Showing papers on "Volatility (finance) published in 2002"
TL;DR: In this paper, the moments and the asymptotic distribution of the realized volatility error were derived under the assumption of a rather general stochastic volatility model, and the difference between realized volatility and the discretized integrated volatility (which is called actual volatility) were estimated.
Abstract: Summary. The availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility error—the difference between realized volatility and the discretized integrated volatility (which we call actual volatility). These properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation-intensive methods.
2,207 citations
TL;DR: In this article, the authors investigated the role of information-based trading in affecting asset returns and showed that information does affect asset prices, and that a difference of 10 percentage points in the probability of information based trading between two stocks leads to a difference in their expected returns of 2.5 percent per year.
Abstract: We investigate the role of information-based trading in affecting asset returns. We show in a rational expectation example how private information affects equilibrium asset returns. Using a market microstructure model, we derive a measure of the probability of information-based trading, and we estimate this measure using data for individual NYSE-listed stocks for 1983 to 1998. We then incorporate our estimates into a Fama and French (1992) asset-pricing framework. Our main result is that information does affect asset prices. A difference of 10 percentage points in the probability of information-based trading between two stocks leads to a difference in their expected returns of 2.5 percent per year. ASSET PRICING IS FUNDAMENTAL to our understanding of the wealth dynamics of an economy. This central importance has resulted in an extensive literature on asset pricing, much of it focusing on the economic factors that influence asset prices. Despite the fact that virtually all assets trade in markets, one set of factors not typically considered in asset-pricing models are the features of the markets in which the assets trade. Instead, the literature on asset pricing abstracts from the mechanics of asset price evolution, leaving unsettled the underlying question of how equilibrium prices are actually attained. Market microstructure, conversely, focuses on how the mechanics of the trading process affect the evolution of trading prices. A major focus of this extensive literature is on the process by which information is incorporated into prices. The microstructure literature provides structural models of how prices become efficient, as well as models of volatility, both issues clearly of importance for asset pricing. But of perhaps more importance, microstructure models pro
1,337 citations
TL;DR: In this paper, the authors present an accurate method of estimating option portfolio value and the sensitivities of option portfolio values to stock price and stock-return volatility that is easily implemented using data from only the current year's proxy statement or annual report.
Abstract: The costs associated with compiling data on employee stock option portfolios is a substantial obstacle in investigating the impact of stock options on managerial incentives, accounting choice, financing decisions, and the valuation of equity. We present an accurate method of estimating option portfolio value and the sensitivities of option portfolio value to stock price and stock-return volatility that is easily implemented using data from only the current year’s proxy statement or annual report. This method can be applied to either executive stock option portfolios or to firm-wide option plans. In broad samples of actual and simulated CEO option portfolios, we show that these proxies capture more than 99% of the variation in option portfolio value and sensitivities. Sensitivity analysis indicates that the degree of bias in these proxies varies with option portfolio characteristics, and is most severe in samples of CEOs with a large proportion of out-of-the-money options. However, the proxies’ explanatory power remains above 95% in all subsamples.
1,245 citations
TL;DR: In this paper, the authors investigated the role of various explanations for the cyclical volatility of real economic activity and concluded that the moderation in volatility is attributable to a combination of improved policy, identifiable good luck in the form of productivity and commodity price shocks, and other unknown forms of...
Abstract: From 1960 to 1983, the standard deviation of annual growth rates in real GDP in the United States was 2.7%. From 1984 to 2001, the corresponding standard deviation was 1.6%. This paper investigates this large drop in the cyclical volatility of real economic activity. The paper has two objectives. The first is to provide a comprehensive characterization of the decline in volatility using a large number of U.S. economic time series and a variety of methods designed to describe time-varying time-series processes. In so doing, the paper reviews the literature on the moderation and attempts to resolve some of its disagreements and discrepancies. The second objective is to provide new evidence on the quantitative importance of various explanations for this "great moderation." Taken together, we estimate that the moderation in volatility is attributable to a combination of improved policy (20-30%), identifiable good luck in the form of productivity and commodity price shocks (20-30%), and other unknown forms of ...
1,080 citations
TL;DR: In this article, the authors explore how the introduction of habit preferences into the simple intertemporal consumption-based capital asset pricing model "solves" the equity premium and risk-free rate puzzles.
1,030 citations
TL;DR: This paper explored the effect of equity volatility on corporate bond yields and found that idiosyncratic firm-level volatility can explain as much cross-sectional variation in yields as can credit ratings, together with the upward trend in idiosyncratic equity volatility documented by Campbell, Lettau, Malkiel, and Xu.
Abstract: This paper explores the effect of equity volatility on corporate bond yields. Panel data for the late 1990's show that idiosyncratic firm-level volatility can explain as much cross-sectional variation in yields as can credit ratings. This finding, together with the upward trend in idiosyncratic equity volatility documented by Campbell, Lettau, Malkiel, and Xu (2001), helps to explain recent increases in corporate bond yields.
1,007 citations
TL;DR: In this paper, the authors proposed using the price range in the estimation of stochastic volatility models and showed that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise.
Abstract: We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor. VOLATILITY IS A CENTRAL CONCEPT in finance, whether in asset pricing, portfolio choice, or risk management. Not long ago, theoretical models routinely assumed constant volatility (e.g., Merton (1969), Black and Scholes (1973)). Today, however, we widely acknowledge that volatility is both time varying and predictable (e.g., Andersen and Bollerslev (1997)), and stochastic volatility models are commonplace. Discrete- and continuous-time stochastic volatility models are extensively used in theoretical finance, empirical finance, and financial econometrics, both in academe and industry (e.g., Hull and White (1987), Heston (1993), Bates (1996), Ghysels, Harvey, and Renault (1996), Jarrow (1998), Duffie, Pan, and Singleton (2000)). Unfortunately, the estimation of stochastic volatility models has proved quite difficult. The Gaussian quasi-maximum likelihood estimation (QMLE) approach of Harvey, Ruiz, and Shephard (1994), which initially seemed appeal
939 citations
TL;DR: This paper examined the relation between net buying pressure and the shape of the implied volatility function (IVF) of S&P 500 index options and options on twenty individual stocks, and found that time variation in the volatility of an option series is directly related to net buying pressures from public order flow, while call options tend to dominate in stock option markets.
Abstract: This paper examines the relation between net buying pressure and the shape of the implied volatility function (IVF) of S&P 500 index options and options on twenty individual stocks. We find that time variation in the implied volatility of an option series is directly related to net buying pressure from public order flow. We also find that movements in implied volatility in the index option market are most strongly affected by buying pressure for index puts, while call options tend to dominate in stock option markets. Simulated delta-neutral trading strategies that sell options generate abnormal returns that match the deviations of the IVFs from historical volatility levels. Index option abnormal returns decrease monotonically across exercise prices and are significant, while stock option abnormal returns are symmetric, smaller, and insignificant. When vega risk is also hedged in the simulations using index options, however, the abnormal returns go from positive to negative, indicating that the steeply sloped IVF for index options does not present a profitable arbitrage opportunity once the costs of hedging have been considered.
750 citations
TL;DR: In the 20 years following the publication of the ARCH model, there has been a vast quantity of research uncovering the properties of competing volatility models as mentioned in this paper, including high-frequency volatility models, large-scale multivariate ARCH models, and derivatives pricing models.
Abstract: In the 20 years following the publication of the ARCH model, there has been a vast quantity of research uncovering the properties of competing volatility models. Wide-ranging applications to financial data have discovered important stylized facts and illustrated both the strengths and weaknesses of the models. There are now many surveys of this literature. This paper looks forward to identify promising areas of new research. The paper lists five new frontiers. It briefly discusses three—high-frequency volatility models, large-scale multivariate ARCH models, and derivatives pricing models. Two further frontiers are examined in more detail—application of ARCH models to the broad class of non-negative processes, and use of Least Squares Monte Carlo to examine non-linear properties of any model that can be simulated. Using this methodology, the paper analyses more general types of ARCH models, stochastic volatility models, long-memory models and breaking volatility models. The volatility of volatility is defined, estimated and compared with option-implied volatilities. Copyright © 2002 John Wiley & Sons, Ltd.
702 citations
TL;DR: This article employed a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al.
Abstract: Using the Investors' Intelligence sentiment index, we employ a generalized autoregressive conditional heteroscedasticity-in-mean specification to test the impact of noise trader risk on both the formation of conditional volatility and expected return as suggested by De Long et al. [Journal of Political Economy 98 (1990) 703]. Our empirical results show that sentiment is a systematic risk that is priced. Excess returns are contemporaneously positively correlated with shifts in sentiment. Moreover, the magnitude of bullish (bearish) changes in sentiment leads to downward (upward) revisions in volatility and higher (lower) future excess returns.
574 citations
TL;DR: The authors generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime, and the resulting Markov regime-switching Garch model improves on existing variants, for instance by making multi-period-ahead volatility forecasting a convenient recursive procedure.
Abstract: Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime. The resulting Markov regime-switching GARCH model improves on existing variants, for instance by making multi-period-ahead volatility forecasting a convenient recursive procedure. The empirical analysis demonstrates that the model resolves the problem with the high single-regime GARCH forecasts and that it yields significantly better out-of-sample volatility forecasts.
TL;DR: This paper examined changes in the information content of earnings over the past three decades using the two metrics from Beaver [1968]: abnormal trading volume and abnormal return volatility and found no evidence of a decline in the informativeness of earnings announcements.
Abstract: This paper examines changes in the information content of earnings over the past three decades using the two metrics from Beaver [1968]: abnormal trading volume and abnormal return volatility We find no evidence of a decline in the information content of earnings announcements over the past three decades, as measured by both abnormal trading volume and return volatility around quarterly earnings announcements If anything, our results suggest an increase over time in the informativeness of quarterly earnings announcements Variables reflecting changes in firm-specific factors account for a portion of the observed increase
01 Jan 2002
TL;DR: In this article, a brief survey of continuous time finance is given, where the authors categorize diffusion models according to the nature of their volatility coefficient and treat models whose volatility coefficient does not exhibit randomness.
Abstract: This chapter gives a brief survey of continuous time finance. We categorize diffusion models according to the nature of their volatility coefficient. Models whose volatility coefficient does not exhibit randomness are treated in Sect. 3.1. Models whose volatility coefficient follows a stochastic process are discussed in Sect. 3.2.
TL;DR: In this paper, the authors use time series of option prices on the SP500 and FTSE indices to study the deformation of the implied volatility surface and show that it may be represented as a randomly fluctuating surface driven by a small number of orthogonal random factors.
Abstract: The prices of index options at a given date are usually represented via the corresponding implied volatility surface, presenting skew/smile features and term structure which several models have attempted to reproduce. However, the implied volatility surface also changes dynamically over time in a way that is not taken into account by current modelling approaches, giving rise to `Vega' risk in option portfolios. Using time series of option prices on the SP500 and FTSE indices, we study the deformation of this surface and show that it may be represented as a randomly fluctuating surface driven by a small number of orthogonal random factors. We identify and interpret the shape of each of these factors, study their dynamics and their correlation with the underlying index. Our approach is based on a Karhunen-Loeve decomposition of the daily variations of implied volatilities obtained from market data. A simple factor model compatible with the empirical observations is proposed. We illustrate how this ...
TL;DR: In this paper, the authors investigate the relative importance of various factors in explaining the volatility skew observed in the prices of stock options traded on the Chicago Board Options Exchange and find no robust relationship between skewness and the firm's leverage.
Abstract: We investigate the relative importance of various factors in explaining the volatility skew observed in the prices of stock options traded on the Chicago Board Options Exchange. The skewness of the risk-neutral density implied by individual stock option prices tends to be more negative for stocks that have larger betas, suggesting that market risk is important in pricing individual stock options. Also, implied skewness tends to be more negative in periods of high market volatility, and when the risk-neutral density for index options is more negatively skewed. Other firm-specific factors, including firm size and trading volume a so help explain cross-sectional variation in skewness. However, we find no robust relationship between skewness and the firm's leverage. Nor do we find evidence that skewness is related to the put/call ratio, which may be viewed as a proxy for trading pressure or market sentiment. Overall, firm-specific factors seem to be more important than systematic factors in explaining the variation in the skew for individual firms.
TL;DR: Lewellen et al. as mentioned in this paper studied the asset-pricing implications of parameter uncertainty and showed that when investors must learn about expected cash-f lows, empirical tests can find patterns in the data that differ from those perceived by rational investors.
Abstract: This paper studies the asset-pricing implications of parameter uncertainty We show that, when investors must learn about expected cash f lows, empirical tests can find patterns in the data that differ from those perceived by rational investors Returns might appear predictable to an econometrician, or appear to deviate from the Capital Asset Pricing Model, but investors can neither perceive nor exploit this predictability Returns may also appear excessively volatile even though prices react efficiently to cash-f low news We conclude that parameter uncertainty can be important for characterizing and testing market efficiency THERE IS MUCH EVIDENCE THAT STOCK RETURNS are predictable At the aggregate level, variables such as interest rates, financial ratios, and the default premium appear to forecast stock returns ~eg, Fama and French ~1989! and Lewellen ~2001!! Further, LeRoy and Porter ~1981! and Shiller ~1981! argue that price volatility cannot be explained solely by changes in dividends, providing indirect evidence that stock returns are predictable At the firm level, Fama and French ~1992, 1996! and Jegadeesh and Titman ~1993! show that size, book-to-market, and past returns together explain much of the crosssectional variation in average returns There seems little doubt that expected returns vary both cross-sectionally and over time The interpretation of predictability is more contentious The empirical results are potentially consistent with either market efficiency or mispricing In general terms, market efficiency implies that prices fully ref lect available information To formalize this idea for empirical testing, Fama ~1976! distinguishes between the probability distribution of returns perceived by “the market,” based on whatever information investors view as relevant, and the true distribution of returns conditional on all information The market is said to be ~informationally! efficient if these distributions are the same It follows that, in an efficient market, investors should be aware of any cross* Lewellen is from the MIT Sloan School of Management and Shanken is from the Simon Graduate School of Business Administration at the University of Rochester, and NBER We are
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TL;DR: The authors model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework.
Abstract: We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads to pronounced counter-cyclical variation in the Sharpe ratio. We document significant lead-lag correlations between the conditional moments that also appear related to business cycles. Finally, we show that although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due to the lead-lag correlations.
TL;DR: In this paper, a new conditional jump model was developed to study jump dynamics in stock market returns. But the model is not suitable for the analysis of stock market volatility and the model does not capture the rally often observed in equity markets following a significant downturn.
Abstract: This article develops a new conditional jump model to study jump dynamics in stock market returns. We propose a simple filter to infer ex post the distribution of jumps. This permits construction of the shock affecting the time t conditional jump intensity and is the main input into an autoregressive conditional jump intensity model. The model allows the conditional jump intensity to be time-varying and follows an approximate autoregressive moving average (ARMA) form. The time series characteristics of 72 years of daily stock returns are analyzed using the jump model coupled with a generalized autoregressive conditional heteroscedasticity (GARCH) specification of volatility. We find significant time variation in the conditional jump intensity and evidence of time variation in the jump size distribution. The conditional jump dynamics contribute to good in-sample and out-of-sample fits to stock market volatility and capture the rally often observed in equity markets following a significant downturn.
TL;DR: In this article, the authors investigated the multifractal model of asset returns (MMAR), a class of continuous-time processes that incorporate the thick tails and volatility persistence exhibited by many financial time series.
Abstract: This paper investigates the multifractal model of asset returns (MMAR), a class of continuous-time processes that incorporate the thick tails and volatility persistence exhibited by many financial time series. The simplest version of the MMAR compounds a Brownian motion with a multifractal time-deformation. Prices follow a semi-martingale, which precludes arbitrage in a standard two-asset economy. Volatility has long memory, and the highest finite moments of returns can take any value greater than 2. The local variability of a sample path is highly heterogeneous and is usefully characterized by the local Holder exponent at every instant. In contrast with earlier processes, this exponent takes a continuum of values in any time interval. The MMAR predicts that the moments of returns vary as a power law of the time horizon. We confirm this property for Deutsche mark/U.S. dollar exchange rates and several equity series. We develop an estimation procedure and infer a parsimonious generating mechanism for the e...
TL;DR: In this article, the distinction between risk and ambiguity (Knightian uncertainty) is considered and a recursive multiple-priors utility model is proposed to learn about an uncertain parameter from conditionally i.i.d. signals, where ambiguous signals capture differences in information quality that cannot be captured by noisy signals.
Abstract: This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally i.i.d. signals. Ambiguous signals capture differences in information quality that cannot be captured by noisy signals. They may increase the volatility of conditional actions and they prevent ambiguity from vanishing in the limit. Properties of the model are illustrated with two applications. First, in a dynamic portfolio choice model, stock market participation costs arise endogenously from preferences and depend on past market performance. Second, ambiguous news induce negative skewness of asset returns and may increase price volatility. Shocks that trigger a period of ambiguous news induce a price discount on impact and are likely to be followed by further negative price changes.
TL;DR: This paper explored the hypothesis that high volatility of real and nominal exchange rates may be due to the fact that local currency pricing eliminates the pass-through from changes in exchange rates to consumer prices.
TL;DR: The authors showed that the B/M effect is greater for stocks with higher idiosyncratic return volatility, higher transaction costs and lower investor sophistication, consistent with the market mispricing explanation for the anomaly.
Abstract: This paper shows that the book-to-market (B/M) effect is greater for stocks with higher idiosyncratic return volatility, higher transaction costs and lower investor sophistication, consistent with the market mispricing explanation for the anomaly. The B/M effect for high volatility stocks exceeds that for the low volatility stocks in 20 of the 22 sample years. Also, volatility exhibits significant incremental power beyond the transaction costs and investor sophistication measures in explaining cross-sectional variation in the B/M effect. These findings are consistent with the Shleifer and Vishny (1997) thesis that risk associated with the volatility of arbitrage returns deters arbitrage activity and is an important reason why the B/M effect exists.
TL;DR: In this article, the authors exploit the distributional information contained in high-frequency intraday data in constructing a simple conditional moment estimator for stochastic volatility diffusions.
TL;DR: The authors found that good practices and good policy appear to have played a more important role in explaining the post-1984 decline in the volatility of consumer price inflation than exogenous disturbances, suggesting that good luck is the most likely explanation.
Abstract: The volatility of U.S. real GDP growth since 1984 has been markedlylowerthanoverthepreviousquartercentury.Weutilizefrequency-domain and VAR methods to distinguish among competing explanations for this reduction: improvements in monetary policy, better business practices, and a fortuitous reduction in exogenous disturbances. We find that reduced innovation variances account for much of the decline in aggregate output volatility, suggesting that good luck is the most likely explanation. Good practices and good policy appear to have played a more important role in explaining the post-1984 decline in the volatility of consumer price inflation.
TL;DR: In this paper, the authors provide both qualitative and quantitative measures of the precision of measuring integrated volatility by realized volatility for a fixed frequency of observation and propose a simple approach to capture the information about integrated volatility contained in the returns through the leverage effect.
Abstract: In this paper we provide both qualitative and quantitative measures of the precision of measuring integrated volatility by realized volatility for a fixed frequency of observation. We start by characterizing for a general diffusion the difference between realized and integrated volatility for a given frequency of observation. Then we compute the mean and variance of this noise and the correlation between the noise and the integrated volatility in the Eigenfunction Stochastic Volatility model of Meddahi (2001a). This model has as special cases log-normal, affine and GARCH diffusion models. Using previous empirical results, we show that the noise is substantial compared with the unconditional mean and variance of integrated volatility, even if one employs five-minute returns. We also propose a simple approach to capture the information about integrated volatility contained in the returns through the leverage effect. We show that in practice, the leverage effect does not matter. Copyright © 2002 John Wiley & Sons, Ltd.
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TL;DR: In this article, the authors investigated the multifractal model of asset returns (MMAR), a class of continuous-time processes that incorporate the thick tails and volatility persistence exhibited by many financial time series.
Abstract: This paper investigates the multifractal model of asset returns (MMAR), a class of continuous-time processes that incorporate the thick tails and volatility persistence exhibited by many financial time series. The simplest version of the MMAR compounds a Brownian motion with a multifractal time-deformation. Prices follow a semi-martingale, which precludes arbitrage in a standard two-asset economy. Volatility has long memory, and the highest finite moments of returns can take any value greater than 2. The local variability of a sample path is highly heterogeneous and is usefully characterized by the local Holder exponent at every instant. In contrast with earlier processes, this exponent takes a continuum of values in any time interval. The MMAR predicts that the moments of returns vary as a power law of the time horizon. We confirm this property for Deutsche mark/U.S. dollar exchange rates and several equity series. We develop an estimation procedure and infer a parsimonious generating mechanism for the exchange rate. In Monte Carlo simulations, the estimated multifractal process replicates the scaling properties of the data and compares favorably with some alternative specifications.
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TL;DR: In this paper, the authors argue that changes in inventory behavior stemming from improvements in information technology (IT) have played a direct role in reducing real output volatility, and they suggest that technical progress is primarily responsible for the reduced volatility of output.
Abstract: 1. INTRODUCTION The volatility of real GDP growth in the United States has fallen by half since the early 1980s relative to the prior postwar experience. (1) Inflation also stabilized around then (although only when compared with a shorter period of volatility in the 1970s). Some studies have argued that an improvement in U.S. monetary policy around that time can explain both the lower output and inflation volatility; others have attributed the decreased volatility of GDP to a reduction in the size of the shocks hitting the U.S. economy--essentially "good luck"--and have attributed the improvement on the inflation front to better policy. (2) In this paper, we argue that changes in inventory behavior stemming from improvements in information technology (IT) have played a direct role in reducing real output volatility. Our rationale is that even if the magnitude of the exogenous shocks hitting the economy has not changed, the role of inventory investment in magnifying or propagating those shocks has moderated significantly. Thus, even a large swing in final demand would be expected to produce a smaller swing in production now than it would have twenty or thirty years ago. We argue further that this implies a more modest role for both luck and improved monetary policy in stabilizing output, although policy remains the likely source of reduced inflation volatility. Our view that technical progress is primarily responsible for the reduced volatility of output is formed largely by two important features of the data. First, in a growth-accounting sense, most of the reduction in aggregate variability can be explained by a corresponding reduction in the variability of output in the durable goods sector. The nondurables, services, and structures sectors of the economy do not contribute importantly to the increased aggregate stability, nor are these sectors themselves significantly more stable. (3) Second, the dramatic decline in the volatility of durables production is not accompanied by a similar reduction in the variability of durables final sales. In fact, the ratio of output variability to sales variability in that sector drops sharply after the early 1980s. The view that policy alone brought about the increased stability would have to explain why policy affected the volatility of production so much more than final sales, and why the phenomenon of increased stability has been concentrated in the durable goods sector. In other words, policy (or good luck) would have to explain why the impact was felt primarily in durable goods inventories. After providing a detailed look at the changing volatility of macro data, we present a model in which improved information about final demand leads to less volatile output, both absolutely and relative to final demand. We then show how changes in monetary policy alone are unlikely to have important effects on the volatility of production relative to final sales. Finally, we suggest that monetary policy played the primary role in the reduction of inflation volatility. 2. THE CHANGING MACROECONOMIC ENVIRONMENT In this section, we provide an overview of the changing volatility of the U.S. macroeconomy over the postwar period 1952:3 to 2000:2. We begin by comparing the behavior of inflation and output volatility over three subsamples and conclude that while the stability of output growth over the past fifteen or so years is unprecedented, the current stability of inflation is similar to the stability that prevailed in the 1950s and 1960s. Turning then to disaggregate output data, we point out the importance of the durable goods sector in explaining the decline in aggregate volatility. We then look at the changing relative volatilities of output and final sales throughout the goods sector and highlight the role of inventory behavior in stabilizing output. 2.1 Inflation and Output Chart 1 presents U.S. real GDP growth from 1953:2 to 2000:2; Chart 2 depicts the consumer price index (CPI) over the same period. …
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TL;DR: In this article, the authors investigate the role of good luck in the moderation of cyclical volatility of real economic activity in the United States and provide new evidence on the quantitative importance of various explanations for this "great moderation".
Abstract: From 1960-1983, the standard deviation of annual growth rates in real GDP in the United States was 27% From 1984-2001, the corresponding standard deviation was 16% This paper investigates this large drop in the cyclical volatility OF real economicactivity The paper has two objectives The first is to provide a comprehensive characterization of the decline in volatility using a large number of US economic time series and a variety of methods designed to describe time-varying time series processes In so doing, the paper reviews the literature on the moderation and attempts to resolve some of its disagreements and discrepancies The second objective is to provide new evidence on the quantitative importance of various explanations for this 'great moderation' Taken together, we estimate that the moderation in volatility is attributable to a combination of improved policy (20-30%), identifiable good luck in the form of productivity and commodity price shocks (20-30%), and other unknown forms of good luck that manifest themselves as smaller reduced-form forecast errors (40-60%)
TL;DR: In this paper, the authors revisited the yield spread's usefulness for predicting future real GDP growth and found that the contribution of the spread can be decomposed into the effect of expected future changes in short rates and the effect on the term premium.
Abstract: This paper revisits the yield spread's usefulness for predicting future real GDP growth. We show that the contribution of the spread can be decomposed into the effect of expected future changes in short rates and the effect of the term premium. We find that both factors are relevant for predicting real GDP growth but the respective contributions differ. We investigate whether the cyclical behavior of interest rate volatility could account for either or both effects. We find that while volatility displays important correlations with both the term structure of interest rates and GDP, it does not appear to account for the yield spread's usefulness for predicting GDP growth.
TL;DR: In this paper, the authors evaluate the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns, which apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using highfrequency data.
Abstract: The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the conditional variance under various realistic univariate heteroscedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks. Copyright © 2002 John Wiley & Sons, Ltd.