Empirical properties of asset returns: stylized facts and statistical issues

By Rama Cont. 2001

3,626 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

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Citations in the corpus

Conditional Time Series Forecasting with Convolutional Neural Networks (2017, background): “It is well known that while temporal relationships in the data exist, they are diﬃcult to analyze and predict accurately due to the non-linear trends, heavy tails and noise present in the series [6].”
Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “Empirical evidence on the universality of certain stylized facts (Cont 2001) and scaling relations (Mandelbrot et al. 1997, Benzaquen et al. 2016, Kyle and Obizhaeva 2016, Patzelt and Bouchaud 2017, Toth et al. 2017, Andersen et al. 2018) seems to support the universality hypothesis.”
An introduction to Lévy processes with applications in finance (2008, methodology): “The variance gamma and the normal inverse Gaussian process can be easily simulated because they are time-changed Brownian motions; we follow Cont and Tankov (2003) closely.”
Portfolio Selection : A Survey (2014, neutral): “[2008] proposed another piecewise universal portfolio selection strategy via context trees, and Kozat et al. [2011] generalized to sequential decision problem via tree weighting.”
Inhomogeneous Dependence Modeling with Time-Varying Copulae (2009, positive): “For a concise survey on stylized empirical facts from financial returns see Cont (2001) and Granger (2003). Modeling distributions with copulae has drawn attention from many researchers because it avoids the ‘‘procrustean bed’’ of normality assumptions, producing better fits of the empirical charact”
Econophysics: Empirical facts and agent-based models (2009, neutral): “One can find many different lists of these facts in several reviews (e.g. Bollerslev et al. (1994); Pagan (1996); Guillaume et al. (1997); Cont (2001)).”
Price Dynamics in a Markovian Limit Order Market (2011, methodology): “Summary We propose in this work a Markovian model of a limit order market, which captures some salient features of the dynamics of market orders and limit orders, yet is even simpler than the model of Cont et al. (2010b) and enables a wide range of properties of the price process to be computed anal”
On stock return prediction with LSTM networks (2017, background): “Referring to Rama Cont (2000) one can summarize a set of stylized facts regarding univariate return series, • No linear autocorrelation.”
Leverage causes fat tails and clustered volatility (2009, positive): “A typical value measured for financial time series, such as American stocks [7, 22], is γ ≈ 3.”
Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd, Elsevier, Amsterdam 2006, pp.1109-1186 (2005, background): “Agent type i’s demand for foreign currency is then given by dt = si,t+1 + ȳ − (1 + r)st 2αμi , (54)”
t-Statistic Based Correlation and Heterogeneity Robust Inference (2010, background): “the discussion in Loretan and Phillips (1994), Cont (2001) , Ibragimov (2004), Ibragimov”
Long range dependence in financial markets (2005, positive): “When testing for long range dependence in a model based on fractional Brownian motion, we thus test the joint hypothesis of self-similarity and long-range dependence and strict self-similarity is not observed to hold in asset returns [15, 16].”
The effectiveness of Keynes-Tobin transaction taxes when heterogeneous agents can trade in different markets: A behavioral finance approach (2006, background): “According to Cont (2001), Lux and Ausloos (2002) and Johansen and Sornette (2003), speculative markets are characterized by five universal features: (1) prices are distorted in the form of bubbles and crashes, (2) price volatility is higher than warranted by fundamentals, (3) the distribution of…”
Lambert W random variables—a new family of generalized skewed distributions with applications to risk estimation (2009, positive): “Asset returns, that is, the percentage change in price, typically exhibit negative skewness and positive excess kurtosis—so-called stylized facts [Yan (2005), Cont (2001)].”