Liquidity-Adjusted Value At Risk Research Articles

Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach

AbstractIn this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.

Forecasting liquidity‐adjusted VaR: A conditional EVT‐copula approach

AbstractThis study models the joint distribution of individual stock returns and bid‐ask spreads using combined EGARCH‐EVT and combined GP‐INGARCH‐EVT processes for the marginals, and bivariate copulas for the dependence structure. We use the proposed approach to first simulate returns and spreads of individual stocks from different countries and regions, and then forecast the Liquidity‐adjusted Value‐at‐Risk (L‐VaR) measure according to three types of L‐VaR models. The backtesting results suggest that the proposed simulated L‐VaR models perform better than the competing L‐VaR/VaR models in forecasting L‐VaR. It is also observed that the simulated L‐VaR models perform better than the competing L‐VaR/VaR models in predicting the economic downturn.

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

ESTIMASI RISIKO PASAR DENGAN LVaR DAN EXPECTED SHORTFALL MENGGUNAKAN SIMULASI MONTE CARLO

Value at Risk (VaR) is a statistical technique used to manage and calculate the level of financial risk within a certain period of time and a certain level of confidence. VaR can be adjusted to liquidity risk which is called Liquidity adjusted Value at Risk (LVaR). Another alternative calculation is the Expected Shortfall (ES) which is a loss beyond the confidence limit that can occur due to liquidity. This study aims to estimate market risk with LVaR and ES on a stock portfolio incorporated in the LQ45 index using a Monte Carlo simulation. Furthermore, back-testing is carried out using the Kupiec test. The data used in this study are two stocks that are included in the LQ45 index which have the largest sales volume in a period of three years, namely ANTM and BBRI shares. As a result, it was found that the stock portfolio of ANTM and BBRI in the initial fund of Rp. 10,000,000.00 with a 95% confidence level, obtained ES of Rp.496.470,00 per day and an LVaR value of Rp 499.174,00 per day. The ES model obtained is less accurate while the LVaR model is accurate.

Constrained optimization algorithms for the computation of investable portfolios analytics: evaluation of economic-capital parameters for performance measurement and improvement

PurposeThis paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.Design/methodology/approachThe author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.FindingsThe author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).Practical implicationsThe attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.Originality/valueWhile this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

Asset Allocation with Liquidity-Adjusted Market Risk Modeling: Empirical Relevance to Multiple-Asset Portfolios

In this paper, the author demonstrate a practical approach for measurement, management and control of market risk exposure for financial trading portfolios. This approach is based on the renowned concept of Liquidity-Adjusted Value at Risk (L-VaR) along with the creation of a software tool utilizing matrix-algebra technique under the notion of different correlation factors and liquidation horizons.

On the Evaluation of LVaR During the Closeout Liquidity Horizon

In this paper, we launch a practical modus-operandi for the assessment of potential market risk exposures for financial trading portfolios by providing an investment management perspective from the 2007-2009 global financial crisis. This proposed tactic is based on the renowned concept of Liquidity-Adjusted Value-at-Risk (LVaR) along with the innovation of a risk-engine algorithm that can estimate LVaR for both long and short-sales positions.

Evaluation of Optimal and Coherent Risk-Capital Structures under Adverse Market Outlooks

This paper proposes a reengineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (LVaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both long-only positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios with the aid of an appropriate scaling of the multiple assets’ LVaR matrix along with GARCH-M technique to forecast conditional volatility and expected return.

Incorporating Asset Liquidity Effects in Economic-Capital Modeling Algorithms

Recent turmoil in financial markets endorses the need for rigorous handling and integration of asset liquidity risk into Value-at-Risk (VaR) models. In this work we develop and test measures of certain kinds of asset liquidity risk that is useful for completing the definition of market risk and for predicting liquidity-adjusted VaR (LVaR) under adverse market conditions.

Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

PurposeThis paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.Design/methodology/approachThe implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.FindingsIn particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.Originality/valueThe optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

Evaluation of Optimal and Coherent Risk-Capital Structures Under Adverse Market Outlooks Using Machine Learning Techniques

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. This study analyses, from a fund manager’s perspective, the performance of liquidity adjusted risk modeling in obtaining optimal and coherent economic capital structures, subject to meaningful operational and financial constraints as specified by the fund manager. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both pure long positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios using machine learning techniques, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. The key methodological contribution is a different and less conservative liquidity scaling factor than the conventional root-t multiplier. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets by minimizing L-VaR subject to the constraints that the expected return, trading volume and liquidation horizon should meet the budget limits set by the fund manager. In addition, the paper illustrates how the modified L-VaR method can be used by an equity trading unit in a dynamic asset allocation framework for reporting risk exposure, optimizing economic capital, and assessing risk reduction alternatives. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on the L-VaR optimization procedure.

Assessing liquidity‐adjusted risk forecasts

AbstractIn this paper, we provide a thorough study on the relevance of liquidity‐adjusted value‐at‐risk (LVaR) and expected shortfall (LES) forecasts. We measure additional liquidity of an asset via the difference between its respective bid and ask prices and we assess the non‐normality of bid–ask spreads, especially in turbulent market times. The empirical assessment comprises German stocks in both calm and turmoil market times, and our results provide evidence that liquidity risk turns out to be crucial for the quality of regulatory risk assessment in turmoil market times. We find that a Cornish–Fisher approximation describes a sensible choice for LVaR forecasts whereas an extreme value approach results in adequate LES forecasts.

The Importance of Measuring Liquidity Risk with Smart Financial Applications

Over the past few days, alarm bells have been ringing for the risk of recession in the world’s leading economies (Germany, United Kingdom, Italy, Brazil and Mexico). Deceleration is affecting several regions in the world and might even become more widespread, exacerbating investor mistrust and financial market instability produced by the U.S.-China trade war, among other uncertainty factors. Investors have transferred part of their investment portfolios to safe haven assets, such as sovereign bonds, with investor mistrust at record lows in recent years. Managing your investment portfolio when an economic cycle is drawing to an end can be tricky. Even though some experts recommend rebalancing asset allocation, buying treasury bonds, focusing on raw materials, commodities or real estate investment, the most important thing is to learn how to manage risk. In the current context of uncertainty and adverse market conditions, assessing investment portfolio performance optimization, as well as liquidity risk, is crucial, as explained in the chapter “Theoretical and practical foundations of liquidity-adjusted value-at-risk (LVaR): optimization algorithms for portfolio selection and management” of the recently published book Expert Systems in Finance. Smart Financial Applications in Big Data Environments (Routledge, Taylor & Francis Group, 2019).

Asset Market Liquidity Risk Management Using Machine Learning Techniques

Asset market liquidity risk is a significant and perplexing subject and though the term market liquidity risk is used quite chronically in academic literature it lacks an unambiguous definition, let alone understanding of the proposed risk measures. To this end, this paper presents a review of contemporary thoughts and attempts vis-a-vis asset market/liquidity risk management. Furthermore, this research focuses on the theoretical aspects of asset liquidity risk and presents critically two reciprocal approaches to measuring market liquidity risk for individual trading securities, and discusses the problems that arise in attempting to quantify asset market liquidity risk at a portfolio level. This paper extends research literature related to the assessment of asset market/liquidity risk by providing a generalized theoretical modeling underpinning that handle, from the same perspective, market and liquidity risks jointly and integrate both risks into a portfolio setting without a commensurate increase of statistical postulations. As such, we argue that market and liquidity risk components are correlated in most cases and can be integrated into one single market/liquidity framework that consists of two interrelated sub-components. The first component is attributed to the impact of adverse price movements, while the second component focuses on the risk of variation in transactions costs due to bid-ask spreads and it attempts to measure the likelihood that it will cost more than expected to liquidate the asset position. We thereafter propose a concrete theoretical foundation and a new modeling framework that attempts to tackle the issue of market/liquidity risk at a portfolio level by combining two asset market/liquidity risk models. The first model is a re-engineered and robust liquidity horizon multiplier that can aid in producing realistic asset market liquidity losses during the unwinding period. The essence of the model is based on the concept of Liquidity-Adjusted Value-at-Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions. Conversely, the second model is related to the transactions cost of liquidation due to bid-ask spreads and includes an improved technique that tackles the issue of bid-ask spread volatility. As such, the model comprises a new approach to contemplating the impact of time-varying volatility of the bid-ask spread and its upshot on the overall asset market/liquidity risk using machine learning techniques.

High-Frequency Trading and Its Impact on Exogenous Liquidity Risk of China’s Stock Index Futures Market before and after Trading Restrictions

Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.

Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints

AbstractWe develop a model for optimizing multiple‐asset portfolios with semi‐parametric liquidity‐adjusted value‐at‐risk (LVaR), whereby linear correlations are substituted by the multivariate nonlinear Kendall's tau dependence measure, under multiple credible operational and budget constraints. When considering a diversified large portfolio of international stock markets of both developed and emerging economies and commodities, under both regular and stressed market perspectives, the obtained results consistently confirm the dominance of our modeling algorithms relative to other competing portfolio strategies, including the traditional mean‐variance VaR approach and global minimum‐variance portfolios. The obtained results are robust to different trading scenarios and promising for practical optimization techniques in large multiple‐asset portfolios and operation research models in financial institution management.

Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

Purpose This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios. Design/methodology/approach This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart. Findings To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach. Practical implications The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk. Originality/value The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach

This paper develops a novel approach to assess liquidity-adjusted Value-at-Risk (LVaR) optimization of multi-asset portfolios based on vine copulas and LVaR models. This framework is applied to stock markets of the G-7 countries, gold, commodities and Bitcoin. The results show that our approach is superior to the classical mean–variance Markowitz portfolio technique in terms of the optimal portfolio selection under a number of realistic operational and budget constraints. We find that both Bitcoin and gold improves the risk-return performance of the G-7 stock portfolio. However, Bitcoin (gold) performs better under a scenario of only long-positions (when short-selling is allowed).

Risk management for private equity funds

Although risk management has been a well-ploughed field in financial modeling for over three decades, the understanding how to correctly quantify and manage the risks of investing in private equity remains limited and continues to considerably lag that of other traditional asset classes. The objective of this paper is to fill this gap by developing the first comprehensive risk management framework for private equity fund investments. The framework captures the three main sources of risks that private equity investors face when investing in the asset class: market risk, liquidity risk, and cash flow risk. Underlying the framework is a stochastic model for the value and cash flow dynamics of private equity funds, which allows deriving three dynamic risk measures for private equity fund investments: Value-at-Risk (VaR), Liquidity-Adjusted Value-at-Risk (LVaR), and Cash-Flow-at-Risk (CFaR). The model is calibrated to historical data of buyout funds and the dynamics of the developed risk measures are illustrated using Monte-Carlo simulations. A sensitivity analysis shows the impact of changes in the main model parameters on risk measures.

Liquidity Adjusted Value at Risk: Integrating the Uncertainty in Depth and Tightness

Efficient market risk management should also focus on the market liquidity risk, which is generally ignored by the conventional Value at Risk Metrics. We propose two alternative parametric methods to existing studies for the estimation of Liquidity Adjusted Value at Risk (LVaR). The first model is based on the volatility dynamics of VNET (Engle and Lange, 2001) whereas the second model also incorporates the first two moments of the tightness dimension to the latter, as measured by relative weighted bid-ask spreads. Considering a portfolio with different underlying volatility assumptions (EWMA, GARCH-CCC, GARCH-DCC), validation results indicate that both parametric LVaR approaches are strong alternatives to tightness based LVaR models and strictly superior to conventional VaR models, with respect to performance related to regulatory compliance, statistical coverage and overall relative cost of liquidity vs. loss size on violation days.