc: number of quarters between peak and next trough.
  - Upturn duration Du: number of quarters to reach previous peak after trough.
  - Downturn amplitude Ac = fk – f0 (peak to trough).
  - Upturn amplitude Au = fk+4 – fk (trough to first four quarters of expansion).
  - Slopes computed as amplitude divided by duration.
- Identification of intense episodes:
  - Disruptions (crunches/busts/collapses): bottom quartile of downturn amplitudes.
  - Booms: top quartile of upturn amplitudes.
  - Severe disruptions/strong booms: bottom/top 12.5 percent (worst/best half of disruptions/booms).

### Main features of financial cycles (frequency, duration, amplitude, slope)
- Overall counts and frequency:
  - 473 complete financial cycles identified over 1960:1-2007:4.
  - Downturns: credit 114, house prices 114, equity prices 245.
  - Upturns: credit 115, house prices 114, equity prices 251.
  - Financial cycles more frequent in 1960-1985 than in 1986-2007 (example for credit: downturns 69 vs 45; upturns 67 vs 48).
- Time spent in phases (sample means; medians in brackets):
  - Credit: Time in Downturn 0.30 [0.30]; Time in Upturn 0.20 [0.23].
  - House Price: Time in Downturn 0.41 [0.40]; Time in Upturn 0.31 [0.32].
  - Equity Price: Time in Downturn 0.45 [0.44]; Time in Upturn 0.38 [0.39].
- Duration (means with medians in brackets):
  - Downturn durations (Full Period means):
    - Credit 5.50 [4.00]
    - House Price 8.47 [6.00]
    - Equity Price 6.64 [5.00]
  - Upturn durations (Full Period means):
    - Credit 8.00 [4.00]
    - House Price 14.25 [6.50]
    - Equity Price 21.93 [7.00]
  - Notable change: equity upturns shorter in globalization period (10 quarters vs almost 32 in earlier period as discussed in text).
- Amplitude (sample medians; means in brackets where shown):
  - Typical downturn amplitudes (medians, Full Period):
    - Credit -4.03 [ -6.68 ]
    - House Price -5.99 [ -10.85 ]
    - Equity Price -23.70 [ -27.38 ]
  - Typical upturn amplitudes (medians, Full Period):
    - Credit 4.36 [6.44]
    - House Price 3.62 [5.64]
    - Equity Price 20.09 [24.08]
- Slope (medians, Full Period):
  - Downturn slopes:
    - Credit -0.93 [-1.25]
    - House Price -1.06 [-1.22]
    - Equity Price -4.07 [-4.70]
  - Upturn slopes:
    - Credit 1.23 [2.01]
    - House Price 1.19 [1.54]
    - Equity Price 4.75 [5.99]
- Key qualitative findings:
  - Downturns tend to be sharp and short (about five to eight quarters); upturns are longer and slower.
  - Equity and house price cycles tend to be longer and more pronounced than credit cycles.
  - Equity price cycles became shorter over time; equity cycles are more violent (three to four times faster than credit/housing).

### Synchronization: within and across countries
- Within-country synchronization:
  - Median and mean concordance between credit and house prices: 0.68 (i.e., 68 percent of the time).
  - Concordance involving equity prices generally lower; weakest linkages with other markets.
  - Concordances higher in the globalization period than in pre-globalization period.
- Across-country synchronization (Full Sample statistics):
  - Credit: Mean 0.75; Median 0.75; Max 0.82; Min 0.67.
  - House Price: Mean 0.59; Median 0.59; Max 0.66; Min 0.44.
  - Equity Price: Mean 0.70; Median 0.71; Max 0.77; Min 0.63.
  - Synchronization increased over time, significantly for house and equity prices.
  - Cross-country synchronization highest for credit and equity cycles; housing synchronization about 60 percent on average.

### Intense financial cycles: disruptions and booms (descriptive)
- Counts of intense events (Full sample):
  - Disruptions (bottom quartile of downturns): credit crunches 28; house price busts 28; equity price busts 61.
  - Booms (top quartile of upturns): credit booms 24; house price booms 28; equity price booms 63.
- Disruptions (medians/means comparisons vs other downturns):
  - Credit crunches:
    - Duration mean 10.29 (vs other credit downturns 3.94).
    - Amplitude median -13.26 (about four times larger than other credit downturns).
    - Slope median -1.58 (more violent).
  - House price busts:
    - Duration mean 18.14 (vs other house downturns 5.33).
    - Amplitude median -28.52 (about seven times larger than other house downturns).
    - Slope median -1.75 (more violent).
  - Equity price busts:
    - Duration mean 11.79 (vs other equity downturns 4.93).
    - Amplitude median -50.62 (about twice as large as other equity downturns).
    - Slope median -5.06 (three times more violent than credit/house disruptions).
- Booms (comparisons vs other upturns):
  - Equity price booms: median amplitude 46.36 (vs other upturns 14.96).
  - Credit booms: median amplitude 12.13 (vs other credit upturns 3.54); duration shorter 4.48.
  - House price booms: median amplitude 11.24 (vs other house upturns 2.40); duration 13.25.
- Dynamic patterns around intense episodes (median year-over-year growth patterns):
  - Disruptions usually preceded by rapid increases (e.g., median credit growth 5 to 6 percent just before peak), followed by sharp decelerations (credit falling to -6 percent and taking about 10 quarters to return positive).
  - House price busts decline for more than three years; house booms follow prolonged low growth then sustained recovery after about a year (over 15 percentage points change).
  - Equity price busts exhibit sharp and prolonged declines with no recovery within three years after start of bust.

### Coincidence of cycles and cross-market amplification
- Unconditional probabilities of being in downturn (upturn) (in percent):
  - Credit downturn 26.96; credit upturn 19.22.
  - House price downturn 40.52; house upturn 33.10.
  - Equity price downturn 45.46; equity upturn 40.01.
- Conditional probabilities and amplification:
  - Probability of credit downturn rises to 48 percent (from 26.96) if there is a house price disruption.
  - Probability of house downturn rises to 78 percent if a credit disruption is underway.
- Cross-market amplification (selected comparisons):
  - Credit downturns overlapping with house price busts: longer (mean 7.38 vs 4.86), deeper, and more violent.
  - Credit downturns with equity price busts: not significantly longer, but more severe in amplitude.
  - Credit downturns with financial crises: much longer, deeper, and more violent (differences not always statistically significant).
  - House price downturns overlapping with equity price busts: longer, deeper, and more intense (statistically significant).
  - Equity cycles: fewer systematic changes when coinciding with other disruptions; equity downturns sometimes shorter and shallower when coinciding with house price busts.
- Cross-country synchronization effects:
  - Synchronized downturns defined as >40% of countries in downturn; highly synchronized if >50% (for equity cycles thresholds 80% and 90% used).
  - Synchronized downturns tend to be much more severe even if not always longer (e.g., highly synchronized equity downturns: prices drop about 40 percent vs 18 percent for other downturns).

### Formal econometric analysis: duration and amplitude determinants
- Duration analysis (Weibull duration models with country fixed effects):
  - Positive duration dependence for all cycles: Weibull parameter p estimates:
    - Credit p ≈ 1.71.
    - House Price p ≈ 1.47.
    - Equity Price p ≈ 1.63.
  - Implication: the longer a downturn has gone on, the more likely it is to end.
  - Bivariate regression insights (selected):
    - House price busts lengthen credit downturns and shorten equity downturns.
    - Strong global growth of the respective financial variable in the first year after peak increases probability of exit from downturns.
    - Inflationary pressures before a downturn reduce likelihood of exit (for credit and equity).
    - Greater trade and financial openness generally associated with shorter downturns (notably housing and equity).
    - Financial crises delay exit from credit downturns, but may hasten exit from equity downturns.
    - Longer previous expansions delay exit from house price downturns.
    - Globalization dummy (post-1985) associated with higher probability of exit from equity downturns.
    - Exit from synchronized credit downturns is significantly less likely.
- Amplitude analysis (panel regressions with country fixed effects):
  - Key relationships:
    - Credit contractions associated with house price busts are deeper.
    - Equity contractions associated with house price busts are shallower.
    - House price downturns associated with equity busts are more severe.
    - Favorable global financial conditions mitigate severity (especially for credit and equity).
    - Inflation before a downturn accentuates severity.
    - Trade and financial openness moderate house and equity price downturn amplitudes.
    - Crises associated with deeper credit downturns.
    - Amplitude of previous expansion tends to reduce depth of subsequent downturns (except for equity episodes).
    - Equity downturns less severe in globalization period.
  - Limited evidence on determinants of upturn amplitudes: global conditions may help equity recoveries; booms not universally associated with stronger recoveries.

### Conclusions and policy-relevant observations
- Empirical regularities:
  - Equity and house price cycles are longer and more pronounced than credit cycles.
  - Cross-country synchronization highest for credit and equity cycles and increased over time.
  - Cross-market linkages matter: national credit and house price cycles feed off each other and amplify episodes; globally synchronized downturns in credit and equity are longer and deeper.
  - Duration dependence: downturns become more likely to end the longer they persist.
- Policy implications and directions for future research:
  - Need for models with financial frictions to quantitatively match documented features of financial cycles.
  - Study welfare implications of financial disruptions and linkages to the real economy in general equilibrium frameworks.
  - Further empirical work on determinants of duration and amplitude, including institutional and market characteristics and global conditions.
  - Consideration of macro-prudential design informed by cross-asset and cross-country interactions and potential harmful effects of asset price and credit booms when they burst.

*IMF Working Paper content as supplied in the content unit.*

### Executive Summary ......................................................................................................

### _wp1176 - Executive Summary ......................................................................................................

### Overview and motivation
- Gyrations in financial markets have greatly influenced real activity around the world over the past two decades, including:
  - Japan’s massive asset market crash in the early 1990s following the largest housing bubble in its modern history, marking the start of its “Lost Decade.”
  - Deep financial crises in many emerging economies in Asia in the second half of the 1990s after prolonged credit booms.
  - Simultaneous equity market busts and recessions in many advanced economies after the late 1990s equity booms.
  - The 2008-2009 crisis as the latest severe financial disruption.
- The severity of the 2008-2009 crisis has made the study of financial cycles a central topic of research.
- Objective of the paper: provide a comprehensive empirical overview of financial cycles using objective dating methods analogous to those used for business cycles.

### Data and scope
- Dataset: 21 “advanced” OECD countries for the period 1960:1-2007:4.
- The analysis excludes years following the recent crisis because a number of financial cycle episodes associated with the crisis are still ongoing.

### Main questions answered
- What are the main features of financial cycles?
- How synchronized are financial cycles within and across countries?
- When does the coincidence of financial cycles lead to magnified (intense) outcomes?

### Main findings: Features of financial cycles
- Financial disruptions tend to be long whereas booms are relatively short.
- Equity and house price cycles are typically longer and more pronounced than credit cycles.
- The features of financial cycles have changed over time; in particular, equity price cycles have become shorter.
- Figure note: Disruptions (booms) correspond to the bottom (top) 25 percent of downturns (upturns) in terms of amplitude. Downturns (upturns) refer to episodes other than disruptions (booms).

### Main findings: Synchronization within and across countries
- Within countries: cycles in credit and house prices appear to be the most highly synchronized.
- Across countries: the degree of synchronization is highest for credit and equity cycles, and synchronization has been increasing over time.

### Main findings: Coincidence and intensity of financial cycles
- Strong feedback effects between house price and credit cycles: disruptions in one market intensify the other, plausibly because of collateral constraints and complementarities between credit and housing finance.
- Globally synchronized financial downturns result in longer and deeper episodes, especially for credit and equity cycles.
- Definition note (from Figure A): A credit downturn (or upturn) is accompanied with a housing bust (boom) if it starts at the same time or after the beginning of an ongoing housing episode.

### Empirical methods and additional results
- Uses well-established, reproducible methodology for dating financial downturns and upturns, applied to quarterly data to improve identification over annual studies.
- Employs various regression models to analyze factors associated with duration and amplitude of financial cycles.
- Finds positive duration dependence for the downturn phase of financial cycles: the longer a downturn has gone on, the more likely it is to end.
- Regression results confirm the presence of feedback effects between cycles in different financial markets even after controlling for other potential factors.

### Policy relevance and concluding remarks
- Analysis is relevant to debates on the role of financial markets in the real economy and the design of macro-prudential approaches.
- The paper concludes with a brief discussion of policy implications and directions for future research.

*Source: Executive Summary of the IMF Working Paper (content unit: _wp1176 - Executive Summary) contained in the provided PDF.*

### Section 2 presents our database and methodology. The dataset includes 21 “advanced” OECD

### _wp1176 - Section 2 presents our database and methodology. The dataset includes 21 “advanced” OECD

### Database and methodology
- Sample coverage:
  - 21 advanced OECD countries.
  - Quarterly data covering 1960:1-2007:4.
  - Sample split into two periods: the pre-globalization period (1960-1985) and the globalization period (1986-2007).
  - House price series start in 1970 for all countries except: Austria (1986:3), Belgium (1985:1), Greece (1993:4), and Portugal (1988:1).
- Variables studied (core of financial intermediation):
  - Credit: aggregate claims on the private sector by deposit money banks.
  - House prices: indices of house or land prices (country-dependent).
  - Equity prices: share price indices weighted with market value of outstanding shares.
- Data sources and treatment:
  - Credit: IFS.
  - House prices: mostly OECD.
  - Equity prices: IFS and DATASTREAM.
  - All series seasonally adjusted when necessary and in constant prices.
- Cycle identification methodology:
  - Classical cycle definition (Burns and Mitchell, 1946) applied to log-levels.
  - Harding and Pagan (2002a) algorithm (BBQ) used to identify turning points.
  - Censoring rules: complete cycle duration ≥ five quarters; each phase ≥ two quarters.
  - Turning point conditions defined via local maxima/minima criteria in the text.
  - Concordance index from Harding and Pagan (2002b) used to measure synchronization.
- Definitions of features:
  - Downturn duration Dc: number of quarters between peak and next trough.
  - Upturn duration Du: number of quarters to reach previous peak after trough.
  - Downturn amplitude Ac = fk – f0 (peak to trough).
  - Upturn amplitude Au = fk+4 – fk (trough to first four quarters of expansion).
  - Slopes computed as amplitude divided by duration.
- Identification of intense episodes:
  - Disruptions (crunches/busts/collapses): bottom quartile of downturn amplitudes.
  - Booms: top quartile of upturn amplitudes.
  - Severe disruptions/strong booms: bottom/top 12.5 percent (worst/best half of disruptions/booms).

### Main features of financial cycles (frequency, duration, amplitude, slope)
- Overall counts and frequency:
  - 473 complete financial cycles identified over 1960:1-2007:4.
  - Downturns: credit 114, house prices 114, equity prices 245.
  - Upturns: credit 115, house prices 114, equity prices 251.
  - Financial cycles more frequent in 1960-1985 than in 1986-2007 (example for credit: downturns 69 vs 45; upturns 67 vs 48).
- Time spent in phases (sample means; medians in brackets as in source):
  - Credit: Time in Downturn 0.30 [0.30]; Time in Upturn 0.20 [0.23].
  - House Price: Time in Downturn 0.41 [0.40]; Time in Upturn 0.31 [0.32].
  - Equity Price: Time in Downturn 0.45 [0.44]; Time in Upturn 0.38 [0.39].
- Duration (means with medians in brackets):
  - Downturn durations (Full Period means):
    - Credit 5.50 [4.00]
    - House Price 8.47 [6.00]
    - Equity Price 6.64 [5.00]
  - Upturn durations (Full Period means):
    - Credit 8.00 [4.00]
    - House Price 14.25 [6.50]
    - Equity Price 21.93 [7.00]
  - Notable changes: equity upturns shorter in globalization period (10 quarters vs almost 32 in earlier period per discussion).
- Amplitude (sample medians; means in brackets where shown):
  - Typical downturn amplitudes (medians, Full Period):
    - Credit -4.03 [ -6.68 ]
    - House Price -5.99 [ -10.85 ]
    - Equity Price -23.70 [ -27.38 ]
  - Typical upturn amplitudes (medians, Full Period):
    - Credit 4.36 [6.44]
    - House Price 3.62 [5.64]
    - Equity Price 20.09 [24.08]
- Slope (violence; medians, Full Period):
  - Downturn slopes:
    - Credit -0.93 [-1.25]
    - House Price -1.06 [-1.22]
    - Equity Price -4.07 [-4.70]
  - Upturn slopes:
    - Credit 1.23 [2.01]
    - House Price 1.19 [1.54]
    - Equity Price 4.75 [5.99]
- Key qualitative findings:
  - Downturns tend to be sharp and short (about five to eight quarters); upturns are longer and slower.
  - Equity and house price cycles tend to be longer and more pronounced than credit cycles.
  - Equity price cycles became shorter over time; equity cycles are more violent (three to four times faster than credit/housing).

### Synchronization: within and across countries
- Within-country synchronization (concordance; fraction of time in same phase):
  - Median and mean concordance between credit and house prices: 0.68 (i.e., 68 percent of the time).
  - Concordance involving equity prices generally lower; weakest linkages with other markets.
  - Concordances higher in the globalization period than in pre-globalization period.
- Across-country synchronization (Full Sample statistics):
  - Credit: Mean 0.75; Median 0.75; Max 0.82; Min 0.67.
  - House Price: Mean 0.59; Median 0.59; Max 0.66; Min 0.44.
  - Equity Price: Mean 0.70; Median 0.71; Max 0.77; Min 0.63.
  - Synchronization increased over time, significantly for house and equity prices.
  - Cross-country synchronization highest for credit and equity cycles; housing synchronization about 60 percent on average.

### Intense financial cycles: disruptions and booms (descriptive)
- Counts of intense events (Full sample):
  - Disruptions (bottom quartile of downturns): credit crunches 28; house price busts 28; equity price busts 61.
  - Booms (top quartile of upturns): credit booms 24; house price booms 28; equity price booms 63.
- Disruptions (comparison vs other downturns; medians and means as in source):
  - Credit crunches:
    - Duration mean 10.29 (vs other credit downturns 3.94).
    - Amplitude median -13.26 (about four times larger than other credit downturns).
    - Slope median -1.58 (more violent).
  - House price busts:
    - Duration mean 18.14 (longest; vs other house downturns 5.33).
    - Amplitude median -28.52 (about seven times larger than other house downturns).
    - Slope median -1.75 (more violent).
  - Equity price busts:
    - Duration mean 11.79 (vs other equity downturns 4.93).
    - Amplitude median -50.62 (about twice as large as other equity downturns).
    - Slope median -5.06 (three times more violent than credit/house disruptions).
- Booms (comparison vs other upturns):
  - Equity price booms: median amplitude 46.36 (vs other upturns 14.96).
  - Credit booms: median amplitude 12.13 (vs other credit upturns 3.54); duration shorter (4.48).
  - House price booms: median amplitude 11.24 (vs other house upturns 2.40); duration 13.25.
- Dynamic patterns around intense episodes (median year-over-year growth patterns):
  - Disruptions are usually preceded by rapid increases in the variable (e.g., median credit growth 5 to 6 percent just before peak), followed by sharp decelerations (credit falling to -6 percent and taking about 10 quarters to return positive).
  - House price busts decline for more than three years; house booms follow prolonged low growth then sustained recovery after about a year (over 15 percentage points change).
  - Equity price busts exhibit sharp and prolonged declines with no recovery within three years after start of bust.

### Coincidence of cycles and cross-market amplification
- Conditional probabilities (Table 5; probabilities in percent):
  - Unconditional probabilities of being in downturn (upturn):
    - Credit downturn 26.96; credit upturn 19.22.
    - House price downturn 40.52; house upturn 33.10.
    - Equity price downturn 45.46; equity upturn 40.01.
  - Conditional example: probability of credit downturn rises to 48 percent (from 26.96) if there is a house price disruption; probability of house downturn rises to 78 percent if a credit disruption is underway.
- Cross-market amplification (selected findings):
  - Credit downturns overlapping with house price busts:
    - Longer (mean 7.38 vs 4.86), deeper (amplitude more negative), and more violent.
  - Credit downturns with equity price busts:
    - Not significantly longer, but more severe in amplitude.
  - Credit downturns with financial crises:
    - Much longer, deeper, and more violent (though differences not always statistically significant).
  - House price downturns overlapping with equity price busts:
    - Longer, deeper, and more intense (statistically significant differences).
  - Equity cycles: fewer systematic changes when coinciding with other disruptions; equity downturns sometimes shorter and shallower when coinciding with house price busts.
- Cross-country synchronization effects:
  - Synchronized downturns defined as >40% of countries in downturn; highly synchronized if >50% (for equity cycles thresholds 80% and 90% used).
  - Synchronized downturns tend to be much more severe even if not always longer (e.g., highly synchronized equity downturns: prices drop about 40 percent vs 18 percent for other downturns).

### Formal econometric analysis: duration and amplitude determinants
- Duration analysis (Weibull duration models with country fixed effects; Table 10):
  - Positive duration dependence found for all cycles: Weibull parameter p estimates:
    - Credit p ≈ 1.71.
    - House Price p ≈ 1.47.
    - Equity Price p ≈ 1.63.
    - Implication: the longer a downturn has gone on, the more likely it is to end.
  - Bivariate regression insights (selected):
    - House price busts lengthen credit downturns and shorten equity downturns.
    - Strong global growth of respective financial variable in first year after peak increases probability of exit from downturns.
    - Inflationary pressures before a downturn reduce likelihood of exit (for credit and equity).
    - Greater trade and financial openness generally associated with shorter downturns (notably housing and equity).
    - Financial crises delay exit from credit downturns, but may hasten exit from equity downturns.
    - Longer previous expansions delay exit from house price downturns.
    - Globalization dummy (post-1985) associated with higher probability of exit from equity downturns.
    - Exit from synchronized credit downturns is significantly less likely.
- Amplitude analysis (panel regressions with country fixed effects; Table 11):
  - Key relationships:
    - Credit contractions associated with house price busts are deeper.
    - Equity contractions associated with house price busts are shallower.
    - House price downturns associated with equity busts are more severe.
    - Favorable global financial conditions mitigate severity (especially for credit and equity).
    - Inflation before a downturn accentuates severity.
    - Trade and financial openness moderate house and equity price downturn amplitudes.
    - Crises associated with deeper credit downturns.
    - Amplitude of previous expansion tends to reduce depth of subsequent downturns (except for equity episodes).
    - Equity downturns less severe in globalization period.
  - Limited evidence on determinants of upturn amplitudes: global conditions may help equity recoveries; booms not universally associated with stronger recoveries.

### Conclusions and policy-relevant observations
- Empirical regularities emphasized:
  - Equity and house price cycles are longer and more pronounced than credit cycles.
  - Cross-country synchronization highest for credit and equity cycles and increased over time.
  - Cross-market linkages matter: national credit and house price cycles feed off each other and amplify episodes; globally synchronized downturns in credit and equity are longer and deeper.
  - Duration dependence: downturns become more likely to end the longer they persist.
- Policy implications and directions for future research (as indicated in text):
  - Need for models with financial frictions to quantitatively match documented features of financial cycles.
  - Study welfare implications of financial disruptions and linkages to the real economy in general equilibrium frameworks.
  - Further empirical work on determinants of duration and amplitude, including institutional and market characteristics and global conditions.
  - Consideration of macro-prudential design informed by cross-asset and cross-country interactions and potential harmful effects of asset price and credit booms when they burst.

*Italicized attribution line: IMF Working Paper content as supplied in the content unit.*

### References

### References

### Financial crises, credit cycles, and financial intermediation
- Adrian, Tobias, and Hyun Song Shin, 2010, “The Changing Nature of Financial Intermediation and the Financial Crisis of 2007–2009,” Annual Review of Economics, Vol. 2.
- Bikhchandani, Sushil, and Sunil Sharma, 2000, “Herd Behavior in Financial Markets - A Review,” IMF Working Papers 00/48, International Monetary Fund.
- Bernanke, Ben, 1993, “Credit in the Macroeconomy,” Quarterly Review, Federal Reserve Bank of New York, Vol. Spring, pp. 50-70.
- Bernanke, Ben, Mark Gertler, and Simon Gilchrist, 1996, “The Financial Accelerator and the Flight to Quality,” The Review of Economics and Statistics, Vol. 78, No. 1, pp.1-15.
- Chari, V.V., Lawrence Christiano, and Patrick J. Kehoe, 2008, “Facts and Myths about the Financial Crisis of 2008,” Federal Reserve Bank of Minneapolis, Research Department, Working Paper No. 666.
- Ivashina, Victoria, and David Scharfstein, 2010, “Loan Syndication and Credit Cycles,” American Economic Review: Papers & Proceedings, Vol.100, pp. 57–61.
- Laeven, Luc and Fabian Valencia, 2010, “Resolution of Banking Crises: The Good, the Bad, and the Ugly,” IMF working paper 10/146.
- Mendoza, Enrique, and Marco E. Terrones, 2008, “An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data.” NBER Working Paper No. 14049.
- Mian, Atif, and Amir Sufi, 2010, “The Great Recession: Lessons from Microeconomic Data,” American Economic Review: Papers & Proceedings, Vol.100, pp. 51–6.
- Reinhart, Carmen, and Kenneth Rogoff, 2009, This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press.
- Fisher, Irving, 1933, “The Debt-Deflation Theory of Great Depressions,” Econometrica, Vol.1, October, pp. 337-357.
- Kindelberger, Charles P., and Robert Aliber, 2005, Maniacs, Panics, and Crashes, Wiley Investment Classics.

### Business cycles, duration dependence, and dating methods
- Artis, M. John, Zenon G. Kontolemis, and Denise R. Osborn, 1997, “Business Cycles for G7 and European Countries,” Journal of Business, Vol.70, pp. 249–79.
- Backus, David K, and Patrick J. Kehoe, 1992, “International Evidence of the Historical Properties of Business Cycles,” American Economic Review, Vol. 82, No. 4, pp. 864-88, September.
- Bry, Gerhard and Charlotte Boschan, 1971, “Cyclical Analysis of Economic Time Series : Selected Procedures and Computer Programs”, NBER Technical Working Paper No. 20.
- Burns, Arthur F., and Wesley C. Mitchell, 1946, Measuring Business Cycles. NBER.
- Canova, Fabio, 1998, “Detrending and Business Cycle Facts,” Journal of Monetary Economics, Vol. 41, No. 3, pp. 475-512.
- Diebold, Francis X., and Glenn Rudebusch, 1990, “A Nonparametric Investigation of Duration Dependence in the American Business Cycle,” Journal of Political Economy, Vol. 98, June 1990, pp. 596-616.
- Diebold, Francis X., Glenn Rudebusch, and Daniel Sichel, 1993, “Further Evidence on Business-Cycle Duration Dependence,” in: Business Cycles, Indicators and Forecasting, eds. James H. Stock and Mark W. Watson, pp. 255-84.
- Ohn, Jonathan, Larry W. Taylor, and Adrian Pagan. 2004, “Testing for Duration Dependence in Economic Cycles,” Econometrics Journal, Vol., pp. 528-49.
- Watson, Mark W., 1994, “Business-Cycle Durations and Postwar Stabilization of the U.S. economy,” American Economic Review, Vol. 84, No. 1, pp. 24-46.
- Castro, Vítor, 2008, “The Duration of Economic Expansions and Recessions: More than Duration Dependence,” NIPE Working Papers 18/2008, NIPE - Universidade do Minho.
- Harding, Don, and Adrian Pagan, 2002a, “Dissecting the Cycle: A Methodological Investigation,” Journal of Monetary Economics Vol. 49, pp. 365-81.
- ––––––, 2002b. “A Comparison of two Business Cycle Dating Methods,” Journal of Economic Dynamics and Control, Vol. 27, pp. 1681-690.
- Pagan, Adrian R., Kirill A. Sossounov, 2003, “A Simple Framework for Analysing bull and bear markets,” Journal of Applied Econometrics, Vol. 18, No. 1, pp. 23-46.
- Lunde, Asger, and Allen Timmermann, 2004, “Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets,” Journal of Business & Economic Statistics, American Statistical Association, Vol. 22, pp. 253-73.
- Sichel, Daniel E., 1994, “Inventories and the Three Phase of the Business Cycle,” Journal of Business & Economic Statistics, Vol. 12, No. 3, pp. 269-77.

### Asset prices, bubbles, and macro-financial links
- Borio, Claudio, and Patrick McGuire, 2004, “Twin Peaks in Equity and House prices?” BIS Quarterly Review, March, Vol. March, pp. 79-93.
- Borio, Claudio, and Philip Lowe, 2002, “Asset Prices, Financial and Monetary Stability: Exploring the Nexus,” BIS Working Paper, No. 114.
- Cecchetti, Stephen G., 2006, “Measuring the Macroeconomic Risks Posed by Asset Price Booms,” NBER Working Papers 12542.
- Cecchetti, Stephen G, and Hong Lee, 2008, “Measuring the Impact of Asset Price Booms Using Quantile Vector Autoregressions,” Working Paper, Brandeis University.
- Bordo, Michael D, and Olivier Jeanne, 2002, “Monetary Policy and Asset Prices: Does 'Benign Neglect' Make Sense?” International Finance, Vol.5, No. 2, pp. 139-64.
- Goetzmann, William N., Lingfeng Li, and K. Geert Rouwenhorst, 2005, “Long-Term Global Market Correlations,” Journal of Business, University of Chicago Press, Vol. 78, No. 1, pp. 1-38.
- Hall, Viv B., C. John McDermott, and James Tremewan, 2006, “The Ups and Downs of New Zealand House Prices,” Motu Economic and Public Policy Research.
- Terrones, Marco, 2004, “The Global House Price Boom,” IMF World Economic Outlook, September, pp.71-136.
- Cecchetti, Stephen G., 1995, “Distinguishing Theories of the Monetary Transmission Mechanism,” Proceedings, Federal Reserve Bank of St. Louis, Vol. May, pp. 83-97.

### International transmission, contagion, and financial integration
- Bekaert, Geert, and Harvey Campbell, 2000, “Foreign Speculators and Emerging Equity Markets,” Journal of Finance, Vol.55, pp. 565-613.
- Bekaert, Geert, Harvey Campbell and Robin Lumsdaine, 2002, “Dating the Integration of World Equity Markets,” Journal of Financial Economics, Vol. 65, No. 2, pp. 203-47.
- Ehrmann, Michael, Marcel Fratzscher, and Roberto Rigobon, 2005, “Stocks, Bonds, Money Markets and Exchange Rates - Measuring International Financial Transmission,” Working Paper Series 452, European Central Bank.
- Forbes, Kristin, and Roberto Rigobon, 2002, “No Contagion, Only Interdependence: Measuring Stock Market Comovements,” Journal of Finance, Vol.152 (5, October), pp. 2223-261.
- Frankel, Jeffrey A., and George Saravelos, 2010, “Are Leading Indicators of Financial Crises Useful for Assessing Country Vulnerability? Evidence from the 2008-09 Global Crisis,” NBER Working Papers 16047.
- Kose, M. Ayhan, Eswar S. Prasad, Kenneth Rogoff, and Shang-Jin Wei, 2010, “Financial Globalization and Economic Policies,” in Handbook of Development Economics, Vol. 5, ed. by Dani Rodrik, and Mark R. Rosenzweig (Oxford, Elsevier).
- Kose, M. Ayhan, Eswar S. Prasad, and Marco Terrones, 2003a, “Financial Integration and Macroeconomic Volatility,” IMF Staff Papers, Vol. 50, pp. 119-42.
- ––––––, 2003b, “How Does Globalization Affect the Synchronization of Business Cycles?” American Economic Review, Papers and Proceedings, Vol. 93, pp. 57–62.
- Goldstein, Morris, Carmen Reinhart, and Graciela Kaminsky, 2000, “Assessing Financial Vulnerability, an Early Warning System for Emerging Markets: Introduction,” MPRA Paper 13629, University Library of Munich, Germany.
- Quinn, Dennis, and Hans-Joachim Voth, 2008, “Free Flows, Limited Diversification: Explaining the Fall and Rise of Stock Market Correlations, 1890-2001,” CEPR Discussion Papers 7013, C.E.P.R. Discussion Papers.

### Monetary policy, transmission, and macro-financial measurement
- Bernanke, Ben, and Mark Gertler, 1989, “Agency Costs, Net Worth, and Business Fluctuations,” American Economic Review, Vol.79, pp. 14-31.
- Boivin, Jean, Michael T. Kiley, and Frederic S. Mishkin, 2010, “How Has the Monetary Transmission Mechanism Evolved Over Time?” Finance and Economics Discussion Series 2010-26, Board of Governors of the Federal Reserve System (U.S.).
- Cecchetti, Stephen G., 1995, “Distinguishing Theories of the Monetary Transmission Mechanism,” Proceedings, Federal Reserve Bank of St. Louis, Vol. May, pp. 83-97.
- Sinai, Allen, 1992, “Financial and Real Business Cycles,” Eastern Economic Journal, Vol.18, No. 1, pp. 1-54.
- Sinai, Allen, 2010, “The Business Cycle in a Changing Economy: Conceptualization, Measurement, Dating,” American Economic Review: Papers & Proceedings, Vol.100, pp. 25–9.
- Stock, James, H., and Mark W. Watson, 2010, “Indicators for Dating Business Cycles: Cross-History Selection and Comparisons,” American Economic Review: Papers & Proceedings, Vol.100, pp. 16–19.
- ––––––, 2005, “Understanding Changes In International Business Cycle Dynamics,” Journal of the European Economic Association, MIT Press, Vol. 3, No. 5, pp. 968-1006.
- ––––––, 1999, “Business Cycle Fluctuations in US Macroeconomic Time Series.” In Handbook of Macroeconomics, Vol. 1. eds. J.B. Taylor and M. Woodford.

*References section as provided in the original content unit.*

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