Market Risk Management Research Articles

Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm

Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision‐making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long‐short‐term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO‐LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.

ABSORPTIVE CAPACITY: COMPONENTS AND ORGANIZATIONAL MECHANISMS FOR ITS DEVELOPMENT

ABSTRACT Purpose: The objective of this study is to understand how the process of absorptive capacity (ACAP) is developed, by analyzing the behaviors and abilities of a group of companies, their routines and processes and the mechanisms of learning and knowledge governance. The companies operate in the metal mechanics sector and they are based in the southern region of Brazil. Originality/value: The relevance consists of the operationalization of component elements and in the development of organizational mechanisms of dynamic capacity (Meirelles & Camargo, 2014), as means of ACAP development (Wang & Ahmed, 2007) in organizations belonging to the metal mechanics sector. Design/methodology/approach: It is a qualitative, exploratory and descriptive empirical research, carried out by a multiple case study. It seeks to investigate in detail three organizations of the metal mechanics sector, by analyzing the procedures, resources, mechanisms, and systems, which makes it possible for them to develop absorptive capacities. Content analysis was performed in order to analyze the data. Findings: The researched companies develop absorptive capacity through some behaviors and abilities, such as relationship capacity, learning capacity, and people’s training capacity, and through routines and processes, especially information search routines, market monitoring routines and risk management. Finally, the researched companies develop absorptive capacity by learning mechanisms and knowledge governance, such as researches, information exchange among clients, suppliers and other companies.

Price Discovery Mechanism in Spot and Futures Market of Agricultural Commodities: The Case of India

There has been increasing focus by emerging market researchers, policymakers and regulators for investigating price discovery, relationship between future and physical market and accessible trading and risk management instruments for the benefit of various stakeholders and thus contributing to the development of literature. The central question of this paper is examining the role of influence of one market on the other and the role of each market segment in price discovery in the Indian context. Johansen Vector Error Correction Model (VECM) has been employed to examine the relationship between the spot and futures prices. The cointegration results do not confirm the existence of long-run relationship between spot and futures prices. It is thus, implied that futures prices unlikely serve as market expectations of subsequent spot prices of selected agri-commodities in India and do not help in price discovery process.

On the failure of mutual fund industry regulation

Mutual funds grant retail investors access to professional asset management and facilitate exposure to financial markets. The academic literature and regulators have traditionally focused on issues such as portfolio diversification, performance, liquidity, and management fees in attempts to analyze and improve market efficiency. Scarce attention has been paid to market risk management. There is unanimity on this issue throughout the world. The lack of regulatory attention creates a gap, which is partially covered by mutual fund rating agents and asset management analysts. Those agents base their ratings on various rating methodologies — which engenders a wide array of difficulties, especially for retail investors. We employ proprietary data on historic mutual fund ratings in Israel and show that retail investors do not necessarily benefit from this diversity of opinions. Furthermore, we find that the voluntary implementation of quantitative risk measurement techniques by certain mutual funds tends to be associated with fewer outflows and greater inflows in these funds. Interestingly, the application of (backward-looking) value-at-risk analysis is associated with fewer outflows, while (forward-looking) stress-testing techniques are associated with greater inflows. Given the similarity of mutual fund industry environments across the globe, our results have worldwide applicability.

FARVaR: Functional Autoregressive Value-at-Risk*

Motivated by the stylized fact that intraday returns can provide additional information on the tail behavior of daily returns, we propose a functional autoregressive value-at-risk (VaR) approach which can directly incorporate such informational advantage into the daily VaR forecast. Our approach leads to greater flexibility in modeling the dynamic evolution of the density function of intraday returns and the ability to capture substantial swings in the tails following major events. We comprehensively evaluate our proposed model using intraday transaction data and demonstrate that it can improve coverage ability, reduce economic cost, and enhance statistical reliability in market risk management.

Distributional predictability between commodity spot and futures: Evidence from nonparametric causality-in-quantiles tests

Unbiasedness and informational efficiency of futures markets under different market conditions is a claim that still remains unsettled in the theory of non-arbitrage and asset pricing and in empirics as well. This study investigates this claim using a novel causality-in-quantile model of Balcilar et al. (2016) for two energy commodities, crude oil and natural gas, and two precious metals, gold and silver. The model estimates causalities-in-mean and -variance between spot and futures market in a time varying context conditioning on the states of the markets represented by the quantiles of the conditional distribution of the dependent variable. The causality in return is asymmetric and unidirectional from futures to spot market for all commodities. That means the predictability of the futures market, due to its informational efficiency, is strong in the normal market and declines when the spot market enters into extreme bearish and bullish conditions. The causality-in-variance is bi-directional in the normal to bull markets except for natural gas where it is unidirectional from futures to spot only. It is a confirmation of the risk management and price discovery role of futures market. Lack of causality in the bear market entails some kind of disconnectedness between the spot and futures markets in a bad market where intervention is called for from the exchange and regulators to restore stability in the spot and futures market dynamics. Although economic uncertainty is found to have no impact on the causality-in-mean except gold; however, the causality-in -variance is influenced in the case of gold and crude. This is a kind of reaffirmation of the fact that under economic uncertainty, futures contracts are used for hedging under different market conditions. However, the causality between commodity spot and futures are resilient to exchange rate.

Shocks and Volatility Spillover Between Stock Markets of Developed Countries and GCC Stock Markets

ABSTRACTThe purpose of this paper is to examine the spillover of returns, information and volatility of returns, and conditional variance-covariance between the stock markets of developed countries namely the United States of America, the United Kingdom and China (US, UK and CH) and the stock markets of Gulf Cooperation Council (GCC) countries (Kuwait, United Arab Emirates, Qatar, Saudi Arabia, Oman, and Bahrain) using daily returns spanned from 2 March 2003 to 9 December 2010. We consider shocks and volatility spillover model by applying a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model using; MGARCH-BEKK to identify the source and magnitude of volatility and shock spillover. We get the correlation between GCC markets is positive, indicating that there is a common factor which is driving the markets towards the same direction. Evidence shows that the own-shocks and volatility in GCC markets are highly significant. Cross-information spillover effects, as another observable trend, are found between Qatar and Oman. Furthermore, the results show that UA is significantly affected by spillover (return, shocks and volatility) from developed markets, while there have been no significant effects seen from Kuwait markets. This study takes a new empirical look in the sense that the models incorporating all the countries under investigation are estimated jointly utilizing multivariate GARCH-BEKK formulation. In addition, this paper should be interesting for academicians as well as practitioners. Including those interested in modelling multivariate volatility for financial market risk management.

On the Failure of Mutual Fund Industry Regulation

Mutual funds grant retail investors access to professional asset management and facilitate exposure to financial markets. The academic literature and regulators have traditionally focused on issues such as portfolio diversification, performance, liquidity, and management fees in attempts to analyze and improve market efficiency. Scarce attention has been paid to market risk management. There is unanimity on this issue throughout the world. The lack of regulatory attention creates a gap, which is partially covered by mutual fund rating agents and asset management analysts. Those agents base their ratings on various rating methodologies — which engenders a wide array of difficulties, especially for retail investors. We employ proprietary data on historic mutual fund ratings in Israel and show that retail investors do not necessarily benefit from this diversity of opinions. Furthermore, we find that the voluntary implementation of quantitative risk measurement techniques by certain mutual funds tends to be associated with fewer outflows and greater inflows in these funds. Interestingly, the application of (backward-looking) value-at-risk analysis is associated with fewer outflows, while (forward-looking) stress-testing techniques are associated with greater inflows. Given the similarity of mutual fund industry environments across the globe, our results have worldwide applicability.

Use of neural network models in the market risk management

This topic is of high relevance due to the fact that many currently available mathematical market risk assessment models contain many limitations for their effective use. However, these limitations are often not feasible, what leads to a decrease in forecast accuracy. To avoid this, more accurate models are necessary. Neural network-based models can show a more precise result due to their basic property – nonlinearity. The interest in neural networks re-emerged only after some important theoretical results were attained in the early eighties and new hardware developments increased the processing capacities. Artificial neural networks can be most adequately characterised as «computational models» with particular properties such as the ability to adapt or learn, to generalise, or to cluster or organise data, and which operation is based on parallel processing. The task of this paper is to build a model that can enable us to assess a market risk for a company. The primary goal of this paper is to determine a lower bound of the yield to be forecast by the neural network model with a certain level of significance. Current actual yields will be fed to the neural network output, and some factors will be fed to the neural network input.

Financial risk management based on quantile regression model

To reflect the loss of asset returns in the financial market in time to better avoid risks, a quantile regression model was established. First, the development history and research background of the quantile regression model were introduced. Then, the research results of VaR method and quantile regression theory were summarized. The definition of financial risk and the Bayesian inference framework were introduced. Finally, the VaR indicators in financial risk management were introduced, and the application of Bayesian quantile regression model in the field of financial market risk management was discussed. Results show that Bayesian quantile regression had certain adaptability in the calculation of financial market VaR, and could easily calculate the VaR value under each confidence level.

Estimating a covariance matrix for market risk management and the case of credit default swaps

We analyze covariance matrix estimation from the perspective of market risk management, where the goal is to obtain accurate estimates of portfolio risk across essentially all portfolios—even those with small standard deviations. We propose a simple but effective visualisation tool to assess bias across a wide range of portfolios. We employ a portfolio perspective to determine covariance matrix loss functions particularly suitable for market risk management. Proper regularisation of the covariance matrix estimate significantly improves performance. These methods are applied to credit default swaps, for which covariance matrices are used to set portfolio margin requirements for central clearing. Among the methods we test, the graphical lasso estimator performs particularly well. The graphical lasso and a hierarchical clustering estimator also yield economically meaningful representations of market structure through a graphical model and a hierarchy, respectively.

Structured Factor Model and Its Applications on Market Risk Management

The aims of this paper are twofold. First, we develop a new algorithm to fit a structured factor model for high-dimensional data analysis. The algorithm fits to the covariances of observable variables and has several advantages: it overcomes the curse of dimensionality that arises usually in analyzing high-dimensional data and converges effectively; it produces a unique solution of factor loadings that preserve the layer structure of observable variables; it properly captures the idiosyncratic components of the structured factor model and thus become particular useful for the applications in the risk management where idiosyncratic component represent a part of the specific risk and might be too significant to be treated as an estimation errors. Secondly, as an application, we provide a model to decompose market risk and simulate the shocks of the market variables relevant to the trading portfolio as part of a large bank's VaR based risk measure framework. The model is capable of capturing not only the covariances and the marginal volatilities of the market variables, but also can be enhanced to include some desired features about the target loss distributions. The model is scalable so that it works effectively with virtually no limit to the size of the portfolio. We also discuss how the model can be used to simulate shocks of illiquid market variables. The case study on SP500 suggests that the model is capable of providing a satisfactory performance with a simple and transparent setting.

Credit Risk Management of Kumari Bank Ltd. Nepal

Banks are always faced with different types of risks that may have a potentially negative effect on their business. Risk-taking is an inherent element of banking and, indeed, profits are in part the reward for successful risk taking in business. On the other hand, excessive and poorly managed risk can lead to losses and thus endanger the safety of a bank's depositors. Risks are considered warranted when they are understandable, measurable, controllable and within a bank’s capacity to readily withstand adverse results. Sound risk management systems enable managers of banks to take risks knowingly, reduce risks where appropriate and strive to prepare for a future, which by its nature cannot be predicted.Financial institutions are subject to a number of risks such as Credit risk, Market risk management, Foreign exchange risk, Operational risk, and Liquidity risk. Although credit risk has always been of primary concern to these institutions, its importance became paramount during the recent financial crisis. The crisis exposed the shortcomings of existing risk management systems, and several firms saw significant losses resulting from failure of their counterparties to deliver on contracts. Firms may also be worried about a second recession, which makes credit risk a top priority.

Price Change, Volatility, and Accurate VaR: Evidence from the NSW and QLD Power Markets

This article investigates the relationship among price changes, volatility forecasts, and accurate Value-at-Risk (VaR) estimates in power markets. The authors use various GARCH(p,q) methods to model daily volatility and estimate VaR in New South Wales (NSW) and Queensland (QLD) regions. Then, they explore the relationship among price changes, and volatility forecasts and accurate VaRs for both generator and retailer sides, at 99 percent, 95 percent and 90 percent VaR confidence levels in six different out-of-sample periods. They find that forecasted volatility and accurate VaR variables tend to be statistically significant in explaining variations in power price changes. They also find that the coefficients of forecasted volatility tend to be negative. This indicates that higher forecasted volatility predicts negative price changes. Therefore, market participants in power markets, either generator or retailer, should expect a negative price change when their volatility forecast is high. Furthermore, the authors find that accurate VaR tends to lead to a decrease in price changes. This implies that well-functioning market risk management explains an unfavorable price change for the generator, but a favorable price change for the retailer in the Australian power markets.

Trends and prospects of the Ukrainian credit market post-crisis development

The article deals with the transformation processes that took place in the credit market of Ukraine under the influence of the modern financial and economic crisis. In the article have been analyzed problems and factors that caused the long stagnation of the credit activity in banking sector especially in certain types of bank lending. The author notes that the main reason of such a long credit contraction in the banking sector was the week institutional development of the Ukrainian credit market in previous periods. It is the lack of developed credit market institutions that caused problems of proper protection of the creditors' rights, non-performing loans management and hedging of market risks. In the article has been pointed out on the discovered tendency of active growth of the share of loans issued in the non-banking sector. At this stage, the trend is quite active, but mostly occurs in the segment of short-term consumer lending and in the area of instruments alternative to mortgage lending. Despite the market nature of the process reflecting this trend, due to the underdeveloped institutional infrastructure, it also poses a potential risk of increased fraud activity and over-indebtedness of the households. Based on the analysis of the characteristics and processes in the Ukrainian credit market the author has made conclusions about the importance of state assistance in the development of regulatory and legal support of the Ukrainian credit market in order to restore bank lending and strengthen supervision of the non-banking sector of the credit market. It is emphasized that without a state-initiated solution of the institutional problems present on the credit market of Ukraine, the recovery of the credit market may take an indefinite period of time.

Measuring Black Swans in Financial Markets

There exists a well-developed statistical theory predicting extreme price values for financial markets known as extreme value theory (EVT). This approach relies on the seemingly obvious, but rarely analyzed, assumption that price displacement extremes actually exist for various markets. This paper attempts to describe the behavior of financial markets as a set of functions in terms of the dynamic variables price and time based on the net difference between ask and bid volumes over a unit period, thereby offering evidence to support the assumption that price extremes exist. Yet, it’s not meaningful to show merely that extremes exist. If the extreme negative price displacement simply represents a complete market collapse then the assumption becomes trivial. Accordingly, the paper also introduces a method to determine whether price displacements are constrained by non-trivial extremes. This description might have implications for EVT and market risk management in approximating the magnitude of “Black Swan” events. The paper also shows that if one can closely approximate the magnitude of such a rare event, one cannot also predict when the event will occur with any meaningful degree of certainty.

Real Time Monitoring of Asset Markets: Bubbles and Crises

While each financial crisis has its own characteristics, there is now widespread recognition that crises arising from sources such as financial speculation and excessive credit creation do inflict harm on the real economy. Detecting speculative market conditions and ballooning credit risk in real time is therefore of prime importance in the complex exercises of market surveillance, risk management, and policy action. This chapter provides an R implementation of the popular real-time monitoring strategy proposed by Phillips, Shi and Yu in the International Economic Review (2015), along with a new bootstrap procedure designed to mitigate the potential impact of heteroskedasticity and to effect family-wise size control in recursive testing algorithms. This methodology has been shown effective for bubble and crisis detection and is now widely used by academic researchers, central bank economists, and fiscal regulators. We illustrate the effectiveness of this procedure with applications to the S&P financial market and the European sovereign debt sector using the psymonitor R package developed in conjunction with this chapter.

Expert's opinion impact on financial risk management

The present work proposes to incorporate expert's opinions into the traditional calculation of financial risk management models. These traditional quantitative models ignore qualitative knowledge available (subjective and mathematically imprecise). To do this, this paper articulates value-at-risk (VaR) practioner's perspective with experts' opinion. This work focuses on the techniques associated with the mathematical approach as opposed to the behavioural one and within this approach, contrasting those axiomatic with the Bayesian. The authors argue that expert's judgement is relevant to market risk management. In particular, financial risks are especially sensitive to decision makers' opinions. Three strategies are proposed. Firstly, Bayesian techniques, secondly, fuzzy logic with VaR approximation through triangular fuzzy numbers (TFN) and thirdly, expert discussion through Delphi dynamics are considered.

Influence of green supply chain risk management on performance of Chinese manufacturing enterprises

This paper briefly introduces the background of the research on the impact of green supply chain risk management on corporate performance, reviews the relevant research literature at home and abroad, and uses the gray relational analysis to analyze the impact of the green supply chain risk management on enterprise performance based on 26 industry-related statistical data, from purchasing risk management performance,manufacturing risk management performance and marketing risk management performance.

Automatic Portmanteau Tests with Applications to Market Risk Management

In this article, we review some recent advances in testing for serial correlation, provide code for implementation, and illustrate this code's application to market risk forecast evaluation. We focus on the classic and widely used portmanteau tests and their data-driven versions. These tests are simple to implement for two reasons: First, the researcher does not need to specify the order of the tested autocorrelations, because the test automatically chooses this number. Second, its asymptotic null distribution is chi-squared with one degree of freedom, so there is no need to use a bootstrap procedure to estimate the critical values. We illustrate the wide applicability of this methodology with applications to forecast evaluation for market risk measures such as value-at-risk and expected shortfall.