C22 Time-Series Models; Dynamic Quantile Regressions (Updated!)
Refine
Year of publication
Document Type
- Working Paper (18)
- Article (1)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19)
Keywords
- Exponential smoothing (3)
- GARCH-Prozess (3)
- Zeitreihenanalyse (3)
- ARCH-Prozess (2)
- Börsenkurs (2)
- Deutschland (2)
- Schätzfunktion (2)
- Volatilität (2)
- joint inference (2)
- loss function (2)
This study uses Markov-switching models to evaluate the informational content of the term structure as a predictor of recessions in eight OECD countries. The empirical results suggest that for all countries the term spread is sensibly modelled as a two-state regime-switching process. Moreover, our simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions. The term structure is confirmed to be a reliable recession indicator. However, the results of probit estimations show that the markov-switching filter does not significantly improve the forecasting ability of the spread.
Extending the data set used in Beyer (2009) to 2017, we estimate I(1) and I(2) money demand models for euro area M3. After including two broken trends and a few dummies to account for shifts in the variables following the global financial crisis and the ECB's non-standard monetary policy measures, we find that the money demand and the real wealth relations identified in Beyer (2009) have remained remarkably stable throughout the extended sample period. Testing for price homogeneity in the I(2) model we find that the nominal-to-real transformation is not rejected for the money relation whereas the wealth relation cannot be expressed in real terms.
Recently, the Bank of Japan outlined a “two perspectives” approach to the conduct of monetary policy that focuses on risks to price stability over different time horizons. Interpreting this as pertaining to different frequency bands, we use band spectrum regression to study the determination of inflation in Japan. We find that inflation is related to money growth and real output growth at low frequencies and the output gap at higher frequencies. Moreover, this relationship reflects Granger causality from money growth and the output gap to inflation in the relevant frequency bands. Keywords: spectral regression, frequency domain, Phillips curve, quantity theory. JEL Numbers: C22, E3, E5
Forecasting stock market volatility and the informational efficiency of the DAX-index options market
(2002)
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.
This paper proposes tests for out-of-sample comparisons of interval forecasts based on parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage with respect to the set of conditioning variables from all models, for a given loss function. We propose a pairwise test to compare two models for a single predictive interval. The set-up is then extended to a comparison across multiple models and/or intervals. The limiting distribution varies depending on whether models are strictly non-nested or overlapping. In the latter case, degeneracy may occur. We establish the asymptotic validity of wild bootstrap based critical values across all cases. An empirical application to Growth-at-Risk (GaR) uncovers situations in which a richer set of financial indicators are found to outperform a commonly-used benchmark model when predicting downside risk to economic activity.
We build a novel leading indicator (LI) for the EU industrial production (IP). Differently from previous studies, the technique developed in this paper is able to produce an ex-ante LI that is immune to “overlapping information drawbacks”. In addition, the set of variables composing the LI relies on a dynamic and systematic criterion. This ensures that the choice of the variables is not driven by subjective views. Our LI anticipates swings (including the 2007-2008 crisis) in the EU industrial production – on average – by 2 to 3 months. The predictive power improves if the indicator is revised every five or ten years. In a forward-looking framework, via a general-to-specific procedure, we also show that our LI represents the most informative variable in approaching expectations on the EU IP growth.
Wir verwenden eine neue, auf der Burr-Verteilung basierende Spezifikation aus der Familie der Autoregressive Conditional Duration (ACD) Modelle zur ökonometrischen Analyse der Transaktionsintensitäten während der Börseneinführung (IPO) der Deutsche Telekom Aktie. In diesem Fallbeispiel wird die Leistungsfähigkeit des neu entwickelten Burr-ACD-Modells mit den Standardmodellen von Engle und Russell verglichen, die im Burr-ACD Modell als Spezialfälle enthalten sind. Wir diskutieren außerdem alternative Möglichkeiten, Intra- Tagessaisonalitäten der Handelsintensität in ACD Modellen zu berücksichtigen.
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.
Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
(2011)
We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions.
This paper provides theory as well as empirical results for pre-averaging estimators of the daily quadratic variation of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit theorems and an explicit test on serial dependence in microstructure noise. Using transaction data of different stocks traded at the NYSE, we analyze the estimators’ sensitivity to the choice of the pre-averaging bandwidth and suggest an optimal interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we provide guidance for optimal implementation of pre-averaging estimators and discuss potential pitfalls in practice. Quadratic Variation , MarketMicrostructure Noise , Pre-averaging , Sampling Schemes , Jumps