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Trading Risk Management: Practical Applications to Emerging Markets
Abstract
The global deregulation of financial markets has created new investment opportunities, which in turn require the development of new instruments, regulations and efficient risk-management policies/procedures to cope with increased risks. Nonetheless, many disastrous financial crises have hit several financial and non-financial corporations in emerging economies; even so, the developments and innovations in cash-markets instruments and derivative products are on a continuous growth path. Emerging countries and markets, since the early 1990s, have started to play an important role in standardized and over-the-counter (OTC) derivatives and cash-markets. Yet while emerging-market countries share some similarities in development patterns, it is often their individual differences that create unique opportunities and risks that may be addressed through derivative structures and sound risk-management practices.
... Since then the VAR concept was widespread and several specific applications were adapted to credit risk management and mutual funds investments. In his research papers, Al Janabi (2004Janabi ( , 2005a has established a practical framework and other relevant parameters for the measurement, management and control of market (trading) risk. The effects of illiquid assets, that are dominant characteristics of emerging markets, were also incorporated in his models. ...
... A simplified calculation process of the estimation of VAR risk factors (using variance/covariance method) for a single and multiple assets' positions is illustrated (Al Janabi, 2004, 2005a as follow: ...
... In order to perform the calculation of VAR under illiquid market conditions, one can define the following (Al Janabi, 2004Janabi, , 2005a: ...
The Mexican financial markets, like many other emerging markets, are generally portrayed as illiquid, volatile, segmented and politically unpredictable. Despite all these shortcomings, portfolio investments and trading activities in emerging markets have immense rewards for markets' participants. These investment alternatives may create unique expected return opportunities and substantial inherent risks. Risk measurement, management and control in such economies are in fact wearisome tasks; however, it may be addressed through art and science risk management practices. In this research paper, key market risk management methods and procedures that financial institutions, regulators and policymakers should consider in devising their daily market risk management objectives are examined and are adapted to the specific needs of emerging financial markets. The proper use of Value At Risk (VAR) and stress-testing methods are illustrated with real-world examples and practical reports of market risk analysis and control. The calculations and conclusions that are presented herein where applied to both, the Mexican foreign exchange and stock markets. To this end, several case studies were achieved with the objective of setting a practical framework of market risk measurement and control reports in addition to the inception of procedures for the setting of VAR's limits. The effects of hedging equity trading exposures with reciprocal foreign exchange trading positions were examined and quantified.
... Moreover, it can be applied to both developed and emerging economies and for both trading and investment financial positions. Although the risk measurement method that is adopted in this work is based mainly on the variance/covariance approach (a parametric approach that assumes normal distribution of returns), for emerging and illiquid markets it is possible to correct for the assumption of normality by including stress-testing (under severe market conditions) along with the aggregation of a realistic risk factor that takes into account illiquid securities (Al Janabi 2005). As such, our proposed portfolio VaR model is a combination of a closed-form parametric VaR along with a stresstesting approach that is based on historical simulation of real severe upheavals in the GCC markets. ...
... Some financial institutions feel that the usage of a 99 per cent confidence interval would place too much trust on the statistical model, and hence, some confidence level should be assigned to the 'art–side' of the risk measurement process (Al Janabi 2005). A simplified calculation process of the estimation of VaR risk factors (using variance/covariance method) for a single and multiple assets' positions is illustrated (Al Janabi 2005;2007a) in the following paragraph. From elementary statistics, it is well known that for a normal distribution, 68 per cent of the observations will lie within 1s (standard deviation) from the expected value, 95 per cent within 2s and 99 per cent within 3s from the expected value; thus, the VaR of a single asset in monetary terms is as follows: ...
The aim of this article is to bridge the gap in equity trading risk management literatures and particularly from the perspective of emerging and illiquid markets, such as in the context of the Gulf Cooperation Council (GCC) financial markets. In this article, we demonstrate a practical approach for the measurement and control of market risk exposure for financial trading portfolios that contain several illiquid equity securities during the unwinding (close-out) period. This approach is based on the renowned concept of Value-at-Risk (VaR) along with the development of an optimisation risk algorithm utilising matrix-algebra technique. Our thorough asset market risk modelling-algorithm can simultaneously handle VaR analysis under normal and severe market conditions, besides it takes into account the effects of illiquidity and short-sales of traded equity securities. In order to illustrate the proper use of VaR and stress-testing methods, real-world structured modelling techniques of trading risk management are presented for the GCC financial markets. To this end, comprehensive simulation case studies are accomplished with the objective of constructing a realistic framework for trading risk measurement and control in addition to the instigation of a risk optimisation algorithm-process for the computation of maximum authorised VaR risk-budgeting limits.
JEL Classification: C10, C13, G20, G28
... Moreover, it can be applied to both developed and emerging economies and for both trading and investment financial positions. Although the risk measurement method that is adopted in this work is based mainly on the variance/covariance approach (that assume normal distribution of returns), for emerging and illiquid markets it is possible to correct for the assumption of normality by including stresstesting (under severe market conditions) along with the aggregation of a realistic risk factor that takes into account illiquid securities (Al Janabi, 2005). ...
... These effects are then aggregated across the whole portfolio using the correlations between the risk factors (which are again extracted from the historical observation period) to give the overall VaR value of the portfolio with a given confidence level. A simplified calculation process of the estimation of VaR risk factors (using variance/covariance method) for a single and multiple assets' positions is illustrated (Al Janabi, 2005 and2007a) as follows: ...
The aim of this article is to bridge the gap in equity trading risk management literatures and particularly from the perspective of emerging and illiquid markets, such as in the context of the Gulf Cooperation Council (GCC)’s six financial markets. To the authors’ best knowledge, this is the first research paper that addresses the issue of equity trading risk management in the GCC countries with direct applications to their six stock markets. In this paper, the authors demonstrate a practical approach for measurement, management and control of market and liquidity risk exposures for financial trading portfolios that contain several illiquid equity securities. This approach is based on the renowned concept of Liquidity-Adjusted Value at Risk (L-VaR) along with the development of an optimization software tool utilizing matrix-algebra technique under the notion of different correlation factors and liquidation horizons. The comprehensive trading risk model can simultaneously handle L-VaR analysis under normal and severe market conditions besides it takes into account the effects of illiquidity of all traded equity securities. In order to illustrate the proper use of L-VaR and stress-testing methods, real-world examples and feasible reports of equity trading risk management are presented for the six GCC equity financial markets by implementing a daily database of indices’ returns for the period 2004-2008. To this end, several financial modeling studies are achieved with the objective of creating a realistic framework of equity trading risk measurement and control reports in addition to the instigation of a practical iterative optimization technique for the calculation of maximum authorized L-VaR limits subject to real-world optimum operational constraints.
... A simplified calculation process of the estimation of VAR risk factors (using variance/ covariance method) for a single and multiple assets' positions is illustrated 13,14 as follow: ...
This paper provides real-world techniques and optimum asset allocation strategies that can be applied to equity trading portfolios in emerging and illiquid financial markets. Key market risk management methods and procedures that financial entities, regulators and policymakers should consider in formulating their daily market risk management objectives are examined and are adapted to the specific needs of emerging countries. The aim of this paper is to fill a gap in the trading risk management literature and particularly from the perspective of emerging and illiquid markets, such as in the context of the Mexican financial markets. In this paper, we demonstrate a comprehensive and proactive approach for the measurement, management and control of equity trading risk exposure, which takes into account proper adjustments for the illiquidity of both long and short trading/investment positions under normal and severe market conditions and within a multi-security setting. Our approach is based on the renowned concept of Value-at-Risk (VAR) along with the innovation of a software tool utilising matrix-algebra and other optimisation techniques. To illustrate the proper use of VAR and stress-testing (scenario analysis) methods, real-world examples and practical reports of market risk management are calculated and presented for a selected portfolio from the Mexican Stock Market (BMV). To this end, several case studies were achieved with the objective of creating a realistic framework of trading risk measurement and control reports in addition to the inception of procedures for the calculation of the maximum authorised VAR limits.Journal of Derivatives & Hedge Funds (2007) 13, 33–58. doi:10.1057/palgrave.jdhf.1850059
Effective liquidity management and risk controls are crucial for maintaining the stability of both the global financial system and individual financial institutions. Despite consensus on this necessity, regulatory approaches have varied. Over recent decades, technological advancements have expanded the tools available for liquidity risk management. However, these innovations have also led to underestimations of actual risk exposure. This chapter aims to propose proactive policies and procedures for managing trading risk exposure and liquidity, particularly focusing on their interplay with counterparty, credit, and market risks. It offers practical solutions and internal regulations for financial operational divisions, emphasizing the unique challenges faced by trading units in both emerging and developed economies. Leveraging the author's background as a global market risk director and head of derivatives trading, this chapter offers valuable insights and guidelines for market participants, regulators, and policymakers to develop robust trading units within a sound regulatory framework. Specific techniques for managing trading risk are reviewed and adapted to the needs of developing markets, with a focus on multiple-asset proprietary portfolios. The chapter also explores the utility of trading risk models, such as Value-at-Risk (VaR), offering practical algorithms for parametric variance–covariance VaR. Guidelines for incorporating asset illiquidity into trading portfolios are provided, along with a thorough explanation of the Risk-Adjusted-Return-On-Capital (RAROC) technique. Additionally, the chapter addresses gaps in statistical data, offering solutions for incomplete and omitted statistics. In this context, this chapter presents comprehensive strategies for managing trading and liquidity risks, aiming to enhance the reliability and efficiency of financial markets in both emerging and developed economies.
In the intricate world of finance, where markets operate at lightning speed and assets change hands in the blink of an eye, liquidity reigns supreme. It is the lifeblood of financial markets, ensuring the smooth functioning of economies worldwide and facilitating price discovery. Liquidity refers to the ease with which an asset can be converted into cash without causing a significant change in its price. However, liquidity issues can arise in both financial markets and institutions, presenting challenges with far-reaching implications. Beneath its seemingly straightforward facade lies a complex ecosystem fraught with risks and interdependencies. Liquidity risk is a significant concern for financial institutions and investors, representing the potential inability to convert an asset into cash quickly enough to meet financial obligations without incurring substantial losses. Effective liquidity risk management is imperative for maintaining financial stability and ensuring the smooth functioning of markets. This chapter embarks on an exhaustive journey through the complexities of liquidity, delving into liquidity issues in finance, exploring its interactions with market risk, and probing the uncharted territories where these domains converge. Furthermore, this chapter examines the intricate relationship between liquidity, liquidity risk, and market risk, highlighting the influence of artificial intelligence (AI) and machine learning (ML) in finance. It addresses current challenges and outlines how intelligent algorithms are evolving to manage liquidity and market risk more effectively.
This chapter critically examines the assessment of trading risk under illiquid market conditions, a crucial consideration for financial institutions striving to meet regulatory requirements such as those outlined in the Basel capital adequacy accords. With a specific focus on emerging financial markets, the chapter aims to look into the intricacies of asset liquidity risk and its implications for portfolio management. The primary objective is to derive Liquidity-Adjusted Value-at-Risk (L-VaR) estimates for a range of equity portfolios, recognizing the increasing importance of such risk measures in contemporary financial landscapes. To achieve this, the chapter adopts a comprehensive approach, integrating three distinct asset liquidity models within a multivariate framework. In addition to these models, the chapter employs the GARCH-M (1,1) method to estimate expected returns and conditional volatility, providing a nuanced understanding of risk dynamics under various market conditions. Through rigorous analysis of more than six years of daily return data from emerging Gulf Cooperation Council (GCC) stock markets, the study uncovers notable insights. Among the key findings is the critical influence of the selected internal liquidity model on L-VaR statistics, particularly evident in scenarios involving short-sales of stocks and extreme correlation factors among trading assets. The chapter highlights the significance of accounting for extreme correlations, especially when correlations exhibit significant fluctuations or approach unity. By offering real-world asset allocation tactics tailored for portfolios operating in adverse market conditions, the chapter addresses a notable gap in risk management literature. Through its exploration of L-VaR estimation techniques and their implications for portfolio risk management, this study provides valuable insights for practitioners navigating the complexities of contemporary financial markets.
The purpose of this article was to analyze the benefits, barriers and perspectives for the development of inter-school cooperation in Poland. A review of the literature on various forms of cooperation, competition and coopetition in the environment of higher educa-
tion institutions as well as challenges connected with them were presented. Subsequently, pilot in-depth interviews with decision-makers from universities educating in social sciences were conducted and analyzed. The main conclusion is that cooperation
between universities, in particular cooperation with a stronger partner, is considered to be beneficial. In practice, however, there are numerous barriers that make this cooperation very limited, mainly due to operating in conditions of competition for students and funds for scientific and research activities. Therefore, cooperation with a reputable foreign partner seems to be nowadays the most desirable form of cooperation.
This chapter examines a practical methodology for the assessment and control of market and liquidity risk exposures for financial trading portfolios that consist of certain equity assets. The applied technique is based on the contemporary concept of liquidity-adjusted value at risk (LVaR) along with the application of optimization risk-engine algorithms. This chapter proposes a broad market and liquidity risk management model that can concurrently perform LVaR estimation under regular and stressed market scenarios. It takes into account the effects of illiquidity of traded equity assets. In order to demonstrate the appropriate application of LVaR and stress-testing techniques, real-world case analysis of trading risk management are presented for the Gulf Cooperation Council (GCC) stock markets. To this end, a number of optimization case studies are examined with the aim of developing a novel technique of trading risk measurement as well as the implementation of a risk optimization process for the computation of the maximum permitted LVaR limits.
The aim of the present paper is to present the role of formal and legal determinants in risk management in enterprises listed on the Warsaw Stock Exchange. Theoretical part shows the essence, types and role of stock exchange risk in shaping risk management processes in stock enterprises. While the empirical part includes research findings from analysis of formal and legal requirements for risk management in this enterprise group.
The use of financial portfolio theory to guide new venture planning is discussed and rejected. Instead, a dual conceptualization of business risk is offered that is more consistent with statistical inference and several other theories of risk and decision making. It provides a better understanding of entrepreneurial decision making and how planning can both reduce and increase risk.
It is shown that a strongly consistent estimation procedure for the order of an autoregression can be based on the law of the iterated logarithm for the partial autocorrelations. As compared to other strongly consistent procedures this procedure will underestimate the order to a lesser degree.
Estimating and controlling large risks has become one of the main concern of financial institutions. This requires the development of adequate statistical models and theoretical tools (which go beyond the traditionnal theories based on Gaussian statistics), and their practical implementation. Here we describe three interrelated aspects of this program: we first give a brief survey of the peculiar statistical properties of the empirical price fluctuations. We then review how an option pricing theory consistent with these statistical features can be constructed, and compared with real market prices for options. We finally argue that a true `microscopic' theory of price fluctuations (rather than a statistical model) would be most valuable for risk assessment. A simple Langevin-like equation is proposed, as a possible step in this direction.
