Topics in Finance - The Bullshit Bible PDF

Topics in Finance – The Bullshit Bible (BB) Book 1: Financial Market History (D.Chambers, E.Dimson) 1. Describe long-run asset returns and their order of magnitude (1900 – 2016) – Ch.1 Returns Cumulative real total returns: include reinvested income, are measured in local currency, and...

Topics in Finance – The Bullshit Bible (BB) Book 1: Financial Market History (D.Chambers, E.Dimson) 1. Describe long-run asset returns and their order of magnitude (1900 – 2016) – Ch.1 Returns Cumulative real total returns: include reinvested income, are measured in local currency, and are adjusted for inflation ➔ Reinvestment: practice of using dividends (for stocks), interests (for bonds) or any other form of income distribution earned in an investment to purchase additional shares or units, rather than receiving the distributions in cash ➔ Real returns: return made on an investment after subtracting costs such as inflation, taxes or fees In each country, equities performed best, long term government bonds less well and Treasury bills the worst. (equities > government bonds > Treasury bills). 1st: SAF, 2nd: AUS, 3rd: USA. A common factor among the best-performing equity markets is that they tended to be resource-rich. And/or New World countries. In the USA, an initial investment of 1$ grew in real value to 1271$ if invested in equities, 10$ in bonds and 2.7$ in bills. Performed 3rd in equity performance (6.4% per year), 6th for bonds (2% per year). The US was not the top performer and its return was not especially high relative to the world averages. With 6.4% in contrast with the real US dollar return of 4.3% on the World ex-USA Index. In UK, an initial investment of 1£ grew in real value to 445£ if invested in equities, 7£ in bonds and 3.3£ in bills. Equity: For most countries, the real annualized returns (%) on equity versus bonds and bills were positive, typically at a level of 3% to 6% per year. Equity > bonds > bills everywhere too. Bonds: Positive real return over the 116 years, except for Austria, Italy, Germany and Japan. These countries also delivered poor equity performance, the origins of which date from the first half of the 20th century. These were the countries that suffered the most from the ravages of war and from ensuing periods of high or hyperinflation. Risk Although risky equities performed better than less-volatile bonds or bills, investors did not benefit from investing in more volatile stock markets as compared to more stable markets. US equities had a sd of 20.1% (6th rank). Less volatile: Canada (17%) < Australia (17.7%) < New Zealand (19.4%) < Switzerland (19.5%) < UK (19.7%). The World Index had a sd of 17.5% which shows the benefits of international diversification. Most volatile: Italy (28.5%) < Japan (29.6%) < Finland (30%) = Austria (30%) < Germany (31.7%) < Portugal (34.3%) → countries the most affected by war, civil strife, inflation (Finland: concentrated markets in more recent periods). Inflation, Exchange rates, Common currency returns → not included here 2. Discuss the time-variation of expected returns and the challenges of timing the market – Ch.2 The increasing availability of long return histories and data allowed practitioners and academics to re-examine the case of market-timing and expected returns. The focus is put on time variation in expected equity premium, because equity risk dominates most investor portfolios. It is shown that practical and real-world applications of tactical market timing rules have a surprisingly poor track record. Time-variation of expected returns There was a shift from conventional constant to time-varying expected returns. Bond investors use market yields to estimate long-term returns. They may adjust these yields for roll downs effects or default risk to get better expected returns. But yields rather than realized past returns are a natural choice. Equity investors, on the other hand, rarely start by using dividend yields when assessing long term returns and this makes perfect sense if they assume expected returns to be constant over time. Therefore, future returns could be best estimated from the long-run average of the realized returns (unexpected returns should be wash out over a broadly representative period). Long sample periods give better estimates because they mitigate sampling variation in returns, assume no structural changes. Over time, the historical average return approach was refined to allow for time-varying expected cash or bond yields or inflation, plus a constant equity premium. Historical numbers (1900-2015) - Compound annual real return of world equities: 5% - Equity premium over global bonds: 3.2% - Equity premium over Treasury bills: 4.2% ➔ Assuming constant expected real returns, 5% is the best forecast for the future. Some important theories: - CAPM (Bill Sharpe, 1964): one period model that automatically implied a cst equity premium - Random walk model of asset prices (Fama, Samuelson, 1965) implied unpredictable returns and constant expected returns - EMH (Fama, 1976) (mentions the possibility of time-varying expected return tough) ➔ Academic consensus migrated towards those 3 theories But in the 80s, this consensus was challenged by evidence of cross-sectional anomalies inconsistent with CAPM, in favor of predictability of returns over time. Although there seemed to be little short-term predictability, claims of long-term predictability were much more promising. (ex: mean reversions in stock prices, equity market returns predicted by yield dividends) Fama and French (1989) argued that observed return predictability was related to business cycle fluctuations. Rational and irrational often agreed on the evidence, but argued on the interpretation and cause. In the 90s, rational (risk-based) and irrational (behavioral) theories and models were also developed, although most of the focus was put on anomalies as the long-term outperformance of value stocks. In 2000, Robert Shiller wrote a book about “Irrational Exuberance” about the predictive (ability) of a market valuation indicator called the Shiller P/E or CAPE. This ratio smoothes the earnings over 10 years and inflation-adjusts price/earnings. In 1990, as the bullish market continued, estimates for expected equity premium rose. The debate about constant and time-varying expected returns can also be couched as the debate regarding the perceived information in market yields (valuation ratios) → Does the market dividend yield predict low future returns (reflecting low risk premiums or investor irrationality) or high cash flow growth (reflecting growth optimism). Empirical evidence tends to suggest that low dividend yields tend to precede subpar market returns rather than above average growth. The answer is not clear and there ha been a full reversal in academic thinking in the past 20-30 years. After the bust of the bubble in early 2000s, it was clear that forward-looking valuation measures made better sense (better signal) than historical average returns. The behavioral school of thought was gaining ground at the expense of the efficient markets school. Robert Shiller was hailed in the media for his book, when the NSADAQ peaked. As often, reactions may lead to overreactions. So, it is not a coincidence that market-timing strategies became popular soon after crashes (1987, 2000-2002, 2008). Shiller’s P/E ratio became the most widely cited market-timing measure. The debate between constant and time-varying expected returns can be couched as the debate regarding the perceived information in market yields (valuation ratios). Does a low dividend yield predict low future returns (→ reflecting low required premiums or investor irrationality) or high future cash flow growth ( → reflecting growth optimism)? It must be one or the other or some combination between the two. Empirical research has shown that low dividend yields tend to precede subpar market returns rather than above-average growth. There has been a full reversal in academic thinking on this question in the past 20-30 years. ➔ The equity premium is no longer thought to be constant over time. All time variation in market valuation ratios was once thought to reflect changing growth expectations (with an unchanging ex-ante required risk premium), while now all such variation is thought to reflect changing required returns Not all academics agree that expected returns vary over time in a way that is captured by real- time valuation ratios. Goyal (le frère) and Welch concluded that these well-known predictors do not outperform a passive buy-and-hold investment when used out-of-sample. Another strand of literature studied time-varying returns and market-timing opportunities through the lens of boom/bust episodes (tulip mania, South Sea bubble, 1907, 1929, 1974 crises) mainly using descriptive rather than statistical analysis. This approach is contrarian. It involves identifying unsustainable bubbles and selling risky assets before the bust materialized. An opposite approach to market timing, the procyclical one, captures the spirit of trend: “Cut short your losses” (Ricardo, 1819). The assessment of expected returns is as much art as science. The challenge is to refine the art of prediction. Investors should exploit all our knowledge about historical experience, theories and current market yields and valuations without being overly dependant on any of these three anchors. As is the case with cross-sectional strategies, any time variation in expected returns can be explained by either rational or irrational theories. Rational explanations: time-varying volatility, time-varying risk aversion, time-varying risk of rare disasters. Time-series analysis borrows one main intuition from cross-sectional analysis → assets that outperform poorly in bad times should earn higher returns as a form of compensation. Hence, forward-looking required risk premiums should be higher after bad times. These explanations are mainly cyclical. Note however that there are also secular explanations for the apparent decline in required equity market returns: lower macro volatility, lower trading costs, easier investor access to passive global equity portfolios. Irrational explanations often rely on time-varying sentiment, cycles of greed and fear as well as social interaction. Valuation-based indicators are the most widely used measures of market conditions and timing signals, but the literature is filled with other indicators: - Recent price momentum or trends (Moskowitz, Ooi and Pedersen 2012). Value and momentum tend to be negatively correlated because often an asset that is cheap today tends to have performed poorly in the past. When two signals agree – for example market- timing context when the market is cheap and has recently begun to improve – the double signal is especially strong. - There are other indicators than value or momentum, but these often resemble either long- rum value or short-term momentum (ex: too-loose credit conditions, value-like, and tightening credit conditions, momentum-like, are bearish market indicators. - Over longer horizons, value and yield indicators tend to have the best predictive ability; over short horizons, momentum and macro indicators are more helpful. No tactical indicators are particularly reliable for near-term market timing. Although predictability results look more promising over longer horizons, their practical usefulness is limited. There are two chapters on value or yield based expected returns, promising forecasting ability and disappointing reality (not included here) ➔ Inverse Shiller P/E ratio and dividend discount model (DDM) to forecast expected real returns ➔ Big forecast failures in 1990 ➔ Problems: In-sample data, meaning that it is assumed that investors knew in real-time which quantile the indicator is for the full 1900-2014 period (bricolage). This is called the hindsight bias. A more realistic “out-of-sample” approach involves sorting starting yields by comparing the current starting yield by a preceding rolling average level. Removing hindsight bias and shortening the horizon reveal a more realistic picture. Predictability patterns are weaker if “out-of-sample” data are used, more so if a shorter horizon is used (sans blague). Challenges of timing the market The contrarian timing strategy did outperform the buy-and-hold strategy mildly over the full 115- year period, but the edge is not visually impressive (graph p.37). Moreover, the contrarian strategy uses leverage and turned out to have a higher volatility and thus a slightly lower Sharpe ratio. Perhaps worse, all the outperformance relative to the buy-and-hold strategy occurred in the first half of the sample; most readers can say that the contrarian strategy has underperformed the buy-and- hold strategy during our lifetime ! Here are some reasons for this underwhelming success 1. An adverse window for contrarian investing: the underperformance of the contrarian strategy since the 50s partly reflects the fact that the contrarian strategy resulted in an underinvestment in equities on average. 2. The difficulty of getting the timing right: market-timing signals are accurate only with the benefit of hindsight, where it may have been easy to buy near market lows and sell near market highs. In reality, contrarian indicators give relatively coarse signals and too often recommend buying and selling too early in the cycle. Investors tempted to move risky assets into cash should ask themselves if they have the patience to stay in cash for several years in the plausible scenario where the current low-yield environment persists several more years. 3. Headwinds from short-term momentum: Recall that financial assets tend to exhibit trending behavior (return persistence) over multi-month windows. This partly explains why contrarians signals are more often than not “too early”. Momentum implies that cheap things tend to get cheaper before they normalize (and vice versa for rich things). Contrarian value signals tend to be most efficient at prediction when used over a 2-3 year horizon when momentum headwinds have passed and before the value signal begins to decay. How can investors better take advantage of time-varying expected returns ? ➔ Combine both contrarian and momentum signals and combine them with macro and sentiment indicators. ➔ If investors had to choose only one type of market-timing approach, historical experience would suggest, surprisingly, to ignore valuations and apply instead the opposite of contrarian strategies – that is, procyclical approaches. Trend-following certainly has a better track record than contrarian timing. ➔ However, while the timing a single asset tends to be modest, the risk-adjusted return on a diversified trend following portfolio looks attractive over histories longer than arguably any other investment claim. Despite such evidence, many institutional investors find this strategy a poor cultural fit, unlike the contrarian approach, and also worry that its profits will not be sustainable (and would soon be arbitraged away) because the main explanations for these profits are behavioral and not rational risk-based. ➔ Even the most realistic market-timing models analyzed do no suggest that timing is easy. Short-term predictability is limited. Holding on underperforming positions is difficult, and the risk in market directional positions is concentrated. ➔ Lack of diversification is one of the main problem of market-timing strategies. Samuelson: “financial markets may be micro-efficient but macro-inefficient because it is so hazardous to battle (arbitrage) mispriced markets by timing strategies.” Keynes: “markets can stay irrational longer than you can remain solvent”. ➔ Thus, strategic diversification may be a better path to investment success than tactical timing. Important note: this chapter focuses on equity premium, which according to CAPM is the only long-run source of excess returns. Newer research emphasizes that we live in a multi-factorial world instead. Some risks are not rewarded (ex: stock-specific risk) so we should try to diversify them away with CAPM’s prescriptions. But some factors are well rewarded in the long run. We should diversify across several of them, not just the equity premium, but also other asset class premiums → term premium for bonds, illiquidity premiums for alternative assets, style premiums such as the long-run outperformance of value stocks and momentum stocks over their peers. 3. Discuss the challenges and biases of financial market databases – Ch.3 Introduction The recent financial crises highlighted weak empirical foundations of the economic and financial analytical models. One reason is the scarcity of long-run financial macro-data available to test the theoretical models, especially when allowing for structural changes, which are vital to evaluating the impact of the financial regulation, the cause of economic fluctuations, and interactions between economic changes and the financial system. Macroeconomic data are important for traders and investors to backtest their strategies and looking for accurate information. Databases containing security-specific information over broader time periods are still remarkably scarce and scattered in the literature. The construction of time series databases of country indices had not all the same objective and are therefore heterogeneous. A variety of construction rules were used, some of them no longer relevant.  General issues 1. Easy data bias (the more pervasive problem in existing long-run financial data): Scholars often focus on easily available historical data sources and omit troubled periods from their analysis. Historical sources that publish securities data are often hidden in distant archives with straight rules and thus difficult to access (increases the cost of data collection and explains why secondary sources are relied on). Unfortunately, secondary sources are also “second best”, because they summarize the data without explicitly stating the methodology or informing about potential biases (ex: Paris in the 19th century used secondary sources to price the official market). Periods of wars, political unrest or structural changes are sometimes omitted : a) Rare periods and difficult to interpret b) Rapid institutional changes require effort to make data coherent with previous data c) Markets are often closed during these hard times ➔ Consequence: many return series start after these events, which is a problem to reflect reality 2. Selection bias: corruption of statistical analysis resulting from the sampling process. Tendency in Finance to focus on larger companies, which may lead to underestimated returns. Selection of value stcks with high fundamentals-to-price ratio can also significantly alter returns. 3. Survivorship bias (special case of selection bias): Tendency in performance studies to consider only the securities of issuers that survived up to the end of the period studied → can lead to over-optimistic results because a majority of delistings result from failures. Concerns both markets and companies. This bias could contribute to the equity premium puzzle. Sometimes also called the delisting bias for companies. Influences security returns. 4. Weighting bias: the indices available are not necessarily representative of the investable universe. Indices often use equal or price weight because early data was not always available. 5. Non-synchronous trading effect: a. Within a single market: scholars often rely on the first or the last price of the day for their analysis, but these events do not systematically occur at the same time for each security. The consequence is a non-negligible bias in the moments and co-moments of returns (means, variances, covariances, betas, autocorrelation, cross- autocorrelation coefficients). b. Among several markets: not only openings and closing moments are different, but markets are also located in different time zones and markets are often closed at different dates (ex: religious holidays). The consequence is that return observations are not synchronous. Other biases: - The informational content and the behavior of securities’ prices can vary according to the price discovery system adopted by the exchange, particularly before electronic exchanges (ex: different levels of transparency, order-driven market instead of price-driven markets, adoption of fixings which might smooth volatility).  - Some securities are traded in spot but also in the forward and option markets, which may result in higher liquidity on the spot market and lower volatility than for securities traded spot only. - Prices and dividends are stated as nominal values, while inflation is a bis issue over the course of time. For long time periods, nominal values must be adjusted to inflation. Past inflation data are often of poor quality for many countries, especially during periods of wars. Stocks - Issues with prices, dividends, capital operations, exchange rates, number of shares outstanding and inflation can profoundly influence calculations. - Returns are often computed from end-to-end period to end-to-end period, but not always, which can result in hardly comparable data, introduces autocorrelation or reduce apparent volatility if indices are based on average of the lowest and highest monthly prices. - We should also care about interpreting the numbers (prices are sometimes reported as a profit or loss compared to initial values). The way in which prices are reported may switch over time from absolute value to percentage or vice versa. - Reported prices are not necessarily transaction prices. If no transactions take place in a day, either bid or ask of the previous day is chosen. If the problem lasts for a longer period, the issue of sticky prices arises. - Currency of denomination may change over time (ex: France → FF to euros). - Stocks an be simultaneously quoted in different currencies - Availability of dividends is often a bigger problem than the availability of prices → the largest part of total return is from dividend rather than capital gains. Missing dividends are estimated, which yielded in a proxy of returns and not the actual ones. Even if the dividends are available, there are several other issues (reported as annual without knowing ex-coupon day as an example) → solution: dividends added the return in the calendar year at year end, or spread pro rata temporis across the year → can impact volatility calculations and introduce seasonality. - Dividends are sometimes reported in another currency, which requires the availability of the right exchange rates. - Dividends can be net or gross of taxation (Some more details in the book p. 51-52) Bonds Many issues mentioned for stocks also apply to bonds, but the bond market shows even more diversity than equities, implying that most bond indices only focus on specific segments of the bond market that coincide with popular asset classes. Indices are constructed in the following way: a) Issuer: government bonds or corporate bonds. They have not the same credit risk. Further subdivisions are possible (central bank vs local authorities bonds). Scarcity or the sheer absence of historical information on both ratings and guarantees makes it difficult to build homogeneous long-run corporate bond indices. b) Maturity c) Coupon payment: not all bonds pay fixed coupons. Coupon payments can be specified in real rather than nominal terms or may be subject to optionality clauses. d) Liquidity: even more important to bonds than to stocks because a larger share of them is typically held to maturity and therefore not available for trading, worsening the stale price problem. Moreover, bonds are more likely to trade OTC, making price less accessible. ➔ Only a few long-run databases based on individual bond returns are found in the literature. Another important point is that most available bond indices are derived from yield series. Focusing on yields of a restricted set of bonds reduces the work but comes at a cost: ➔ Yield series are often provided from secondary sources. Control if they include accrued interest or not (Dirty vs Clean price). Yields must be taken from a representative sample. To build a return index, yields have to be transformed into returns, which relies on approximations if not full price information available. ➔ Method to transform yields into returns: o Investigate the correlation among several yield series by computing correlation coefficients for yields and yield changes. o Look into the approximation error introduced when going from yields to returns by using the Duration formula (p.55) and the Shiller approximations. For the single bond index, both approximations work well. ➔ Shows that even with limited information, overall bond returns can be reconstructed as long as the term of maturity of the proxy does not deviate too much from the market. 4. Discuss carry and momentum strategies in the foreign exchange market – Ch.4 Introduction Economists have long been skeptical about investors’ abilities to make sustainable profits from trading in foreign exchange markets. They have traditionally considered exchange rates to be difficult to predict and thus thought that foreign exchange instruments are only useful to hedge the currency risk of other transactions and investments. Of course, currency speculators occasionally make money, but such gains might be the result of luck rather than skills. The historical evidence suggests that returns to currency speculation can be high but are also time- varying and that any currency trading strategy entails high risks. This is consistent with the idea that returns to currency speculation arise from limits to arbitrage and reward investors who are willing to take such risks. Foreign exchange markets and speculation history → not included here Long-run evidence on currency returns Uncovered interest parity condition: states that high interest rate currencies depreciate on average relative to low interest rate currencies at a rate that eliminates the interest rate differential → therefore, should not be able to yield positive returns in the long run. Poor performance of macroeconomic models in forecasting short-term currency movements implies that predicting changes in exchange rates is an extremely difficult task.  The recent literature in empirical finance, however, shows that some simple currency speculation strategies have performed strongly during Post-Bretton Woods period. a. Carry: consists of borrowing low interest rate currencies and investing in high interest rate ones. Many papers find that this strategy yielded an annualized excess return of approximately 6% and a Sharpe ratio (before transaction costs évidemment) close to 0.6 when implemented on developed countries’ currencies from 1983 to 2009. This performance is stronger than that of US stocks during the same period. Researchers have interpreted the returns to the carry as compensation for risk-taking. Lustig and Verdelhan (2007) have argued that the high returns earned by carry traders on average compensate them for taking the risk of incurring significant losses in bad times (when consumption growth is low). Other researchers argued that carry traders are exposed to rare disasters or crash risk because at times they incur dramatic losses. They also showed that carry trade performs poorly in times of unexpected high global equity market volatility and unexpectedly high global foreign exchange volatility. Others provide evidence that this strategy yields low returns when the liquidity of the foreign exchange market deteriorates. b. Momentum: consists of borrowing in currencies with low recent returns and investing in currencies with high recent returns. Researchers showed that returns to currency momentum strategies are high when implemented on a wide range of developed AND emerging countries’ currencies but that their performance varies over time, which might make these strategies unattractive for traders with short term investment horizons. Limits to arbitrage could therefore explain the performance of currency momentum strategies over the last 30 years. History of currency strategies Doskov and Swinkels (2015) explored the long-run profitability of the carry trade using annual data on spot exchange rates and Treasury bill rates for 20 industrialized countries between 1900 and 2012. They found that this strategy yields a lower Sharpe ratio (0.2 – 0.4) when considering the entire period rather than only the Post-Bretton Woods period (0.6). It also highlights the fact that the carry trade incurred large occasional losses, which supports the explanations of its performance in terms of compensation for risk. Accominotti and Chambers (2014, 2016) and Cen and Marsh (2013) look at the performance of carry and momentum strategies during the era of high rate volatility: 1920s and 1930s. They find that both strategies performed strongly during the interwar period. They also found that transaction costs only accounted for 1/3 of their returns during that period. However, they also found that both strategies’ performance varied greatly over time and depended on the exchange rate regime. Table 4.1 p.75-76 summarizes the performance of carry and momentum strategies implemented on a sample of 9 currencies and shows their log annualized (sterling) excess returns and other statistics before and after transaction costs. Results: - It shows that carry trade yielded high risk-adjusted returns during the floating exchange rates periods (1920-1927 and 1985-2012) but performed much less well during the managed float period of the 1930s. - The performance of the momentum strategy was also much stronger in the 1920s than in any other period, including the modern period (on G10 currencies only). - Both strategies also incurred huge losses in certain months in both the interwar and modern periods. Currency investor case studies → not included here 5. Discuss long-term real estate returns – Ch. 5 Introduction Long-lasting non-financial assets – durable assets (real estate, luxury collectibles, precious metals, diamonds) – feature prominently in households’ portfolios. For many, real estate is the most important component of their portfolio. Despite their economic importance, it can be challenging for academics and investment professionals to form expectations of financial returns on durable assets. The risk exposures are hard to estimate, and a whole range of costs and benefits of “carry” may affect equilibrium expected returns. For example, houses are indivisible, leading the investor to under diversification. Houses are also illiquid, costly to maintain, store and ensure. In financial markets, long-term historical returns are often used as a first proxy for expected returns going forward, even if expected returns are time varying and differ from the historical average. Housing and land Figure 5.1 (p.88) presents long-term real (i.e. inflation-adjusted) price indices for US and UK real estate and land for the period 1900-2014. Table 5.1 (p.88) summarizes the real return distribution information, adding total equity and bond returns in comparison. Results: - Long-term appreciation rates of housing and land have been low. They are more or less comparable to the historical returns on government bills. - In the first decades of the 20th century, housing and land even lost value in real terms. - Between the 1940s and the 1990s, housing prices in the US barely moved in real terms – despite substantial economic and demographic growth over this period – before showing a boom and bust that is exceptional by historical standards. - UK housing prices have appreciated somewhat more steadily since the end of WW2, but also during this period the prices increases were interrupted by substantial setbacks. - The low capital gains on real estate have also been documented for other countries. As an example, Amsterdam housing prices went up at an annualized real rate of 0.7% between  1900 and 2010. In Paris, the annualized rate of appreciation has been estimated at 1.2% over the same period. - Capital gains are typically even lower in rural areas than in such “superstar” cities ➔ Important to mention that this analysis only focuses on capital gains and ignores income yields. Housing rental income yields vary over time and in the cross-section – with higher relative prices typically associated with lower yields – but can be substantial. Taking into account maintenance costs and other expenses, the average net rental yield for UK residential properties is about 5% between 1967 and 2003. For UK farmland, high recent price growth seems to have brought down income yields to less than 2%. Diversification and Inflation hedging The analysis also shows how real durable asset returns have historically co-moved with real equity and bond returns and also with inflation (through a linear regression model “Estimates of Equity Market, Bond Market, and Inflation betas). Table 5.4 (p.94) shows: - positive but relatively small equity market beta for real estate and land. - Deflated housing and land returns are negatively correlated with inflation (at least in UK). Conclusion - Rental income yield can add substantially to the returns on housing and land, whether the rental income is explicit or, as with owner-occupants, imputed. - Durable assets (in general) are unlikely to be good inflation hedges, but they may still help diversify a portfolio because of their imperfect correlations with financial assets. 6. Discuss bubble investing – Ch. 9 Introduction Bubbles are extremely rare events but history can be misleading. The rarity of bubbles in the historical record makes the sample size for inference small. The main finding of the study is that the probability of a crash conditional on a boom is only slightly higher than the unconditional probability. The chances that a market gave back its gains following a doubling in value are about 10%. In simple terms, bubbles are booms that went bad. Not all booms are bad. Financial press often defines bubbles as “periods of market euphoria followed by sharp price declines. In this chapter, the author argues that using past crashes in this way is misleading to both investors and policymakers. Particularly, during periods of market booms, focusing attention on a few salient crashes in financial history ignores the base rate for bubbles. In simples terms, bubbles are booms that went bad but not all booms are bad. The author defines a bubble as a large price decline after a large price increase. Moreover, the frequency of bubbles is quite small. The unconditional frequency of bubbles in the data is 0.3% to 1.4%, depending on the definition of a bubble. Not only are bubbles rare, but also they are conditional on a market boom.  ➔ Crashes that gave back prior gains happened only 10% of the time ➔ Market prices were more likely to double again following a 100% price boom History The first discussions in England of a stock market bubble centered on the speculation in shares for start-up companies during the 1690s. The first great stock market bubble began in France, with the creation if the Mississippi Company by John Law (ingenious financial innovation that merged a bank empowered to issue currency with companies chartered for overseas trade). The bubble burst in 1720 after the share price grew by more than 10 times in 2 years. ➔ There is a whole history of bubbles p.152- 154 Main Analysis – Data Dimson, Marsh and Staunton constructed an annual database of equity returns for 21 of the world’s stock markets from 1900 to 2014 (total return on equity indices dominated by dollars). They also used the KG/ICF database which is discontinuous and starts and stops at various intervals (list of securities p.156-157). Main Analysis – Boom and crashes Definition used: Bubble: boom followed by a crash Boom: large, rapid increase in stock prices Crash: Large, rapid decline in market prices Table 9.2 (p.160) defines a boom in two ways: 1) A single year of in which a market value (or cumulative returns) increased by at least 100% 2) A period of three years over which the market increased by 100% Tables 9.2 defines a bubble in two ways: 1) A drop of at least 50% in the following year 2) A drop of at least 50% over the next 5 years There are other ways to use price dynamics to define a bubble (ex: a high price-to-earnings ratio is a common metric invoked as a bubble indicator). Not all dividend and corporate indicators were available here. Results: Panel A reports the results for each definition of a bubble and shows the unconditional counts of market-years and the frequency of doubling and halving. ➔ 3387 market-years in the database, 72 of which were returns over 100% and 84 of which were returns under 50% (column 1). Among those 72, the conditional frequency of doubling in the subsequent year is 8.33%. This is not surprising given that a doubling is more likely in volatile markets. The probability of halving is 4.17%, which is about twice the unconditional  probability. In the following year, 6 of the 72 doubling markets more than double again and 3 of the 72 markets declined by half or more, essentially giving back the prior year’s gains. o The concerned markets (reversals) are: Argentina (1976-1977), Austria (1923-1924), Poland (1993-1994) ➔ 3308 market-years with 2% of the markets with returns in excess of 100% Tables 92 also shows that: - Bubbles may take some time to deflate. After 5 years, 15.28% of the boom markets had crashed to less than half their levels at t=0. On the other hand, 26.39% of the markets had at least doubled in value again → After a stock market boom of at least 100% in a single year, the frequency of doubling in the next 5 years was significantly greater than the probability of halving. o An important remark is that the frequency of crashing at the 5-year horizon is significantly higher for booming markets than the unconditional frequency, while the frequency of doubling after 5 years is about the same → Thus, a boom DOES increase the probability of a crash, but the crash probability is low. Table 9.2 also shows that a rapid boom is NOT a strong indicator of a bust. Probabilities move from 2% to 4% over a horizon of 1 year and from 6% to 15% over a 5-year horizon. ➔ Data shows that a significant proportion of booms that doubled the market values in a single calendar year were NOT followed by a crash that gave back these gains. Table 9.2 also includes results for markets that halved in value in a single year. These are similar to the doubling market results. Subsequent tail events (doubling or halving) at the 1-year and 5-year horizons are higher than the unconditional probabilities of these events. Important remarks: - Doubling in a single year may be too restrictive as a definition of a boom o That is why the broader definition of a bubble was developed in Panel B. (market that doubles over a 3-year horizon). It was chosen to include the US booms of 1928 and 1999 and also US booms of 1935, 1945, 1956 and 1997. It results in 460 events of a doubling over 3 years → Halved 4.57% of the time in the following year, which is about twice the unconditional probability of a 1-year halving event (still rare). o At a 5-year horizon, the probability of the market value declining by a half after 5 year is 10.42%, which is higher than the unconditional probability of 6.31% but not dramatically so. Conclusion Past studies of the mean reversion of stock markets suggest that goes up must go down; a large boom should increase the probability of a future decline. However, focusing on the rejection of the null of no association between past and future multi-year market returns can be misleading for economic decision making. The fact that probabilities of a decline increase from 6% to 10% following a 3-year boom may not be as relevant to investor choice as the fact that the chance of doubling in value is twice the chance of halving in value over the same horizon. ➔ Bubbles are rare ➔ A significant proportion of price increases in global markets are not followed by a crash ➔ The bubbles that did not burst are just as important for investors to know about as the bubbles that did burst. ➔ Dangerous to not include bubbles in the data of a research as it forgoes the equity market premium ➔ For regulators, the evidence raises the question of whether deflating a bubble is the right course of action. Indeed, if a bubble is associated with investment in new technologies with high economic potential as well as high economic uncertainty, it forces a choice between guarding against a financial crisis versus allowing productive investment. Book 2: This time is different (Reinhart, Rogoff) 1. Describe crises by type (inflation crises, currency crashes, banking crises, external debt crisis, domestic debt crisis) – Ch.1 Crises defined by quantitative thresholds: inflation, currency crashes/debasement a) Inflation crises: Many high-inflation spells can best be described as chronic – lasting many years, sometimes dissipating, and sometimes plateauing at an intermediate level before exploding. Many studies use a 12-month inflation threshold of 40% of higher as the mark of a high-inflation episode. Here, they defined an inflation crisis at a threshold of 15% or 25%. For the pre-WW1 period, even 40% per annum is too high as an inflation threshold because inflation rates were lower. (average inflation rate of 5% for 1914-2006). b) Currency crashes: Annual depreciation in excess of 15%. Similar in timing and order of magnitude of the profile for inflation crises c) Banking crisis: Defined in two ways here: - Banking crisis of type 1 → systemic (severe): Bank runs that lead to the closure, merging or takeover by the public sector of one or more financial institutions - Banking crisis of type 2 → financial distress (milder): If no runs, the closure, margining or takeover or large-scale government assistance of an important financial institution that marks the start if a string of similar outcomes for other financial institutions. d) External debt crisis: A sovereign default is defined as the failure of a government to meet a principal or interest payment on the due date (or within the specified grace period). These episodes include instances in which debt is rescheduled is ultimately extinguished in terms less favorable than the original obligation. The majority of external public debt has been denominated in foreign currency and held by foreign residents. e) Domestic debt crisis: The definition given above for the external debt crisis applies. In addition, domestic debt crisis have involved the freezing of bank deposits and/or forcible conversions in such deposits from dollars to local currency. In most countries, over most of their history, domestic debt has been denominated in the local currency and held mainly by residents.  2. Describe serial defaults – Ch.1 Serial defaults refer to multiple sovereign defaults on external or domestic public debt, or both. These defaults may occur five or fifty years apart, and they can range from wholesale default (or repudiation), which is quite rare, to partial default through rescheduling. 3. Describe debt intolerance and debt thresholds– Ch.2 Debt intolerance is a syndrome in which weak institutional structure and a problematic political system make external borrowing a tempting device for governments to employ to avoid hard decisions about spending and taxing. Definition of debt intolerance: extreme duress many emerging markets experience at external debt levels that would seem quite manageable by the standards of advanced countries. The duress typically involves a vicious cycle of loss in market confidence, spiraling interest rates on external government debt, and political resistance to repaying foreign creditors. Debt thresholds: Emerging market countries with overall ratios of public debt to GNP above 100% run a significant risk of default. Even advanced countries like Japan with a debt level of about 170% of its GNP is considered as problematic. Table 2.2 shows that external debt exceeded 100% of GNP in only 16% of the default or restructuring episodes, that more than half of all defaults occurred at levels below 60%, and that there were below 40% of GNP in nearly 20% of the cases. ➔ Tables 2.1 and 2.2 give information about percentage of total defaults or restructurings in middle-income countries with respect to external debt to GNP ratio ➔ We can use the frequency distributions (derived from the tables) to ask whether there is a threshold of external debt to GNP for emerging economies beyond which the risk of experiencing extreme symptoms of debt intolerance rises sharply. o Over half of the observations for countries with a sound credit history are at levels of external debt to GNP below 35% (47% of the observations are below 30%). By contrast, for those countries with a relatively tarnished credit history, levels of external debt to GNP above 40% are required to capture the majority of observations o When external debt levels of emerging markets are above 30-35% of GNP, risks of a credit event starts to increase significantly.  4. Describe how to measure vulnerability– Ch.2 To operationalize the concept of debt intolerance - to find a way to quantitatively measure a country’s fragility as a foreign borrower – we focus on two indicators: the sovereign ratings reported by Institutional Investor and the ratio debt to GNP (or external debt to exports). The Institutional Investor ratings (IIR), which are compiled twice a year, are based on survey information provided by economists and sovereign risk analysts at leading banks and securities firms. The ratings go from 0 to 100, with 100 representing the lowest likelihood of defaulting on government debt obligations. A proxy for default risk may be constructed: (100 – IIR). Unfortunately, market-based measures of default risk (ex: based on prices at which country’s debt trades on secondary markets) are available only for a smaller range of countries and over much shorter sample period. The second major component of measure of a country’s vulnerability to lapse or relapse into external debt default consists of total external debt (public + private), scaled by GNP and exports. Historically, much of government debt in emerging markets was external, and the small part of external debt that was private before a crisis often became public. Table 2.3 shows the panel pairwise correlations between the two debt ratios and the Institutional Investor measures of risk for large sample of developing countries. It highlights the fact that different measures of risk present a very similar picture of different country’s relative rankings and of the correlation between risk and debt. As expected, the numbers are statistically significant. Methodology to measure risk intolerance: We use the components of debt intolerance (IIR and debt ratios) in a two-step algorithm to define creditors’ “clubs” and regions of vulnerability. Step1: The mean and standard deviation are calculated for all the countries and group them into 3 clubs: club A (IIR >= 73.5), club B , club C (IIR 20% annual). Because inflation represents a form of partially defaulting on government liabilities that are not fully indexed to prices or the exchange rate, this observed co-movement is not surprising. Default through inflation became more commonplace over the years as fiat money displaced coinage as the principal means of exchange. In effect, even when we focus on the post 1900 era of fiat money, the pattern is evident. That is, a tight relationship between inflation and outright external default is of fairly modern vintage. For 1900-2007, the simple pairwise correlation coefficient is 0.39; for the years after 1940, the correlation nearly doubles to 0.75. This increased correlation can probably be explained by a change in the willingness of governments to expropriate through various channels and the abandonment of a gold (or metallic) standard rather than by a change in macroeconomic influences. In Depression-era defaults, deflation was the norm. To the extent that such price-level declines were unexpected, debt burdens became even more onerous and detrimental to economic performance (Irving Fisher “debt-deflation” theory). As a corollary to that theory, an adverse economy presumably makes sovereign default more likely. In contrast, a higher background rate of inflation makes it less likely that an economy will be pushed into a downward deflationary spiral. The defaults and inflation moved together positively in the later part of the post-WW2 period. It probably indicates that governments are now more willing to resort to lighten their real interest burdens. Moreover, inflation conditions often continue to worsen after an external default. Shut out from international capital markets and facing collapsing revenues, governments that have not been able to restrain their spending have, on a recurring basis, resorted to the inflation tax, even in its most extreme hyperinflationary form. 9. Describe domestic default and why it occurs – Ch.7 Domestic debt is a large portion of countries’ total debt. It averages almost 2/3 of the total public debt (in this sample, 64 countries). These debts have typically carried a market interest rate except during the era of financial repression after WW2. Theoretical models encompass a wide range of assumptions about domestic public debt. The overwhelming majority of models simply assume that debt is always honored. The general assumption throughout the literature is that although governments may inflate debt away, outright defaults on domestic public debt are extremely rare. This assumption is in stark contrast to the literature on external public debt, in which government’s incentive to default is one of the main focuses in history. In fact, our reading of the historical record is that overt de jure default on domestic public debt, though less common than external default, are hardly rare. The dataset includes more than 70 cases of overt default (compared to 250 defaults on external debt) since 1800. These de jure defaults took place via a potpourri of mechanisms, ranging from forcible conversions to lower coupon rates to unilateral reduction of principal to suspensions of payments. Domestic defaults are far more difficult to detect than defaults on international debt and are not well documented. Why would a government refuse to pay its domestic public debt in full when it can simply inflate the problem away? One answer is that inflation causes distortions, especially to the banking system and the financial sector. Of course, there are other forms of de facto default beside inflation. The combination of heightened financial repression with rises in inflation was an especially popular form of default in the 1960s to the early 1980s. Interest rate ceilings combined with inflation spurts are also common. So the assumption embedded in many theoretical models, that governments always honor the nominal face value of debt, is significant overstatement, particularly for emerging markets past and present. Nevertheless, we also caution against reaching the conclusion at the opposite extreme, that governments can ignore powerful domestic stakeholders and simply default at will on domestic debt. 10. Compare domestic and external default – Ch.6 & Ch.7 Our reading of the historical record is that overt de jure default on domestic public debt, though less common than external default, are hardly rare. The dataset includes more than 70 cases of overt default (compared to 250 defaults on external debt) since 1800. These de jure defaults took place via a potpourri of mechanisms, ranging from forcible conversions to lower coupon rates to unilateral reduction of principal to suspensions of payments. 11. Describe banking crises and their link to capital flows, housing price cycles – Ch.10 Banking crises For advanced economies during 1800-2008, the picture that emerges is one of serial banking crises. Until very recently, studies of banking crises have focused either on episodes drawn from the history of advanced countries (mainly banking panics before WW2) or on the experience of modern-day emerging markets. This dichotomy has perhaps been shaped by the belief that for advanced economies, destabilizing, systemic, multicountry financial crises are a relic of the past. Banking crises have long impacted rich and poor countries alike. The incidence of banking crises proves to be remarkably similar in both high-income and middle-to-low-income countries. Indeed, the tally of crises is particularly high for the world’s financial centers: France, UK and the US. The sample includes 2 types of banking crises: the first one is common in poor developing countries, although it occasionally surfaces in richer emerging markets. These crises are really a form of domestic default that governments employ in countries where financial repression is a major form of taxation. Under financial repression, banks are vehicles that allow governments to squeeze more indirect tax revenue from citizens by monopolizing the entire savings and payments system, not simply currency. Governments force local residents to save in banks by giving them few, if any, other option. They then stuff the debt into banks via reserve requirements and other devices. This allows the government to finance a part of its debt at a very low interest rate; financial repression thus constitutes a form of taxation. Citizens put money into banks because there are few other safe places for their savings. Governments, in turn, pass regulations and restrictions to force the banks to relend the money to fund public debt. It often results in higher inflation. Banks’ role in effecting maturity transformation – transforming short-term deposit funding into long-term loans – makes them vulnerable to bank runs. During a run on a bank, depositors lose confidence in the bank and withdraw en masse. As withdrawals mount, the bank is forced to liquidate assets under duress. Typically the prices received are “fire sale” prices, especially if the bank holds highly illiquid and idiosyncratic loans. The problem of having to liquidate at fire sale prices can extend to a far broader range of assets during a systemic banking crisis of the kind we focus on. Different banks often hold broadly similar portfolios of assets, and if all banks try to sell at once, the market can dry up completely. If a run occurs, it can bankrupt the entire system, turning a damaging problem into a devastating one. The implosion of the US financial system during 2007-2008 came about precisely because many financial firms outside the traditional and regulated banking sector financed their illiquid investments using short-term borrowing. Moreover, the fact that banking crises, especially systemic ones, are associated with economic downturns is well established. Countries may perhaps “graduate” from serial default on sovereign debt and recurrent episodes of very high inflation but history tells us that graduation from recurrent banking and financial crises is much more elusive. Link to capital flows There is a striking correlation between freer capital mobility and the incidence of banking crises. Periods of high international mobility have repeatedly produced international banking crises. Link to housing price cycles In the study, they find that real estate price cycles around banking crises are similar in duration and amplitude across the two groups of countries. This is surprising given that almost all the other macroeconomic and financial time series (income, consumption, government spending,…) exhibit higher volatility in emerging markets. One common factor feature of the run-up to banking crises is a sustained surge in capital inflows, “capital flow bonanza”. Link to housing price cycles The now-infamous real estate bubble in the US that began to deflate at the end of 2005 occupies center stage as a culprit in the recent global financial crisis. The pattern that emerges is very clear: a boom in real housing prices in the run-up to a crisis is followed by a marked decline in the year of the crisis and subsequent years. Banking crises tend to occur either at the peak of a boom in real housing prices or right after the bust. Table 10.3 illustrates the magnitudes and durations of the downturns in housing prices that have historically accompanied major banking crises in advanced and emerging economies. Two features stand out from the summary statistics: first, the persistence of the cycle in real housing prices in both advanced economies and emerging markets, typically for 4 to 6 years. The second is that the magnitude of the declines in real housing prices around banking crises from peak to trough are not appreciably different in emerging and advanced economies. The prolonged housing price downturns following financial crises are in stark contrast to the behavior or real equity prices. Indeed, the pattern of recovery is more V-shaped. The figure below shows the evolution of real equity prices from 4 years prior to a crisis to 3 years afterwards. Equity prices typically peak before the year of a banking crisis and decline for 2 to 3 years as the crisis approaches, and in the case of emerging markets, in the year following the crisis. The recovery is complete in the sense that 3 years after the crisis, real equity prices are on average higher at the precrisis peak. 12. Discuss inflation and modern currency crashes – Ch.12 No emerging market country in history, even USA, has managed to escape bouts of high inflation. Of course the problems of external default, domestic default and inflation are integrally related. A key finding of this study is how difficult it is to escape a history of high and volatile inflation. There are 3 pages of history of inflation in different countries → not included here, too long Many people have concluded that “this time is different” and that inflation will never return. We certainly agree that there have been important advances in our understandings of central bank design and monetary policy, particularly in the importance of having an independent central bank that places heavy weight on inflation stabilization. Currency crashes Inflation crises and exchange rate crises have traveled hand in hand in the overwhelming majority of episodes across times and countries (with a markedly tighter link in countries subject to chronic inflation, where the pass-through from exchange rates to prices is greatest). When we look at exchange rate behavior, we can see that probably the most surprising evidence comes from the Napoleonic Wars, during which exchange instability escalated to a level that had not been seen before and was not to be seen again for nearly a hundred of years. The figure also shows a significantly higher incidence of crashes and larger median changes in the more modern period. This should hardly come as a surprise, given the prominent exchange rate crises in Mexico (1994), Asia (1997), Russia (1998), Brazil (1999) and Argentina (2001), among other countries.  13. Discuss dollarization – Ch.12 Countries with sustained high inflation often experience dollarization, a huge shift towards the use of foreign currency as a transaction medium, a unit of account, a unit of account, and a store of value. From a practical perspective, this can imply the use of foreign hard currency for trade or, even more prevalently, the indexation of bank accounts, bonds, and other financial assets to foreign currency (sometime called “liability dollarization). In many cases, a sustained shift toward dollarization is one of the many long-term costs of episodes of high inflation, one that often persists even if the government strives to prevent it. A government that has grossly abused its monopoly over the currency and payments system will often find this monopoly more difficult to enforce in the aftermath. Reducing dollarization and regaining control of monetary policy is often one of the major aims of disinflation policy after a period of elevated inflation. Yet dedollarization can be extremely difficult. Successful disinflations generally have not been accompanied by large declines in the degree of dollarization. Figure 12.5 shows that the degree of dollarization at the end of the period of disinflation was the same as or higher than at the time of the inflation peak in more than half of the episodes. Moreover, the decrease in the degree of dollarization in many of the other episodes was generally small. This persistence of dollarization is consistent with the evidence on “hysteresis”. It refers to the tendency for a country that has become dollarized to remain so long after the original reasons for the shift (usually excessive inflation on domestic currency) have abated. The persistence of dollarization is a regularity that tends to be associated with countries’ inflation histories. In fact, countries that had repeated bouts of high inflation over the past few decades generally exhibited a higher degree of dollarization in the late 1990s than did countries with better inflationary histories. Interpreting the (unconditional) probability of high inflation used in figure 12.5 (lower graph) as a rough measure of the credibility of monetary policy gives us some insights as to why achieving low inflation is generally not a sufficient condition for a rapid decrease of dollarization; namely, a low country with a poor inflationary history will need to maintain inflation at low levels for a long period before it can significantly reduce the probability of another inflationary bout. This is yet another parallel to the difficulties a country faces in graduating from debt intolerance. Undoing Domestic Dollarization We have shown that reducing inflation is generally not sufficient to undo domestic dollarization, at least at horizons of more than five years. Nevertheless, some countries have managed to reduce their degree of of domestic dollarization. To identify those countries, it is useful to treat separately cases in which the reduction in domestic dollarization originated in a decline in locally issued foreign currency public debt from those that originated in a decline in the share of foreign currency deposits in broad money. The few governments that managed to dedollarize their locally issued foreign currency obligations followed one of the two strategies: they either amortized the outstanding debt stock on the original terms and discounted the issuance of those securities, or the changed the currency denomination of the debt – sometimes, but not always using market-based approaches. One example of the former strategy is Mexico’s decision to redeem in US dollars all dollar-linked tesobonos outstanding at the time of its December 1994 crisis (using the loans it received from the IMF and the USA) and to cease issuing domestic foreign currency-denominated bonds thereafter. A recent example is Argentina’s decision in late 2001 to convert to domestic currency the government bonds that it had originally issued in US dollars. ➔ There are few other examples here that are not included 14. Describe the US subprime meltdown – Ch.13 As money poured into the United States, US financial firms, including mighty investment banks such as Goldman Sachs, Marrill Lynch, Lehman Brothers, as well as large universal banks, all saw their profits soar. The size of the US financial sector more than doubled from 1970 to 2007. Leaders in the financial sector argued that in fact heir high returns were the result of innovation and genuine value- added products, and they tended to grossly understate the latent risks their firms were taking. In their eyes, financial innovation was a key platform that allowed the US to effectively borrow much larger quantities of money from abroad. For example, innovations such as securitization allowed US consumers to turn their previously illiquid housing assets into ATM machines, which represented a reduction in precautionary saving. The most extreme and the most immediate problems were caused by the market for mortgage loans made to “subprime”, or low-income, borrowers. “Advances” in securitization, as well as a seemingly endless run-up in housing prices, allowed people to buy houses who might not previously have thought they could do so. Unfortunately, many of these borrowers depended on loans with variable interest rates and low initial “teaser” rates. When it came time to reset the loans, rising interest loans and a deteriorating economy made it difficult for many to meet their mortgage obligations. Many were led to think that “this time is different” for the following reasons: - The USA, with the world’s most reliable system of financial regulation, the most innovative financial system, a strong political system, and the world’s largest and most liquid capital markets, was special. It could withstand huge capital inflows without worry. - Rapidly emerging developing economies needed a secure place to invest their funds for diversification purposes - Increased global financial integration was deepening global capital markets and allowing countries to go deeper into debt - In addition to its other strengths, the USA has superior monetary policy institutions and monetary policy makers - New financial instruments were allowing many new borrowers to enter mortgage markets Above all, the huge run-up in housing prices – over 100% nationally in five years – should have been an alarm, especially fueled as it was by rising leverage. At the beginning of 2008, the total value of mortgages in the US was approximately 90% of GDP. Unfortunately, the efforts to maintain growth and prevent sharp stock market declines had the effect of taking the safety valve off the pressure cooker. To sum up: - Real housing prices increased very fast, accentuated by securitization - Rising leverage by banks - Large sustained current account deficits - Slowing trajectory of economic growth  15. Describe the aftermath of financial crises – Ch.14 This chapter focuses on the aftermath of systemic banking crises, which all share 3 main characteristics: - First, asset market collapses are deep and prolonged. Declines in real housing prices average 35% stretched out over six years, whereas equity price collapses average 56% over a downturn of about three and a half years. - Second, the aftermath of banking crises is associated with profound declines in output and employment. The unemployment rate rises an average of 7 percentage points during the down phase of the cycle, which lasts on average more than 4 years. Output falls (from peak to trough) more than 9% on average, although the duration of the downturn, averaging roughly 2 years, is considerably shorter than that of unemployment - Third, as noted earlier, the value of government debt tend to explode; it rose an average of 86% in the major post-WW2 episodes. The main cause of debt explosions is NOT the widely costs of bailing out and recapitalizing the banking system. In fact, the biggest driver of debt increases is the inevitable collapse in tax revenues that governments suffer from a spike in the wake of deep and prolonged output contractions. Many countries also suffer from a spike in the interest burden on debt, for interest rates soar, and in a few cases (ex:Japan) countercyclical fiscal policy efforts contribute to the debt buildup. There is also: - Sovereign default, debt restructuring and/or near default for emerging countries after a financial crisis. The credit ratings decline. In conclusion, crises have a deep and lasting effect on asset prices, output and employment. The global nature of the recent crises has made it far more difficult, and contentious, for individual countries to grow their way out through higher exports or to smooth the consumption effects through foreign borrowing. Book 3: When Genius failed (Lowenstein)