160 research outputs found
A Minimal Model of Financial Stylized Facts
In this work we afford the statistical characterization of a linear Stochastic Volatility Model featuring Inverse Gamma stationary distribution for the high frequency volatility. We detail the derivation of the moments of the return distribution, revealing the role of the Inverse Gamma law in the emergence of fat tails, and of the relevant correlation functions. We also propose a systematic methodology for estimating the parameters, and we describe the empirical analysis of the Standard & Poor 500 index daily returns, confirming the ability of the model to capture many of the established stylized fact as well as the scaling properties of empirical distributions over different time horizons.
The adaptive nature of liquidity taking in limit order books
In financial markets, the order flow, defined as the process assuming value
one for buy market orders and minus one for sell market orders, displays a very
slowly decaying autocorrelation function. Since orders impact prices,
reconciling the persistence of the order flow with market efficiency is a
subtle issue. A possible solution is provided by asymmetric liquidity, which
states that the impact of a buy or sell order is inversely related to the
probability of its occurrence. We empirically find that when the order flow
predictability increases in one direction, the liquidity in the opposite side
decreases, but the probability that a trade moves the price decreases
significantly. While the last mechanism is able to counterbalance the
persistence of order flow and restore efficiency and diffusivity, the first
acts in opposite direction. We introduce a statistical order book model where
the persistence of the order flow is mitigated by adjusting the market order
volume to the predictability of the order flow. The model reproduces the
diffusive behaviour of prices at all time scales without fine-tuning the values
of parameters, as well as the behaviour of most order book quantities as a
function of the local predictability of order flow.Comment: 40 pages, 14 figures, and 2 tables; old figure 12 removed. Accepted
for publication on JSTA
Filtering and Smoothing with Score-Driven Models
We propose a methodology for filtering, smoothing and assessing parameter and
filtering uncertainty in misspecified score-driven models. Our technique is
based on a general representation of the well-known Kalman filter and smoother
recursions for linear Gaussian models in terms of the score of the conditional
log-likelihood. We prove that, when data are generated by a nonlinear
non-Gaussian state-space model, the proposed methodology results from a
first-order expansion of the true observation density around the optimal
filter. The error made by such approximation is assessed analytically. As shown
in extensive Monte Carlo analyses, our methodology performs very similarly to
exact simulation-based methods, while remaining computationally extremely
simple. We illustrate empirically the advantages in employing score-driven
models as misspecified filters rather than purely predictive processes.Comment: 33 pages, 5 figures, 6 table
A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics
The analysis of the intraday dynamics of correlations among high-frequency
returns is challenging due to the presence of asynchronous trading and market
microstructure noise. Both effects may lead to significant data reduction and
may severely underestimate correlations if traditional methods for
low-frequency data are employed. We propose to model intraday log-prices
through a multivariate local-level model with score-driven covariance matrices
and to treat asynchronicity as a missing value problem. The main advantages of
this approach are: (i) all available data are used when filtering correlations,
(ii) market microstructure noise is taken into account, (iii) estimation is
performed through standard maximum likelihood methods. Our empirical analysis,
performed on 1-second NYSE data, shows that opening hours are dominated by
idiosyncratic risk and that a market factor progressively emerges in the second
part of the day. The method can be used as a nowcasting tool for high-frequency
data, allowing to study the real-time response of covariances to macro-news
announcements and to build intraday portfolios with very short optimization
horizons.Comment: 30 pages, 10 figures, 7 table
A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics
The digitalization of news and social media provides an unprecedented source to investigate the role of information on market dynamics. However, the observed sentiment time-series rep- resent a noisy proxy of the true investor sentiment. Moreover, modeling the joint dynamics of different sentiment series can be beneficial for the assessment of their economic relevance. The main methodological contribution of this paper is twofold: i) we filter the latent sentiment sig- nals in a genuinely multivariate model; ii) we propose a decomposition into a long-term random walk component, named long-term sentiment, and a short-term component driven by a station- ary Vector Autoregressive process of order one, named short-term sentiment. The proposed framework is a dynamic factor model describing the joint evolution of the observed sentiments of a portfolio of assets. Empirically, we find that the long-term sentiment co-integrates with the market price factor, while the short-term sentiment captures transient and firm-specific swings. By means of quantile regressions, we assess the significance of the explanatory power of filtered present sentiment on future returns. Then, we demonstrate how the lagged relation can be successfully exploited in a portfolio allocation exercise
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