Strong Mean Reversion Research Articles

Dynamics of commercial real estate asset markets, return volatility and the investment horizon

The term structure of return volatility is estimated for both UK and US direct and securitised commercial real estate, using vector autoregressions. In a similar manner to the general stock market, returns of UK direct real estate and property shares, as well as US real estate investment trust returns, exhibit strong mean reversion. By contrast, US direct real estate returns show a considerable mean aversion effect over short investment horizons. This can be explained by the positive correlation between cash-flow and discount rate news, which can be interpreted as an under-reaction to cash-flow news. When estimating the return volatility of direct real estate markets, long-term investors need not be concerned about the choice of the parameter value used to unsmooth appraisal-based returns, because estimates of long-term volatility are almost unaffected by this choice.

Do Equity Market Correlations Really Change Over Time? The Case of the US and Asia-Pacific Markets

Existing literature suggests that conditional correlations between equity markets vary over time, and increase over periods of financial crises. I test this hypothesis on a set of eight national equity indices from the Asia-Pacific region on one hand, and the US market on the other. Tse (2000) constant conditional correlation test suggests that three out of eight market pairs exhibit constant conditional correlations. The remaining five correlations are time varying, but can be further subdivided into those that are characterized by high persistence in the dynamics, and those which display strong mean reversion graphs. Global asset managers should take these features into account when allocating funds across the Asia-Pacific as the expected diversification gains are likely to differ across the region, and depend on the characteristics of the correlation coefficients.

Asymmetric inflation dynamics: Evidence from quantile regression analysis

This paper applies the regression quantile approach developed by Koenker and Xiao (2004) to investigate the dynamic behavior of inflation in 12 OECD countries. By analyzing the behavior in a wide range of quantiles, this method allows us to quantify the influence of various sizes of shocks that hit the inflation, and is able to capture possible asymmetric adjustment of the inflation towards to its long-run equilibrium. It therefore sheds new lights on the inflation dynamics compared with the conventional unit root methodologies. Our results suggest that generally, the inflation rates are not only mean-reverting but also exhibit asymmetries in their dynamic adjustments, in which large negative shocks tend to induce strong mean reversion, and on the contrary, large positive shocks do not. Policy implications related to the empirical findings are also provided.

An analysis of the time series properties of the UKex-postreal interest rate: fractional integration, breaks or nonlinear

In this article we examine the persistent nature of the 3-month UK real interest rate for the period 1957:Q2 to 2008:Q2. We employ unit root and cointegration tests, confidence intervals for the sum of the Autoregressive (AR) coefficient, fractional integration tests, structural break tests and threshold modelling. Evidence from both unit root tests and AR modelling support the view that the real rate is nonstationary. Similarly, and in contrast to previous literature, the fractional integration test supports covariance nonstationarity, although there is evidence of mean reversion. Evidence from structural break tests support stationary behaviour, but only if we allow for three or more breaks, which may not be defendable on economic grounds. Finally, stationary behaviour is supported by a nonlinear exponential smooth transition model, which suggests that the real rate behaves in a random walk fashion when the rate is close to equilibrium but exhibits strong mean reversion when the disequilibrium becomes l...

Momentum trading, mean reversal and overreaction in Chinese stock market

The vast majority of the literature reports momentum profitability to be overwhelming in the US market and widespread in other countries. However, this paper finds that the pure momentum strategy in general does not yield excess profitability in the Chinese stock markets. We find instead strong mean reversion with an average half-life slightly shorter than 1 year. A pure contrarian investment strategy produces positive excess returns and in general outperforms the pure momentum strategy. Furthermore, momentum may interact with mean reversion. A strategy based on the rolling-regression parameter estimates of the model combining mean reversion and momentum generates both statistically and economically significant excess returns. The combined strategy outperforms both pure momentum and pure contrarian strategies. We conduct a number of robustness tests and confirm the basic findings. Collectively, our results support the overreaction hypothesis.

The Cross-Section and Time Series of Corporate Bond Returns

What are the cross-sectional and time-series characteristics of corporate bond returns? Do corporate bond risk premia vary over time and are these time-variations predictable? And if yes, is it a sign of market inefficiency? Recent empirical studies show a strong mean reversion at the monthly level. This research project first strives to establish systematically the cross-sectional and time series behavior at various frequencies and time lags. We will further analyze how the autoregressive components relate to bond-specific and aggregate explanatory variables, such as changing growth opportunities, or liquidity measures.

IDENTIFYING NONLINEAR SERIAL DEPENDENCE IN VOLATILE, HIGH-FREQUENCY TIME SERIES AND ITS IMPLICATIONS FOR VOLATILITY MODELING

In this article, we show how tests of nonlinear serial dependence can be applied to high-frequency time series data that exhibit high volatility, strong mean reversion, and leptokurtotis. Portmanteau correlation, bicorrelation, and tricorrelation tests are used to detect nonlinear serial dependence in the data. Trimming is used to control for the presence of outliers in the data. The data that are employed are 161,786 half-hourly spot electricity price observations recorded over nearly a decade in the wholesale electricity market in New South Wales, Australia. Strong evidence of nonlinear serial dependence is found and its implications for time series modeling are discussed.

Understanding Commercial Real Estate: How Different from Housing Is It?

On the one hand, urban economics suggests that commercial real estate and owner-occupied housing are driven by common fundamentals, which should make them perform similarly. On the other hand, stronger limits to arbitrage in housing suggest that wider swings in prices, which are unrelated to fundamentals, are feasible in that property sector. And wider swings in fundamentals could be expected in the commercial sector due to the longer lead times for commercial buildings to be permitted and delivered, leading to a greater possibility of a temporal mismatch between supply and demand. Yet, the author finds many more similarities than differences across the two real estate sectors. The simple correlation between appreciation rates on owner-occupied housing and commercial real estate is between 40% and 60%, depending on the data source. Both sectors also exhibit similar time-series patterns in their price appreciation, with persistence across individual years and mean reversion over longer periods. In this article, Gyourko explores the various linkages between these two huge sectors, including the cost of land and materials, cost of capital, lender optimism, and access to the productivity and amenities of a given labor market area. With the recent dramatic declines in house prices, the strong similarities between the two sectors are foreboding given that the commercial real estate capital structure looks weak and a strong mean-reversion process in prices may still be underway.

Short Rate Dynamics and Regime Shifts*

ABSTRACTWe characterize the dynamics of the US short‐term interest rate using a Markov regime‐switching model. Using a test developed by Garcia, we show that there are two regimes in the data: In one regime, the short rate behaves like a random walk with low volatility; in another regime, it exhibits strong mean reversion and high volatility. In our model, the sensitivity of interest rate volatility to the level of interest rate is much lower than what is commonly found in the literature. We also show that the findings of nonlinear drift in Aït‐Sahalia and Stanton, using nonparametric methods, are consistent with our regime‐switching model.

Nonlinear mean reversion in the G7 stock markets

We utilize the nonlinear unit root tests proposed by Park and Shintani (2005) and find strong evidence of nonlinear mean reversion between a US stock index and the stock indices in France, Germany, Italy and the UK. We identified an inaction band where deviations of these international stock indices from the US stock index follow a unit root process. Outside the band, however, they exhibit strong mean reversion properties. We show that standard linear unit root tests are not able to detect nonlinear cointegration and will yield the erroneous conclusion that the stock indices are not cointegrated.

Regional Financial Integration in the Caribbean: Evidence From Financial and Macroeconomic Data

This paper assesses the extent of regional financial integration in the Caribbean Community (CARICOM) by analyzing equity prices in the region and rigidity of external financing constraints. The results are presented in a cross-regional perspective. The Caribbean stock markets are not as well integrated as one would expect from the extent of cross-listing and importance of regional banking groups: price differentials of cross-listed stocks reach an average of 5 percent. Auto-Regressive models suggest that these price differentials are only slowly arbitraged away, with half-lives exceeding 7 worked days, even when looking only at large arbitrage opportunities (using a Threshold Auto-Regressive model). A speculative methodology using macroeconomic data seems to confirm these findings. A strong mean reversion of the current account (respectively regional trade imbalances) is interpreted, following Obstfeld and Taylor (2004), as a lack of ways to finance current account deficits, i.e. a lack of global (respectively regional) financial integration. The region appears to be much less integrated than the EU15 or the ASEAN+3 groups, although it fares well compared to other LDCs.

Regional Financial Integration in the Caribbean: Evidence from Financial and Macroeconomic Data

This paper assesses the extent of regional financial integration in the Caribbean Community (CARICOM) by analyzing equity prices in the region and rigidity of external financing constraints. The results are presented in a cross-regional perspective. The Caribbean stock markets are not as well integrated as one would expect from the extent of cross-listing and importance of regional banking groups: price differentials of cross-listed stocks reach an average of 5 percent. Auto-Regressive models suggest that these price differentials are only slowly arbitraged away, with half-lives exceeding 7 worked days, even when looking only at large arbitrage opportunities (using a Threshold Auto-Regressive model). A speculative methodology using macroeconomic data seems to confirm these findings. A strong mean reversion of the current account (respectively regional trade imbalances) is interpreted, following Obstfeld and Taylor (2004), as a lack of ways to finance current account deficits, i.e. a lack of global (respectively regional) financial integration. The region appears to be much less integrated than the EU15 or the ASEAN+3 groups, although it fares well compared to other LDCs.

Profitability analysis of Chinese listed firms: 1992–2004

This research collects and analyses the profitability data of Chinese listed companies from 1992 to 2004. Results show that, on average, the profitability of Chinese listed companies has declined over the period. Post-IPO earnings also exhibit a downward trend. A further analysis reveals that changes in listed firms’ profitability ratio follow a strong mean reversion pattern.

Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market

It is commonly known that wholesale spot electricity markets exhibit high price volatility, strong mean-reversion and frequent extreme price spikes. This paper employs a basic stochastic model, a mean-reverting model and a regime-switching model to capture these features in the Australian national electricity market (NEM), comprising the interconnected markets of New South Wales, Queensland, South Australia and Victoria. Daily spot prices from 1 January 1999 to 31 December 2004 are employed. The results show that the regime-switching model outperforms the basic stochastic and mean-reverting models. Electricity prices are also found to exhibit stronger mean-reversion after a price spike than in the normal period, and price volatility is more than fourteen times higher in spike periods than in normal periods. The probability of a spike on any given day ranges between 5.16% in NSW and 9.44% in Victoria.

Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market

While the vast majority of the literature reports momentum profitability to be overwhelming in the U.S. market and widespread in other countries, this paper finds that the pure momentum strategy in general does not yield excess profitability in the Chinese stock markets. We find instead strong mean reversion with an average half-life slightly shorter than one year. A pure contrarian investment strategy produces positive excess returns and in general outperforms the pure momentum strategy. Furthermore, momentum may interact with mean reversion. A strategy based on the rolling-regression parameter estimates of the model combining mean reversion and momentum generates both statistically and economically significant excess returns. The combined strategy outperforms both pure momentum and pure contrarian strategies. We conduct a number of robustness tests and confirm the basic findings. Collectively, our results seem to support the overreaction hypothesis.

The Estimation of Pricing Kernel of KOSPI 200 Options Under Stochastic Volatility

In this paper we make an analysis of KOSPI 200 index options listed in Korea Stock and Futures Exchange whose trading volume is world best these days. We adopt the stochastic volatility model suggested by Heston (1993) for the dynamics of the underlying asset and use EMM to estimate the parameters of option pricing kernel. The SNP distribution of the implied volatility contains AR (2) and ARCH effects, and the skewness of the distribution is much higher than normal distribution. The distribution has thinner left tail and fatter right tail than normal distribution, which is opposite to the case of S&P 500 options market. The result of estimation shows that Implied volatility series of KOSPI 200 options have weak mean reverting property and are almost nonstationary. The correlation coefficient between the implied volatility and returns is estimated to have negligible negative number. These features are also opposite to the case of S&P 500 options market where implied volatility is reported to have strong mean reversion, and the correlation between the implied VIatilIty and retturns is reported to have large negative number.

On real interest rate dynamics and regime switching

We find evidence of regime switching dynamics in the USA and the UK real interest rates over the period 1881–2003. For the UK, there is a regime in which the real interest rate displays a relatively stronger mean-reversion and a regime in which it displays a relatively weaker mean-reversion. The former regime is characterized by a relatively larger error in the estimation of the reversion parameter, and higher volatility. For the USA, the two regimes differ in volatility. The probability of transition from one regime to another is found to be significantly related to the inflation rate regime, and to the political regime. The results highlight the importance of regime switching in the dynamics of the real interest rate, as well as the role of inflation and political regimes in explaining this switching.

The Valuation of Power Options in a Pirrong-Jermakyan Model

This article uses a Pirrong-Jermakyan framework to value options on electricity, including daily strike, monthly strike, and spark spread options. This framework posits that power prices depend on two state variables - load and fuel prices. Although variations in load explain a large fraction of variations in power spot prices, power options do not vary strongly with load except very close to the expiry of daily strike and spark spread options due to the strong mean reversion in load. Load mean reversion also affects time decay, and the evolution of implied volatilities over time. I also discuss how to modify the model to take into account the impact of factors other than load and fuel prices (e.g., outages) that affect power prices; I show that a relatively simple modification can capture the effect of these factors on value if they are not very persistent and not priced in equilibrium.

04/00804 A novel nitrogen-fixing cellulytic bacterium associated with root of corn is a candidate for production of single cell protein: Emtiazi, G. et al. Biomass and Bioenergy, 2003, 25, (4), 423–426

It is commonly known that wholesale spot electricity markets exhibit high price volatility, strong mean-reversion and frequent extreme price spikes. This paper employs a basic stochastic model, a mean-reverting model and a regime-switching model to capture these features in the Australian national electricity market (NEM), comprising the interconnected markets of New South Wales, Queensland, South Australia and Victoria. Daily spot prices from 1 January 1999 to 31 December 2004 are employed. The results show that the regime-switching model outperforms the basic stochastic and mean-reverting models. Electricity prices are also found to exhibit stronger mean-reversion after a price spike than in the normal period, and price volatility is more than fourteen times higher in spike periods than in normal periods. The probability of a spike on any given day ranges between 5.16% in NSW and 9.44% in Victoria.

Estimation Error in the Assessment of Financial Risk Exposure

Value at Risk and similar measures of financial risk exposure require predicting the of an asset returns distribution. Assuming a specific form, such as the normal, for the distribution, the standard deviation (and possibly other parameters) are estimated from recent historical data and the cutoff value is computed. But this standard procedure ignores estimation error, which we find to be substantial even under the best of conditions. In practice, a tail event may represent a truly rare occurrence, or it may simply be a not-so-rare occurrence at a time when the predicted volatility underestimates the true volatility, due to sampling error. This problem gets worse the further in the one is trying to predict. Using a simulation of 10,000 years of daily returns, we first examine estimation risk when volatility is an unknown constant parameter. We then consider the more realistic, but more problematical, case of volatility that drifts stochastically over time. This substantially increases estimation error, although strong mean reversion in the variance tends to dampen the effect. Non-normal fat-tailed return shocks makes overall risk assessment much worse, especially in the extreme tails, but estimation error per se does not add much beyond the effect of fatness. Using an exponentially weighted moving average to downweight older data hurts accuracy if volatility is constant or only slowly changing. But with more volatile variance, an optimal decay rate emerges, with better performance for the most extreme tails being achieved using a relatively greater rate of downweighting. We first simulate non-overlapping independent samples, but in practical risk management, risk exposure is estimated day by day on a rolling basis. This produces strong autocorrelation in the estimation errors, and bunching of apparently extreme events. We find that with stochastic volatility, estimation error can increase the probabilities of multi-day events, like three 1% events in a row, by several orders of magnitude. Finally, we report empirical results using 40 years of daily S&P 500 returns which confirm that the issues we have examined in simulations are also present in the real world.