Estimation of zero-intelligence models by L1 data

A unit volume zero-intelligence (ZI) model is defined and the distribution of its L1 process is recursively described. Further, a generalized ZI model allowing non-unit market orders, shifts of quotes and general in-spread events is proposed and a formula for the conditional distribution of its quotes is given, together with a formula for price impact. For both the models, MLE estimators are formulated and shown to be consistent and asymptotically normal. Consequently, the estimators are applied to data of six US stocks from nine electronic markets. It is found that more complex variants of the models, despite being significant, do not give considerably better predictions than their simple versions with constant intensities.     

Analysing liquidity and absorption limits of electronic markets with volume durations

This paper focuses on the liquidity of electronic stock markets applying a sequential estimation approach of models for volume duration with increasing threshold values. A modified ACD model with a Box–Tukey transformation and a flexible generalized beta distribution is proposed to capture the changing cluster structure of duration processes. The estimation results with German XETRA data reveal the market's absorption limit for high volumes of shares, expanding the time costs of illiquidity when trading these quantities.     

Research on the use of financial statement information for forecasting profitability

Research on the use of financial statement information for forecasting profitability has two objectives: (i) to generate improved forecasts of profitability and accurate estimates of firm value; and (ii) to identify market inefficiencies with respect to financial statement information. For these areas of research, this article describes the evolution, provides examples and shares implications of the research. It also discusses opportunities for future research. The article highlights that financial statement analysis research has slowly evolved and has received limited attention from academics. The article argues that there are vast opportunities for impactful research on fundamental analysis and market inefficiencies.     

Change analysis of a dynamic copula for measuring dependence in multivariate financial data

This paper proposes a new approach to measure dependencies in multivariate financial data. Data in finance and insurance often cover a long time period. Therefore, the economic factors may induce some changes within the dependence structure. Recently, two methods have been proposed using copulas to analyse such changes. The first approach investigates changes within the parameters of the copula. The second determines the sequence of copulas using moving windows. In this paper we take into account the non-stationarity of the data and analyse the impact of (1) time-varying parameters for a copula family, and (2) the sequence of copulas, on the computations of the VaR and ES measures. We propose tests based on conditional copulas and the goodness-of-fit to decide the type of change, and further give the corresponding change analysis. We illustrate our approach using the Standard & Poor 500 and Nasdaq indices in order to compute risk measures using the two previous methods.     

Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

Power mapping with dynamical adjustment for improved portfolio optimization

For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the portfolio risk. A method to suppress this noise is power mapping. It raises the absolute value of each matrix element to a power q while preserving the sign. In this paper we use the Markowitz portfolio optimization as a criterion for the optimal value of q and find a K/T dependence, where K is the portfolio size and T the length of the time series. Both in numerical simulations and for real market data we find that power mapping leads to portfolios with considerably reduced risk. It compares well with another noise reduction method based on spectral filtering. A combination of both methods yields the best results.     

Non-parametric estimation of historical volatility

Evolving volatility is a dominant feature observed in most financial time series and a key parameter used in option pricing and many other financial risk analyses. A number of methods for non-parametric scale estimation are reviewed and assessed with regard to the stylized features of financial time series. A new non-parametric procedure for estimating historical volatility is proposed based on local maximum likelihood estimation for the t-distribution. The performance of this procedure is assessed using simulated and real price data and is found to be the best among estimators we consider. We propose that it replaces the moving variance historical volatility estimator.     

Unveiling investor-induced channels of financial contagion in the 2008 financial crisis using copulas

Understanding how financial crises spread is important for policy-makers and regulators in order to take adequate measures to prevent or contain the spread of these crises. This paper will test whether there was contagion of the subprime financial crisis to the European stock markets of the NYSE Euronext group (Belgium, France, the Netherlands and Portugal) and, if evidence of contagion is found, it will determine the investor-induced channels through which the crisis propagated. We will use copula models for this purpose. After assessing whether there is evidence of financial contagion in the stock markets, we will examine whether the ‘wealth constraints’ transmission mechanism prevails over the ‘portfolio rebalancing’ channel. An additional test looks at the interaction between stock and bond markets during the crisis and allows us to determine if the transmission occurred due to the ‘cross market rebalancing’ channel or the ‘flying to quality’ phenomenon. The tests suggest that (i) financial contagion is present in all analyzed stock markets, (ii) a ‘portfolio rebalancing’ channel is the most important crisis transmission mechanism, (iii) and the ‘flight-to-quality’ phenomenon is also present in all analyzed stock markets.     

金融危机及其应对政策对我国宏观经济的影响——基于金融CGE模型的模拟分析

本文通过构建中国金融CGE模型,编制中国金融SAM表,模拟了金融危机及其应对政策对中国宏观经济的影响,并对政策效果进行了后验式评价。研究结果表明,大规模投资对增加企业收入、促进GDP增长等效果明显,减税政策能明显改善居民福利,虽然调整利率同样可以促进经济的恢复但效果并不十分明显。当经济政策能够提高居民边际消费倾向时,经济升温的步伐将加快。但应对金融危机的刺激政策存在着引发通货膨胀、不能有效刺激出口等风险,所以需要相关后续配套政策对宏观经济进行进一步调整。     

Assessing the relevance of an information source to trading from an adaptive-markets hypothesis perspective

I propose a framework motivated by the Adaptive Markets Hypothesis (AMH) to analyze the relevance of a specific information source for the trading of a given security. To illustrate the applicability and advantages of this methodology, I explore the extent to which the financial statement (FS) is relevant for Credit Default Swap (CDS) trading. Specifically, I adopt a Bayesian Model Averaging approach to examine properties of the accounting metrics that enter the implied trading heuristics of the market participants. Hypothesis-testing is conducted on various horizons around the announcement dates of corporate results. The diversity of trading rules and the shift in the heuristics mix that occurred after 2008 support the AMH perspective. Overall, results show that there is a significant component of profit-motivated trading in the CDS market that relies on financial statement information, even after controlling for information transmission from alternative trading forums. Out of sample trading strategies confirm the robustness the main findings.     

Quantile regression estimates and the analysis of structural breaks

The paper considers a test for structural breaks based on quantile regressions instead of OLS estimates. Besides granting robustness, this allows us to verify the impact of a break in more than one point of the conditional distribution. The quantile regression test is then repeatedly implemented as a diagnostic tool to uncover partial or spurious breaks. The test is also implemented to measure the contribution of each explanatory variable to the instability of the regression coefficients, thus finding which one of the different possible sources of breaks linked to the nature of the explanatory variables is the most effective. A real data example of exchange rates shows the presence of a time-driven break, but only at the lower quartile, while the analysis of the explanatory variable excludes its involvement in the break. Since the asymptotic distribution of the OLS test for structural change depends on i.i.d. normal errors and on the exogeneity of the explanatory variables, a Monte Carlo study analyses the behavior of OLS and quantile regression tests for structural changes with lagged endogenous variables, non-normal errors, spurious or partial breaks, and misspecification.     

Empirical comparison of hazard models in predicting SMEs failure

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.     

Risk and predictability of Singapore's private residential market

This study explores the short-run predictability of, and the risks facing investors in, Singapore's private housing market. We explicitly model a periodically collapsing rational speculative bubble within the present-value framework, and propose an unconventional approach as a first-step to screen for structural break(s). We found that a rational speculative bubble is an important predictor of the short-run price growth, especially in volatile times. Furthermore, rent is the only fundamental having a non-negligible impact. The study suggests that the major risk facing market participants comes from unpredictable local policy shifts, and/or a potentially predictable systemic risk.     

Integration of European bond markets

I investigate the time variation in the integration of EU government bond markets. The integration is measured by the explanatory power of European factor portfolios for the individual bond markets for each year. The integration of the government bond markets is stronger for EMU than non-EMU members and stronger for old than new EU members. For EMU countries, the integration is weaker the lower the credit rating is. During the recent crisis periods, the integration is weaker, particularly for EMU countries.     

The impact of the choice of VaR models on the level of regulatory capital according to Basel II

The Basel II framework allows the calculation of the capital requirements for market risk with Value-at-Risk models. Since no special model is prescribed in the framework, banks may use simple models with questionable assumptions concerning their underlying distributions. Our numerical analysis reveals that simple VaR models that perform noticeably worse than comparable simple models with more realistic assumptions may lead to a lower level of regulatory capital for banks. For this reason, banks have a major incentive to implement bad models. This is obviously contrary to the interests of regulatory authorities.