Financial Econometrics and Empirical Market Microstructure
Research of nonlinear dynamics of finance series has been widely discussed in literature since the 1980s with chaos theory as the theoretical background. Chaos methods have been applied to the S&P 500 stock index, stock returns from the UK and American markets, and portfolio returns. This work reviews modern methods as indicators of nonlinear stochastic behavior and also shows some empirical results for MICEX stock market high-frequency microstructure variables such as stock price and return, price change, spread and relative spread. It also implements recently developed recurrence quantification analysis approaches to visualize patterns and dependency in microstructure data.
The problem of optimal portfolio liquidation under transaction costs has been widely researched recently, thus producing several approaches to problem formulation and solving. Obtained results can be used for decision making during portfolio selection or automatic trading at high-frequency electronic markets. This work gives a review of modern studies in this field, comparing models and tracking their evolution. The paper also presents results of applying the most recent findings in this field to real MICEX shares high-frequency data and gives an interpretation of the results.
This paper is concerned with modeling the demand for mortgage loans. The demand for loans can be represented as two functions: probability of borrowing and the loan amount, depending on borrower-specific characteristics, contract terms and set of macrovariables. The decision-making process for borrowing can be described as the sequence of decisions on: 1) choosing the credit program; 2) approving of a borrower; 3) choosing contract terms from a feasible set; 4) and loan performance. Following Philips and Yezer (1996) and Attanasio, Goldberg and Kyriazidou (2008) the author proposes an econometric approach that deals with endogeneity and self-selection of borrowers when estimating the demand-for-loan equations and specifies the structure of data that is required for implementation.
The aim of this paper is to consider some problems with evaluation of the impact of high frequency trading on market liquidity. The first part is devoted to difficulties of disentangling the impact of high frequency on market liquidity from other relevant factors. The remainder of the paper is intended to discuss some issues affecting the evaluation of the influence of high frequency trading on particular aspects of market liquidity.
This paper proposes method of detecting a structural break/shift in time series such as AR(1) with a nonlinear dependence structure of lagged value and the estimation of the break point, based on nonparametric estimations of the dependence’s copulas and comparison with some existing tests. However, we assumed the time series to be stationary and homoscedastic. This paper compares the efficiency of the standard test, considering only linear autoregressive dependence nature. A suggested technique is given, some modifications of the evaluation scheme is offered and a more flexible method of detecting structural break is proposed, usefulness of our methodology is demonstrated through some applications to a few macroeconomic and financial time series. The paper is organized as follows: the first section contains a selective literature review. The second section describes the generation’s procedure of time series, used in further calculations. The problem of detection of the structural break with respect to the nonlinear time series is formulated in the third section. The fourth section contains results of evaluations using simulated data. In Sect. 5 we provide examples of our suggested technique. The final section contains "Conclusions".
Econophysics is a relatively new discipline. It is one of the most interesting and promising trends in modeling complex economic systems such as financial markets. In this paper we use the approach of econophysics to explain various mechanisms of price formation in the stock market. We study a model, which was proposed by Jean-Philippe Bouchaud and Dietrich Stauffer (Bouchaud 2002; Chang et al. 2002; Stauffer 2001; Stauffer and Sornette 1990), and used to describe the agents’ cooperation in the market. The most important point of this research is the calibration of the model, using real market conditions to proof the model’s possibility of setting out a real market pricing process
We examine the synergy of the credit rating agencies’ efforts. This question is important not only for regulators, but also for commercial banks if the implementation of the internal ratings and the advanced Basel Approach are discussed. We consider Russian commercial banks as a good example where proposal methods might be used. Firstly, a literature overview was supplemented with an analysis of the activities of rating agencies in Russia. Secondly, we discussed the methods and algorithms of the comparison of rating scales. The optimization task was formulated and the system of rating maps onto the basic scale was obtained. As a result we obtained the possibility of a comparison of different agencies’ ratings. We discussed not only the distance method, but also an econometric approach. The scheme of correspondence for Russian banks is presented and discussed. The third part of the paper presents the results of econometric modeling of the international agencies’ ratings, as well as the probability of default models for Russian banks. The models were obtained from previous papers by the author, but complex discussion and synergy of their systematic exploration were this paper’s achievement. We consider these problems using the example of financial institutions. We discuss the system of models and their implementation for practical applications towards risk management tasks, including those which are based on public information and a remote estimation of ratings. We expect the use of such a systemic approach to risk management in commercial banks as well as in regulatory borders.
The mortgage crisis that started in the U.S. in 2007 and lasted until 2009 was characterized by an unusually large number of defaults on the subprime mortgage market. As a result, it developed into a global economic recession and placed the stability of the world banking system in jeopardy. Therefore, the issues of credit risk modeling showed the shortcomings of the current credit risk practice. Truncation, or partial observability, and simultaneous equations bias causes sample selection bias. As a result, parameter estimates are biased and inconsistent. Firstly, we provide an overview of current approaches in the mortgage literature to control for the sample selection bias correction, such as the Heckman model and bivariate probit model with selection. Secondly, a review of the most significant mortgage studies discussing this problem is introduced. Specifically, different structural models, specific datasets and empirical results are regarded. In addition, we discuss such key credit risk determinants as borrower characteristics, terms of the mortgage contract, mortgage characteristics, and macroeconomic conditions. Finally, we conclude the discussion with possible research questions.