nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒02‒05
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Volatility Spillovers among Global Stock Markets: Measuring Total and Directional Effects By Santiago Gamba-Santamaria; Jose Eduardo Gomez-Gonzalez; Jorge Luis Hurtado-Guarin; Luis Fernando Melo-Velandia
  2. Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models Using MIDAS Regressions and ARCH Models By Patrick Gagliardini; Eric Ghysels; Mirco Rubin
  3. Semiparametric Estimation of Multivariate GARCH Models By Claudio Morana
  4. Mixed-Frequency Macro-Financial Spillovers By John Cotter; Mark Hallam; Kamil Yilmaz
  5. Record statistics of a strongly correlated time series: random walks and L\'evy flights By Claude Godreche; Satya N. Majumdar; Gregory Schehr
  6. A Simple Mechanism for Financial Bubbles: Time-Varying Momentum Horizon By Li Lin; Didier Sornette

  1. By: Santiago Gamba-Santamaria (Banco de la República de Colombia); Jose Eduardo Gomez-Gonzalez (Banco de la República de Colombia); Jorge Luis Hurtado-Guarin (Banco de la República de Colombia); Luis Fernando Melo-Velandia (Banco de la República de Colombia)
    Abstract: In this study we construct volatility spillover indexes for some of the major stock market indexes in the world. We use a DCC-GARCH framework for modelling the multivariate relationships of volatility among markets. Extending the framework of Diebold and Yilmaz [2012] we compute spillover indexes directly from the series of returns considering the time-variant structure of their covariance matrices. Our spillover indexes use daily stock market data of Australia, Canada, China, Germany, Japan, the United Kingdom, and the United States, for the period January 2001 to August 2016. We obtain several relevant results. First, total spillovers exhibit substantial time-series variation, being higher in moments of market turbulence. Second, the net position of each country (transmitter or receiver) does not change during the sample period. However, their intensities exhibit important time-variation. Finally, transmission originates in the most developed markets, as expected. Of special relevance, even though the Chinese stock market has grown importantly over time, it is still a net receiver of volatility spillovers. Classification JEL: G01; G15; C32
    Keywords: Volatility spillovers; DCC-GARCH model; Global stock market linkages; financial crisis
    Date: 2017–01
  2. By: Patrick Gagliardini (University of Lugano and Swiss Finance Institute); Eric Ghysels (University of North Carolina Kenan-Flagler Business School, University of North Carolina (UNC)); Mirco Rubin (University of Bristol)
    Abstract: We examine the relationship between MIDAS regressions and the estimation of state space models applied to mixed frequency data. While in some cases the binding function is known, in general it is not, and therefore indirect inference is called for. The approach is appealing when we consider state space models which feature stochastic volatility, or other non-Gaussian and nonlinear settings where maximum likelihood methods require computationally demanding approximate filters. The stochastic volatility feature is particularly relevant when considering high frequency financial series. In addition, we propose a filtering scheme which relies on a combination of re-projection methods and now-casting MIDAS regressions with ARCH models. We assess the efficiency of our indirect inference estimator for the stochastic volatility model by comparing it with the Maximum Likelihood (ML) estimator in Monte Carlo simulation experiments. The ML estimate is computed with a simulation-based Expectation-Maximization (EM) algorithm, in which the smoothing distribution required in the E step is obtained via a particle forward-filtering/backward-smoothing algorithm. Our Monte Carlo simulations show that the Indirect Inference procedure is very appealing, as its statistical accuracy is close to that of MLE but the former procedure has clear advantages in terms of computational efficiency. An application to forecasting quarterly GDP growth in the Euro area with monthly macroeconomic indicators illustrates the usefulness of our procedure in empirical analysis.
    Keywords: Indirect inference, MIDAS regressions, State space model, Stochastic volatility, GDP forecasting.
    Date: 2016–07
  3. By: Claudio Morana (Department of Economics, Management and Statistics, University of Milan-Bicocca, Italy; Center for Research on Pensions and Welfare Policies, Collegio Carlo Alberto, Italy; The Rimini Centre for Economic Analysis, Italy)
    Abstract: The paper introduces a new simple semiparametric estimator of the conditional variance covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step; the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-simultaneous case, i.e., for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple ex-post procedure, to ensure well behaved conditional covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.
    Keywords: Multivariate GARCH model, dynamic conditional correlation, semiparametric estimation
    Date: 2017–01
  4. By: John Cotter (University College Dublin); Mark Hallam (University of Essex); Kamil Yilmaz (Koc University)
    Abstract: We develop a new methodology to analyse spillovers between the real and financial sides of the economy that employs a mixed-frequency modelling approach. This enables high-frequency financial and low-frequency macroeconomic data series to be employed directly, avoiding the data aggregation and information loss incurred when using common-frequency methods. In a detailed analysis of macro- financial spillovers for the US economy, we find that the additional high-frequency information preserved by our mixed-frequency approach results in estimated spillovers that are typically substantially higher than those from an analogous common-frequency approach and are more consistent with known in-sample events. We also show that financial markets are typically net transmitters of shocks to the real side of the economy, particularly during turbulent market conditions, but that the bond and equity markets act heterogeneously in both transmitting and receiving shocks to the non- financial sector. We observe substantial short and medium-run variation in macro- financial spillovers that is statistically associated with key variables related to financial and macroeconomic fundamentals; the values of the term spread, VIX and unemployment rate in particular appear to be important determinants of macro-financial spillovers.
    Keywords: spillovers, connectedness, macro- nancial, mixed-frequency
    Date: 2017–02
  5. By: Claude Godreche; Satya N. Majumdar; Gregory Schehr
    Abstract: We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a L\'evy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few years, it was indeed realized that random walks are a very useful "laboratory" to test the effects of correlations on the record statistics. We start with the simple one-dimensional random walk with symmetric jumps (both continuous and discrete) and discuss in detail the statistics of the number of records, as well as of the ages of the records, i.e., the lapses of time between two successive record breaking events. Then we review the results that were obtained for a wide variety of random walk models, including random walks with a linear drift, continuous time random walks, constrained random walks (like the random walk bridge) and the case of multiple independent random walkers. Finally, we discuss further observables related to records, like the record increments, as well as some questions raised by physical applications of record statistics, like the effects of measurement error and noise.
    Date: 2017–02
  6. By: Li Lin (East China University of Science and Technology (ECUST)); Didier Sornette (ETH Zurich and Swiss Finance Institute)
    Abstract: Building on the notion that bubbles are transient self-fulfilling prophecies created by positive feedback mechanisms, we construct the simplest continuous price process whose expected returns and volatility are functions of momentum only. The momentum itself is measured by a simple continuous moving average of past prices over a given time horizon. We introduce a simple dynamics of the time horizon used by the representative investor, which is motivated by the race of trend-following agents to forerun their competitors. Moreover, we make explicit the price processes that exclude risk-free arbitrage opportunities but allow for momentum trading strategies with time-varying horizons. The model consists finally in one specification for the non-bubble regime and a second specification for the bubble regime, with the transition from one to the other controlled by the crossing of a momentum threshold. The proposed price generating process generates the main stylized facts of empirical financial time series. Moreover, it produces realistic regime shifts between non-bubble and bubble regimes. We construct a quasi-likelihood methodology to calibrate the model to empirical financial time series, which is applied to eight empirical historical cases that exhibit large volatility bursts and are candidates for the presence of bubbles. The calibration supports the relevance of the proposed model to represent a significant component of historical bubble regimes.
    Keywords: Financial Bubbles, Momentum, Positive Feedback, Time-Horizon, Quasi-Likelihood, Regime Shifts
    JEL: C52 G01 G17

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