Empirically based modeling in financial economics and beyond, and spurious stylized facts

The discovery of the dynamics of a time series requires construction of the transition density. We explain why 1-point densities and scaling exponents cannot determine the class of stochastic dynamics. Time series require some sort of underlying statistical regularity to provide a basis for analysis, and we construct an exhaustive list of such tests. The condition for stationary increments, not scaling, determines the existence of long time pair autocorrelations. We conjecture that for a selfsimilar process neither the pair correlations 〈x(t)x(s)〉 nor the 2-point density scales in both times t and s except in a pathological case, and give examples using three well-known Gaussian processes. An incorrect assumption of stationary increments can generate spurious stylized facts, including fat tails. When a sliding window is applied to nonstationary, uncorrelated increments then a Hurst exponent H_s = 1 / 2 is generated by that procedure even if the underlying model scales with a Hurst exponent H ≠ 1/2. We explain how this occurs dynamically. The nonstationarity arises from systematic unevenness in the traders' behavior in real time. Spurious stylized facts arise mathematically from using a log increment with a ‘sliding window’ to read the series. In addition, we show that nonstationary processes are generally not globally transformable to stationary ones. We also present a more detailed explanation of our recent FX data analysis and modeling.     

Universal price impact functions of individual trades in an order-driven market

The trade size ω has a direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization C is included as an argument in the scaling relation. However, the rationale of introducing stock capitalization in the scaling is unclear and the anomalous negative correlation between price change r and trade size ω for small trades is unexplained. Here we show that these issues can be addressed by taking into account the aggressiveness of orders that result in trades together with a proper normalization technique. Using order book data from the Chinese market, we show that trades from filled and partially filled limit orders have very different price impacts. The price impact of trades from partially filled orders is constant when the volume is not too large, while that of filled orders shows power-law behavior r?～?ω^α with α?≈?2/3. When returns and volumes are normalized by stock-dependent averages, capitalization-independent scaling laws emerge for both types of trades. However, no scaling relation in terms of stock capitalization can be constructed. In addition, the relation α?=?α_ω/α _r is verified for some individual stocks and for the whole data set containing all stocks using partially filled trades, where α_ω and α _r are the tail exponents of trade sizes and returns. These observations also enable us to explain the anomalous negative correlation between r and ω for small-size trades.     

Forecasting market risk using ultra-high-frequency data and scaling laws

This paper develops a new multiple time scale-based empirical framework for market risk estimates and forecasts. Ultra-high frequency data are used in the empirical analysis to estimate the parameters of empirical scaling laws which gives a better understanding of the dynamic nature of the market. A comparison of the new approach with the popular Value-at-Risk and expected tail loss measures with respect to their risk forecasts during the crisis period in 2008 is presented. The empirical results show the outperformance of the new scaling law method which turns out to be more accurate and flexible due to the scale invariance. The scaling law method promotes the use of massive real data in developing risk measurement and forecasting models.     

货物贸易改革中的宏观审慎管理逻辑

本文利用2010-2013年月度时间序列数据,对外汇占款与货物贸易代客结售汇的关系进行了实证分析,认为货物贸易代客结售汇顺差是形成外汇占款的主要原因。在此基础上,结合货物贸易外汇管理改革,对改革中的宏观审慎管理逻辑和工具进行了分析,发现货物贸易宏观审慎管理工具能够有效地提升国内流动性管理水平。最后,今文对完善货物贸易宏观审慎管理提出了政策建议。     

内在投资价值理论在中国股市的有效性检验

用F-O模型对深发展1991～2010年的内在价值进行测算,然后实证分析股价和内在价值之间的协整关系,发现在过去的20年中,两变量间并不存在协整关系。这说明中国股市是无效市场。通过计算泡沫值发现,20世纪90年代,深发展的泡沫值非常高,进入21世纪后泡沫值逐渐降低,反映出中国股市逐渐走向价值回归之路。     

Disentangling and quantifying market participant volatility contributions

Thanks to the access to labeled orders on the CAC 40 index future provided by Euronext, we are able to quantify market participants contributions to the volatility in the diffusive limit. To achieve this result, we leverage the branching properties of Hawkes point processes. We find that fast intermediaries (e.g. market maker type agents) have a smaller footprint on the volatility than slower, directional agents. The branching structure of Hawkes processes allows us to examine also the degree of endogeneity of each agent behavior, and we find that high-frequency traders are more endogenously driven than other types of agents.     

Prediction accuracy and sloppiness of log-periodic functions

We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of the crash belongs to the latter category. We discuss in detail why and how the fitting procedure must take into account the sloppy nature of this kind of model. We then test the reliability of LPPLs on synthetic AR(1) data replicating the Hang Seng 1987 crash and show that even this case is borderline regarding the predictability of the divergence time. We finally argue that current methods used to estimate a probabilistic time window for the divergence time are likely to be over-optimistic.     

Investor behaviour and contagion

This paper examines two open questions in international finance. First, what is the relative importance of different linkages in causing global financial crises? The second question concerns whether or not investor behaviour affects contagion. I use a two-market agent-based model that incorporates insights from behavioural finance to answer these open questions. Simulated managers only affect contagion when they control a small proportion of the total assets in each market. This evidence may partly explain the Russian contagion to Brazil where direct linkages did not exist. In addition, results show that global managers must make up between 40 and 50% of both local markets in order to become a more important linkage than that of international trade.     

Did COVID-19 change spillover patterns between Fintech and other asset classes?

This study examines the spillover effect between financial technology (Fintech) stocks and other financial assets (gold, Bitcoin, a global equity index, crude oil, and the US Dollar) during the COVID-19 crisis. Employing daily data from June 2019 to August 2020, our empirical analysis shows that the outbreak of COVID-19 exacerbated volatility transmission across asset classes, while subsequent decreases in new confirmed cases globally reduced the intensity of these spillovers. The evidence for the USD and gold supports their safe haven properties during catastrophic events, while innovative technology products as represented by a financial technology index (KFTX) and Bitcoin were highly susceptible to external shocks. These results show that when push comes to shove, the buck stops with the USD and gold and that the exorbitant privilege enjoyed by the USD prevailed during the COVID-19 pandemic.     

Modifying a simple agent-based model to disentangle the microstructure of Chinese and US stock markets

We modify a simple agent-based model (ABM) proposed by Franke and Westerhoff [J. Econ. Dyn. Control, 2012, 36(8), 1193–1211] through considering the price limits and the motion of the fundamental value. The method of simulated moments is applied to calibrate both initial and modified ABMs with CSI 300 and S&P 500 respectively, and the goodness-of-fit of each ABMs is tested. The calibration results indicate that the modified model performs better than initial one. Then, we utilize the GSL-div, proposed by Lamperti [Econometrics Stat, 2018, 5, 83–106.], to verify the explanatory power of ABMs. In this procedure, 13 ARCH family models are introduced as benchmarks. The result shows that the explanatory power of modified ABM exceeds ARCH models in both markets, while initial ABM may be defeated by some of the ARCH family models in explaining the microstructure of CSI 300. Finally, a heuristic algorithm is designed to disentangle the insights of Chinese and US stock markets to the observed time horizon through calibrating the initial fundamental value, and Kupiec test is used to check the robustness of the calibration. The result indicates that the explanation of modified model is robust in both markets, while initial model lost its robustness when explaining S&P 500.     

网络企业——新经济形式下的价值评定

新经济形式下的网络产业有其独特的价值源泉，因此网络企业的价值评定关键在于将网络经济特有的价值指标（如注册用户数，点击率等）融入传统的价值评价体系，再根据网络产业自身的发展特点提出企业的增长模型，从而为企业价值的定量分析提供计量基础。本试从行业的盈利预测分析入手提出企业的增长模型，并以此为基础结合调整后的盈利评价指标，对网络企业内在价值作出定量分析。     

Paramater estimation bias and volatility scaling in Black-Scholes option prices

When asset returns conform to a Gaussian distribution, the moments of the distribution over long return intervals may be estimated by scaling the moments of shorter return intervals. While it is well known that asset returns are not normally distributed, a key empirical question concerns the effect that scaling the volatility of dependent processes will have on the pricing of related financial assets. This study investigates the return properties of the most important currencies traded in spot markets against the U.S. dollar: the Japanese yen, the British pound, and the Swiss franc during the period November 1983 to April 2004. The novelty of this paper is that the volatility properties of the series are tested utilising statistical procedures developed from fractal geometry, with the economic impact determined within an option-pricing framework.     

An agent-based model of corporate bond trading

We construct an heterogeneous agent-based model of the corporate bond market and calibrate it against US data. The model includes the interactions between a market maker, three types of fund, and cash investors. In general, the sensitivity of the market maker to demand and the degree to which momentum traders are active strongly influence the over- and under-shooting of yields in response to shocks, while investor behaviour plays a comparatively smaller role. Using the model, we simulate experiments of relevance to two topical issues in this market. Firstly, we show that measures to reduce the speed with which investors can redeem investments can reduce the extent of yield dislocation. Secondly, we find the unexpected result that a larger fraction of funds using passive investment strategies increases the tail risk of large yield dislocations after shocks.     

Can banks default overnight? Modelling endogenous contagion on the O/N interbank market

We propose a new model of the liquidity-driven banking system focusing on overnight interbank loans. This significant branch of the interbank market is commonly neglected in the banking system modelling and systemic risk analysis. We construct a model where banks are allowed to use both the interbank and the securities markets to manage their liquidity demand and supply as driven by prudential requirements in a volatile environment. The network of interbank loans is dynamic and simulated every day. We show how the intrasystem cash fluctuations alone, without any external shocks, may lead to systemic defaults, and what may be a symptom of the self-organized criticality of the system. We also analyze the impact of different prudential regulations and market conditions on the interbank market resilience. We confirm that the central bank’s asset purchase programmes, limiting the declines in government bond prices, can successfully stabilize banks’ liquidity demands. The model can be used to analyze the interbank market impact of macroprudential tools.     

数字经济中经济法原则运用的相关理据和逻辑推理

在过去的经济法学领域,人们为求得共识,对经济法原则的研究机械性重复,弱化了其生命力。在当下,数字经济及相关市场迅速发展,亟须经济法予以回应,而经济法又面临着时代发展未定因素与市场风险不确定性所带来的挑战。因此,有必要对经济法基本原则加以厘定,并通过对经济法原则在数字经济发展中得以运用的理由及其事实依据的分析,以及对经济法原则运用的价值判断逻辑和现实运用逻辑的阐发,探寻和彰显经济法原则在数字经济调适中的价值指引作用,以助实现数字经济时代新场域的良法善治。