High Serial Correlation Research Articles

Uncertainties and risks in reservoir operations under changing hydroclimatic conditions

Abstract Uncertainties and risks associated with hydroclimatic variations pose a challenge to the management and planning of water resources systems. This study demonstrates the importance of understanding the changing hydrologic regime of the Feather River Basin (FRB) and its impacts on water resources decision variables (i.e., storage requirement and performance of a water supply reservoir). A simple storage–yield–reliability model (S–Y–R) is used to quantify the risk of the stationary-based designed reservoir under the temporal variation and nonstationarity in N-year blocks of the Feather River Inflow into Lake Oroville (FRI). Furthermore, the potential linkages of the long-term variability in the FRI to climate variations are investigated by applying wavelet spectrum and coherence analysis to the FRI and atmospheric–oceanic indices (e.g., ENSO and PDO). The results show substantial variations in the FRB hydrologic regime over different timescales with episodes of abrupt shifts toward significantly higher storage requirements, and decrease in the reservoir performance during historical periods of high FRI variance and lag-1 serial correlation. Although the mean inflows are high, the storage capacity is increased by (a) 38 and 48% due to the 5 and 20% increase in the FRI variance during the periods 1904–1953 and 1960–2009, respectively, and (b) 34% due to the increase in the serial correlation coefficient in the period of 1750–1799. Likewise, reservoir performance significantly decreased for the same reasons in the same critical periods. The reliability and resilience dropped to 74 and 29% (1904–1953) and to 76 and 50% (1960–2009 period) due to the increased variance of FRI, while vulnerability reached 70% during the high lag-1 correlations in 1532–1581 and 1564–1613, and 40% in 1904–1953 due to the high FRI variance. Furthermore, the wavelet coherence analysis observes strong associations between the streamflow and climate teleconnection patterns in specific periodic cycles during the same critical periods which link the variability in FRI and decision variables to the hydroclimatic variations. These linkages give a primary indication for the reservoir storage requirement characterization.

Dynamic regularization discriminant local preserving projection method for fault diagnosis

Due to the high dimensionality, serial correlation, and nonlinearity of industrial process data, the primary task for diagnosing fault is to extract key fault features from fault datasets. In this paper, to obtain much more inherent fault information, a dynamic regularization discriminant local preserving projection approach (DRDLPP) based on feature reduction is put forward to diagnose fault, which addresses the small sample size problem of discriminant local preserving projection (DLPP) by incorporating the regularization term into the objective function of DLPP. The enhanced performance of DRDLPP for fault diagnosis over conventional diagnostic approaches mostly benefits from two aspects: One aspect is that DRDLPP can discover local manifold fault information hidden in original sample space by preserving the local neigborhood structure of data; The other aspect is that DRDLPP has the remarkable capacity to capture dynamic information by extending the observation vector with previous observation vectors. What is more, the information criterion function is utilized to capture the optimal dimensionality reduction order and time lag of DRDLPP method. The experimental results of the Tennessee Eastman process demonstrate that the proposed DRDLPP approach provides a better visual performance and achieve lower misclassification rates in fault diagnosis.

A dependent frequency–severity approach to modeling longitudinal insurance claims

In nonlife insurance, frequency and severity are two essential building blocks in the actuarial modeling of insurance claims. In this paper, we propose a dependent modeling framework to jointly examine the two components in a longitudinal context where the quantity of interest is the predictive distribution. The proposed model accommodates the temporal correlation in both the frequency and the severity, as well as the association between the frequency and severity using a novel copula regression. The resulting predictive claims distribution allows to incorporate the claim history on both the frequency and severity into ratemaking and other prediction applications. In this application, we examine the insurance claim frequencies and severities for specific peril types from a government property insurance portfolio, namely lightning and vehicle claims, which tend to be frequent in terms of their count. We discover that the frequencies and severities of these frequent peril types tend to have a high serial correlation over time. Using dependence modeling in a longitudinal setting, we demonstrate how the prediction of these frequent claims can be improved.

The business cycle implications of fluctuating long run expectations

I consider a DSGE model where consumption depends on the present discounted value of wage and capital income. The agent is uncertain if these variables are stationary or non-stationary and puts positive probability on both representations. The agent uses Bayesian learning to update his probability weights on each model and these weights vary over time according to how well each model fits the data. The model exhibits an improved fit to the data relative to a no-learning benchmark. It requires half the standard deviation of exogenous shocks to match the volatility of output and still matches the relative volatilities of key business cycle variables. The model lowers the contemporaneous correlation of consumption and wages with output and generates positive autocorrelation in growth rates. Impulse responses exhibit persistent responses and forecast errors are positively serially correlated. Finally, in contrast to the existing literature, the model endogenously generates observed time varying volatility and long run predictability of business cycle variables, especially for investment, without generating counterfactually high serial correlation of forecast errors.

Why is there so much Inertia in Inflation and Output? A Behavioral Explanation

Serial correlation in macroeconomics is pervasive. Macroeconomic modellers find it impossible to model this feature without relying on serially correlated shocks. Using a behavioral macroeconomic model, I show that serial correlation in inflation and output can easily be explained in the context that agents do not have rational expectation. This important feature is missing in the standard New Keynesian rational expectations models. The rational expectation models need serially correlated exogenous shocks to account for the high serial correlation in inflation and output while the behavioral model produces serial correlation in these variables endogenously. I also show that inertia in the beliefs about the future is a strong force in producing the serial correlation in inflation and output.

Pairwise likelihood inference for the random effects probit model

This paper proposes a pairwise likelihood estimator based on an analytic approximation method for the random effects probit model. It is widely known that the standard approach for the random effects probit model relies on numerical integration and that its likelihood function does not have a closed form. When the number of time periods or the serial correlation across periods is large, the resulting estimator is likely to become biased. This study derives an analytic approximation for the likelihood function of one pair of time periods without relying on typical numerical-integral procedures. We then apply this formula in a pairwise likelihood estimation procedure to derive our estimator, which is obtained as the product of the analytic approximation of the likelihood function for all possible pairs of time periods. A simulation study is conducted for the comparison of our proposed estimator with the estimators for the pooled probit model and Gaussian quadrature procedure. The evidence shows that our proposed estimator enjoys desirable asymptotic properties. In addition, compared to the estimator based on the Gaussian quadrature procedure, our proposed estimator exhibits comparable performances in all the configurations considered in the simulation study and shows superiority for the cases of a large number of time periods and high serial correlation across periods. We apply our proposed estimator to British Household Panel Survey data so as to characterize the trend of working probabilities.

Stop-loss strategies with serial correlation, regime switching, and transaction costs

Stop-loss strategies are commonly used by investors to reduce their holdings in risky assets if prices or total wealth breach certain pre-specified thresholds. We derive closed-form expressions for the impact of stop-loss strategies on asset returns that are serially correlated, regime switching, and subject to transaction costs. When applied to a large sample of individual U.S. stocks, we show that tight stop-loss strategies tend to underperform the buy-and-hold policy in a mean-variance framework due to excessive trading costs. Outperformance is possible for stocks with sufficiently high serial correlation in returns. Certain strategies succeed at reducing downside risk, but not substantially.

Monetary Policy with Diverse Private Expectations

We study the impact of diverse beliefs on conduct of monetary policy. Individual belief is modeled by a state variable that defines an individual’s perceived laws of motion. We use a New Keynesian Model that is solved with a quadratic approximation hence individual decisions are quadratic functions. Aggregation renders the belief distribution an aggregate state variable. Although the model has standard technology and policy shocks, diverse expectations change materially standard results about a smooth trade-off between inflation volatility and output volatility. Our main results are summed up as follows: (i) The policy space contains a curve of singularity which is a collection of policy parameters that divides the space into two sub-regions. Some trade-off between output and inflation volatilities exists within each region and some across regions. (ii) The singularity causes volatility of variables to be non monotone in policy parameters. Policymakers cannot assume a more aggressive policy will change outcomes in a predictable manner. (iii) When beliefs are diverse a central bank must also consider the volatility of individual consumption and the related volatility of financial markets. We show aggressive anti-inflation policy increases consumption volatility and aggressive output stabilization policy entails rising inflation volatility. Efficient central bank policy must therefore be moderate. (iv) High optimism about the future typically lowers aggregate output and increases inflation. This “stagflation” effect is stronger the stickier prices are. Policy response is muted since the effects of higher inflation and lower output on interest rates partially cancel each other. Effective policy requires targeting exuberance directly or its effects in asset markets. Central banks already do so with short term interventions. (v) The observed high serial correlation of 0.80 in policy shocks contributes greatly to market volatility and we show that a reduction in persistence of central bank’s deviations from a fixed rule will contribute to stability. (vi) Belief dispersion is measured by cross sectional standard deviation of individual beliefs. An increased belief diversity is found to make policy coordination harder and results in lower aggregate output and lower rate of inflation. Bank policy can lower belief dispersion by being more transparent.

Short-term financial forecasting using ANN adaptive predictors in cascade

Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could be a proper experimental ground with such a goal. The present paper extends the previous authors’ research (Dobrescu et al., 2006; Nastac et al., 2007) on the same issue to improve the accuracy of exchange rate forecasting by using a set of neural predictors in cascade, instead of a single one. The results show that the presented model, despite its proved advantages, could be further improved in order to avoid the translation into residuals of the high serial correlation present in the primary database.

Current account dynamics and degree of capital mobility

By modelling the current account balances (as a percentage of GDP) in a dynamic AR(1) model, Taylor (2002) proposed to use speed of mean reversion of the dynamics of the current account as a tool for measurement of capital mobility and confirmed the stylized fact of U-Shape degree of capital mobility through the last two centuries with this new approach. With the assumption that countries obey their Long-Run Budget Constraint (LRBC), the OLS estimate of the degree of current account persistency in Taylor (2002) is downward bias due to small sample and high serial correlation of current account ratios with its lags. By correcting these biases with the Andrews (1993) exact median-unbiased estimation, we find that the confidence intervals for half-life estimates include 1 year (which was used as a benchmark) in 37 out of 75 country–period cases, which cast some doubt on the use of half-life estimate as a practical measure of capital mobility.

Assessing French inflation persistence with impulse saturation break tests and automatic general-to-specific modelling

This article has three different motivations. Firstly, we wish to contribute to the debate on whether French inflation has been persistent since the mid-eighties. Empirical evidence in this domain has been mixed. We use the standard method of testing for breaks in the mean of the inflation series to conclude whether possible unit root findings are the result of neglected breaks. Then, we build standard autoregressive representations of inflation, using an automatic general-to-specific approach. We conclude against inflation persistence in the sample period, and the point estimates of persistence we obtain are several percentage points below those achieved with other break tests and model selection methods. Moreover, our final model is congruent. Secondly, we provide the first empirical application of the new impulse saturation break test. The resulting estimates of the break dates are in line with other literature findings and have a sound economic meaning, confirming the good performance the test had revealed in theoretical and simulation studies. Finally, we also illustrate the shortcomings of the Bai–Perron test when applied to a small sample with high serial correlation. Indeed, we show the Bai–Perron break dates’ estimates would not allow us to build a congruent autoregressive representation of inflation.

Inflation and Monetary Regimes

Correlations of inflation with the growth rate of money increase when data are averaged over longer time periods. Correlations of inflation with the growth of money also are higher when high-inflation as well as low-inflation countries are included in the analysis. We show that serial correlation in the underlying inflation rate ties these two observations together and explains them. We present evidence that averaging increases the correlation of inflation and money growth more when the underlying inflation rate has higher serial correlation.

Inflation and monetary regimes

Correlations of inflation with the growth rate of money increase when data are averaged over longer time periods. Correlations of inflation with the growth of money also are higher when high inflation as well as low-inflation countries are included in the analysis. We show that serial correlation in the underlying inflation rate ties these two observations together and explains them. We present evidence that averaging increases the correlation of inflation and money growth in more when the underlying inflation rate has higher serial correlation.

Revisiting the Predictability of Bond Risk Premia

This paper investigates the source of predictability of bond risk premia by means of long-term forward interest rates. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. After a simple reparametrization of models used to predict spot rates or excess returns, we find that forward rates exhibit much less predictive power than previously recorded. Furthermore, our economic value analysis indicates that there are no economic gains to mean-variance investors who use the predictions of these models in a stylized dynamic asset allocation strategy.

International Portfolios and Current Account Dynamics: The Role of Capital Accumulation

Despite the liberalization of capital flows among OECD countries, equity home bias remains sizable, but is now less severe than in earlier decades; as a result, fluctuations in returns on foreign assets have a substantial effect on countries' wealth. As documented in this paper, changes in net foreign asset positions (NFA) are highly volatile, serially uncorrelated and countercyclical. We show that a two-country RBC model with productive investment and unrestricted international trade in stocks and bonds can explain these facts. Capital accumulation and shocks to the efficiency of investment are key ingredients for the success of the model. In contrast to related models that abstract from capital, equity home bias is not sensitive to preference parameters. In the model, NFA changes are largely driven by capital gains/losses due to movements in equity prices. The model thus matches the high volatility and low serial correlation of NFA changes. We compare settings with complete and incomplete financial markets. Imperfect hedging is required to generate a realistic conventional current account measure that solely reflects aggregate net flows of assets between countries.

An empirical analysis of milk addiction

We show the estimable rational addiction model tends to yield spurious evidence in favor of the rational addiction hypothesis when aggregate data are used. Direct application of the canonical model yields results seemingly indicative that non-addictive commodities such as milk, eggs, and oranges are rationally addictive. Monte Carlo simulation demonstrates that such results are likely to obtain whenever the commodity under scrutiny exhibits high serial correlation, or when even a small amount of the variation in prices is endogenous, or when overidentified instrumental variables estimators are used, or when commonly imposed restrictions are employed. We conclude that time-series data will often be insufficient to differentiate rational addiction from serial correlation in the consumption series.

An Analysis of Market Makers' Behavior on Nasdaq Europe

The paper empirically investigates market makers' behavior on Nasdaq Europe. The impact of market makers' competition and some other microstructure effects on spreads are analyzed. We perform a decomposition of the spread into its components in both Huang & Stoll (1997) and Lin, Sanger & booth (1995) settings. The main finding is the small adverse selection component on this rather thin market, together with a high serial correlation in the order flow. Although the spread decomposition does not succeed in identifying inventory costs, we show that market makers manage their inventories effectively. However, reversion to a desired inventory level appears to be slow, compared to what has been documented on more active markets.

An Empirical Analysis of Milk Addiction

We show the estimable rational addiction model tends to yield spurious evidence in favor of the rational addiction hypothesis when aggregate data is used. Direct application of the canonical model yields results seemingly indicative that non-addictive commodities such as milk, eggs, and oranges are rationally addictive. Monte Carlo simulation demonstrates that such results are likely to obtain whenever the commodity under scrutiny exhibits high serial correlation, or when even a small amount of the variation in prices is endogenous, or when overidentified instrumental variables estimators are used, or when commonly imposed restrictions are employed. We conclude that time series data will often be insufficient to differentiate rational addiction from serial correlation in the consumption series.

An Excursion into the Statistical Properties of Hedge Fund Returns

This paper provides an overview of the most important statistical properties of individual hedge fund returns. We find that the net-of-fees monthly returns of the average individual hedge fund exhibit significant degrees of negative skewness, excess kurtosis, as well as positive first-order serial correlation. The correlations between hedge funds in the same strategy group are of the same order of magnitude as the correlations between funds in different strategy groups and relatively low. Only 10-20% of the variation in the average individual hedge fund's returns can be explained by what happens in the US equity and bond markets. Compared to individual funds, portfolios of hedge funds tend to exhibit lower skewness, higher serial correlation and higher correlation with stocks and bonds. Movements in the US equity and bond markets still only explain 20-40% of the variation in hedge fund portfolios returns though. Finally, an equally-weighted portfolio of all funds in our sample offers a 2.76% higher mean return than the average fund of funds. This strongly suggests that the timing and fund picking activities of the average fund of funds are not rewarded by a higher return.

Estimating serial cross‐correlation in real estate returns

AbstractAppraisal smoothing understates the true volatility of real estate returns, and consequently affects asset allocation decisions. The high level of smoothing observed in commercial property index returns can be shown to be largely influenced by the effect of serial cross‐correlation. This paper examines this phenomenon by analysing the time series properties of a sample of individual property returns using the generalized autoregressive conditional heteroskedasticity (GARCH) model. The autoregressive conditional heteroskedasticity (ARCH) model, proposed by Engle (1982) and Bollerslev (1986), is used to characterize time series data that exhibits clustering in the residuals. The time‐varying conditional variance in ARCH models is often used as a measure of intertemporal risk. We use this property to analyse the cross‐correlation coefficients of a sample of properties. Our results suggest that there is positive skewness in the serial cross‐correlations. This is consistent with a process that would generate high serial correlation at the index level. We demonstrate that serial cross‐correlation can be used as a proxy of the proportion of sticky values in an index. The results indicate that serial cross‐correlation is time varying and positively skewed. Copyright © 2001 John Wiley & Sons, Ltd.