Short-run Component Research Articles

Doubly Multiplicative Error Models with Long– and Short–run Components

We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low–, respectively, high–frequency features in the data. We derive the theoretical properties of the Maximum Likelihood and Generalized Method of Moments estimators. Two such models are then proposed, the Component-MEM, which uses daily data for both components, and the MEM-MIDAS, which exploits the logic of MIxed–DAta Sampling (MIDAS). The empirical application involves the S&P 500, NASDAQ, FTSE 100 and Hang Seng indices: irrespective of the market, both DMEM’s outperform the HAR and other relevant GARCH–type models.

The Association Between Bank Credit Terms and Conditions and the Business Cycle in the Euro Area

Abstract We explored the trajectory of bank loan terms and conditions over the business cycle, where the latter was decomposed into its long-run (trend) and short-run (cyclical) components. We found that deterioration of each business cycle component leads to a significant tightening of credit terms and conditions. We found mixed results concerning the symmetry of impacts of the short and long run components. Symmetry was found between the terms and conditions on loans for small vs. large enterprises. Our findings provide very useful information to policy makers and should be taken into consideration when monetary policies are designed.

Forecasting volatility with component conditional autoregressive range model

In this paper, we propose a component conditional autoregressive range (CCARR) model for forecasting volatility. The proposed CCARR model assumes that the price range comprises both a long-run (trend) component and a short-run (transitory) component, which has the capacity to capture the long memory property of volatility. The model is intuitive and convenient to implement by using the maximum likelihood estimation method. Empirical analysis using six stock market indices highlights the value of incorporating a second component into range (volatility) modelling and forecasting. In particular, we find that the proposed CCARR model fits the data better than the CARR model, and that it generates more accurate out-of-sample volatility forecasts and contains more information content about the true volatility than the popular GARCH, component GARCH and CARR models.

A Mixed Frequency Stochastic Volatility Model for Intraday Stock Market Returns

Abstract We propose a mixed frequency stochastic volatility model for intraday returns. To account for long-memory type of dependence patterns we introduce a long-run component that changes daily and a short-run component that captures the remaining intraday volatility dynamics. We analyze the model’s stochastic properties and extend it to capture leverage effects and overnight return information. The model is estimated by simulated maximum likelihood using efficient importance sampling. We apply the model to 30-min returns of 12 stocks. The results show that the model successfully accounts for the complex dynamic and distributional properties of asset returns both on the intraday and the daily frequency.

Behavior of volatility persistence in 10-year sovereign bond yields of India and China: evidence from component-GARCH model of Engle and Lee (1999)

This paper investigates the volatility persistence in sovereign bond yields of India and China during study period of 2010–2018. For that purpose, the researcher has applied the Engle and Lee (in: Engle and Lee (eds) Cointegration, causality, and forecasting: a Festschrift in honour of Clive WJ Granger, Oxford University Press, Oxford, pp 475–497, 1999) C-GARCH model to decompose the volatility of 10-year sovereign bond yields of India and China into permanent and transitory components. The results reveal that permanent conditional volatility shows long memory with long-run component’s half-life decay ranges from 91 to 97 days for India and China, respectively. However, the temporary component of volatility much smaller with short-run component’s half-life decay ranges from .70 to .75 for India and China, respectively.

Gas pipeline infrastructure and economic growth of Russian regions: panel cointegration analysis

The Russian gas supply system is one of the largest infrastructures in the world. It is continuing to develop at a high rate. Modern methodological approaches and software allow to investigate the relationship between the development of energy infrastructure and economic dynamics of the regions. This paper proposes a methodological approach to the evaluation of the relationship “development of gas distribution systems – economic growth”. The evaluation is based on the concept of cointegration and an error-correction models. This approach identifies both long-run and short-run components of this relationship. In this paper we aim to estimate the relationship using annual data of 17 Central Russia regions for the period 1998-2017. The results indicate that this relationship is statistically significant. It allows to apply proposed approach and the obtained estimates to the problems of analysis and forecasting of the domestic gas market.

CAPM, components of beta and the cross section of expected returns

Abstract This paper demonstrates that a conditional version of the Capital Asset Pricing Model (CAPM) explains the cross section of expected returns, just as well as the three factor model of Fama and French. This is achieved by measuring beta (systematic risk) with short-, medium- and long-run components. The short-run component of beta is computed from daily returns over the prior year, while the medium-run beta component is from daily returns over the prior 5 years, and the long-run component from monthly returns over the prior 10 years.

Testing for Bubbles in Cryptocurrencies with Time-Varying Volatility

The recent evolution of cryptocurrencies has been characterized by bubble-like behavior and extreme volatility. While it is difficult to assess an intrinsic value to a specific cryptocurrency, one can employ recently proposed bubble tests that rely on recursive applications of classical unit root tests. This paper extends this approach to the case where volatility is time varying, assuming a deterministic long-run component that may take into account a decrease of unconditional volatility when the cryptocurrency matures with a higher market dissemination. Volatility also includes a stochastic short-run component to capture volatility clustering. The wild bootstrap is shown to correctly adjust the size properties of the bubble test, which retains good power properties. In an empirical application using eleven of the largest cryptocurrencies and the CRIX index, the general evidence in favor of bubbles is confirmed, but much less pronounced than under constant volatility.

Investor sentiment and the cross-section of stock returns: new theory and evidence

We extend the noise trader risk model of Delong et al. (J Polit Econ 98:703–738, 1990) to a model with multiple risky assets to demonstrate the effect of investor sentiment on the cross-section of stock returns. Our model formally demonstrates that market-wide sentiment leads to relatively higher contemporaneous returns and lower subsequent returns for stocks that are more prone to sentiment and difficult to arbitrage. Our extended model is consistent with the existing empirical evidence on the relationship between sentiment and cross-sectional stock returns. Guided by the extended model, wen also decompose investor sentiment into long- and short-run components and predict that long-run sentiment negatively associates with the cross-sectional return and short-run sentiment positively varies with the cross-sectional return. Consistent with these predictions, we find a negative relationship between the long-run sentiment component and subsequent stock returns and positive association between the short-run sentiment component and contemporaneous stock returns.

Testing for misspecification in the short-run component of GARCH-type models

Abstract In this article, a misspecification test in conditional volatility and GARCH-type models is presented. We propose a Lagrange Multiplier type test based on a Taylor expansion to distinguish between (G)ARCH models and unknown GARCH-type models. This new test can be seen as a general misspecification test of a large set of GARCH-type univariate models. It focuses on the short-term component of the volatility. We investigate the size and the power of this test through Monte Carlo experiments and we compare it to two other standard Lagrange Multiplier tests, which are more restrictive. We show the usefulness of our test with an illustrative empirical example based on daily exchange rate returns.

Macroeconomic determinants of the term structure: Long-run and short-run dynamics

Abstract We propose a no-arbitrage term structure model with a Taylor rule and two macroeconomic variables, real activity growth and inflation, that each contain long-run and short-run components. Variance decompositions indicate that the impact of macroeconomic variables on the term structure differs from existing models. For short maturities, inflation is relatively more important than real activity growth at short forecast horizons. For longer maturity yields, the long-run component of inflation explains most of the long-horizon forecast variance, but real activity growth matters for short forecast horizons. Unlike existing macro models, the model implies plausible term premia and expectations of short rates. The long-run components also improve the prediction of bond excess returns relative to information in the yield curve and macro variables. Measures of in-sample and out-of-sample fit confirm the benefits of allowing for long- and short-run components.

Financial market Volatility, macroeconomic fundamentals and investor Sentiment

Abstract In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long run component and a transitory short run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications. Out-of-sample forecasting shows that the components of volatility can improve forecasts of macroeconomic fundamentals, and vice versa.

Short and Long Effects of Productivity on Unemployment

Okun’s (1962) seminal paper is an important study on economic growth and unemployment but a detailed exploration of the effect of productivity growth on unemployment has been left aside. Yet, the relationship between productivity growth and unemployment has been debated since long. In this paper we present stylized facts on the link between productivity growth and unemployment for the short and long run, and present model variants that demonstrate that in the short run productivity growth may increase unemployment while in the long run the relationship between productivity growth and unemployment are likely to co-vary negatively. Using US data, empirically we decompose the time series of unemployment rate and productivity growth into long run and short run components and show empirical evidence that the long run and short run components co-vary as predicted by some theories.

Testing for Bubbles in Cryptocurrencies with Time-Varying Volatility

The recent evolution of cryptocurrencies has been characterized by bubble-like behavior and extreme volatility. While it is difficult to assess an intrinsic value to a specific cryptocurrency, one can employ recently proposed bubble tests that rely on recursive applications of classical unit root tests. This paper extends this approach to the case where volatility is time varying, assuming a deterministic long-run component that may take into account a decrease of unconditional volatility when the cryptocurrency matures with a higher market dissemination. Volatility also includes a stochastic short-run component to capture volatility clustering. The wild bootstrap is shown to correctly adjust the size properties of the bubble test, which retains good power properties. In an empirical application using eleven of the largest cryptocurrencies and the CRIX index, the general evidence in favor of bubbles is confirmed, but much less pronounced than under constant volatility.

Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing

We propose a new model that estimates the long- and short-run components of the variances and covariances. The advantage of our model to the existing DCC-based models is that it uses the same form for both the variances and covariances and that it estimates these moments simultaneously. We apply this model to obtain long- and short-run factor betas for industry test portfolios, where the risk factors are the market, SMB, and HML portfolios. We use these betas in cross-sectional analysis of the risk premia. Among other things, we find that the risk premium related to the short-run market beta is significantly positive, irrespective of the choice of test portfolio. Further, the risk premia for the short-run betas of all the risk factors are significant outside recessions.

A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets

Understanding and quantifying the dependence of returns and liquidity is critical for liquidity risk management. In this paper the idea of mixed data sampling (MIDAS) is extended from linear correlation in Colacito et al. (2011) to the more general dependence measure: copula, and a copula-MIDAS model is proposed to describe the asymmetric return-liquidity dependence of CSI 300 index futures with short-run and long-run components. Based on the skewed t copula-MIDAS model, it is found that extreme decreases in returns tend to be accompanied by extreme increases in bid-ask spreads, but extreme increases in returns may not coincide with extreme reductions in bid-ask spreads. Furthermore, the return-spread dependence consists of both short-run and long-run components, and the long-run component will influence the return-spread dependence in the next two weeks. Last, the out-of-sample forecast of liquidity risk stresses the importance of considering asymmetry and long-run trend in return-spread dependence as it enables investors to well predict liquidity risk in times of market crashes. The results imply that high frequency trading investors of CSI 300 index futures should pay more attentions to prevent the potential liquidity risk when the bid-ask spreads are widened. And investors are suggested to use the past two-week high frequency data to forecast the current return-spread dependence in liquidity risk management.

Decomposing the Size Premium

We decompose firm size into four components: the lagged 5-year component that represents size five years ago, and the long-run, intermediate-run, and short-run components that capture changes in size in each horizon. Our analyses indicate that while the lagged 5-year component explains about 80% of the cross-sectional variation in size, it has little return predictability. In contrast, the long-run change in size component explains only 18% of size, but it completely captures the size premium. Our decomposition also sheds light on the January effect, the disappearance of the size premium since early 1980s, and the return behaviors of new entrants.

Short and Long-Run Effects of Productivity on Unemployment

Okun’s (1962) seminal paper is an important study on economic growth and unemployment but a detailed exploration of the effect of productivity growth on unemployment has been left aside. Yet, the relationship between productivity growth and unemployment has been debated since long. In this paper we present stylized facts on the link between productivity growth and unemployment for the short and long run, and present model variants that demonstrate that in the short run productivity growth can have a negative effect on employment–or may even increase unemployment – while in the long run the relationship between productivity growth and employment are likely to co-vary positively. Using US data, empirically we decompose the time series of unemployment rate and productivity growth into long run and short run components and show empirical evidence that the long run and short run components co-vary as predicted by some theories.

A Mixed Frequency Stochastic Volatility Model for Intraday Stock Market Returns

We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long-run component that changes at daily frequency and a short-run component that captures the remaining intraday volatility dynamics. An additional component captures deterministic intraday patterns. We analyze the stochastic properties of the resulting non-linear state-space model both on the daily and the intraday frequency and show how the model can be estimated in a single step using simulated maximum likelihood based on Efficient Importance Sampling (EIS). We apply the model to intraday returns of five New York Stock Exchange traded stocks. The estimation results indicate distinct dynamic patterns for daily and intradaily volatility components, where about 50% of intraday volatility dynamics are explained by the daily component. In-sample diagnostic tests and an out-of-sample forecasting experiment indicate that already the very basic model specification successfully accounts for the complex dynamic and distributional properties of asset returns both on the intraday and the daily frequency.

Financial Market Volatility, Macroeconomic Fundamentals and Investor Sentiment

In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long-run component and a transitory short-run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications.