Search Results (942)

The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk (henceforth “golden strategy”) has only been studied if all the involved derivatives have the same underlying asset. This paper also considers multi-asset derivatives, gives practical methods to build multi-asset golden strategies for both the expected shortfall and the expectile risk measure, and shows that the use of multi-asset options makes the performance of the obtained golden strategy more efficient. Practical rules are given under the Black–Scholes–Merton multi-dimensional pricing model. Full article

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification. Full article

Expected Shortfall (ES) is a risk measure that is acquiring an increasingly relevant role in financial risk management. In contrast to Value-at-Risk (VaR), ES considers the severity of the potential losses and reflects the benefits of diversification. ES is often calculated using Historical Simulation (HS), i.e., using observed data without further processing into the formula for its calculation. This has advantages like being parameter-free and has been favored by some regulators. However, the usage of HS for calculating ES presents a potentially serious drawback: It strongly depends on the size of the sample of historical data, being typically reasonable sizes similar to the number of trading days in one year. Moreover, this relationship leads to systematic underestimation: the lower the sample size, the lower the ES tends to be. In this letter, we present examples of this phenomenon for representative stocks and bonds, illustrating how the values of the ES and their averages are affected by the number of chosen data points. In addition, we present a method to mitigate the errors in the ES due to a low sample size, which is suitable for both liquid and illiquid financial products. Our analysis is expected to provide financial practitioners with useful insights about the errors made using Historical Simulation in the calculation of the Expected Shortfall. This, together with the method that we propose to reduce the errors due to finite sample size, is expected to help avoid miscalculations of the actual risk of portfolios. Full article

Volatility recovery is of paramount importance in contemporary finance. Volatility levels are heavily used in risk and portfolio management. We employ the Hull–White one- and two-factor models to describe the market condition. We computationally recover the volatility term structure as a piecewise-linear function of time. For every maturity, a cost functional, defined as the squared differences between theoretical and market prices, is minimized and the respective linear part is reconstructed. On the last time steps, before each maturity, the derivative price is decomposed in order to make the minimization problem analytically solvable. The procedure works fast since only scalar values are obtained on each minimization. However, the predictor–corrector nature of the algorithm allows for the precise recovery of very complex volatility functions. An implicit scheme is used to solve the PDEs on bounded domains. The computational simulations with artificial and real data show that the proposed algorithm is stable, accurate and efficient. Full article

Using a structural VAR model, this paper investigates how oil price shocks and US uncertainty affect emerging market corporate bond returns. The key finding is that the response of emerging market corporate bond returns varies significantly depending on the underlying sources of oil price changes. Oil supply shocks generally have a negative impact on corporate bond returns, while aggregate demand and oil market-specific demand shocks lead to a temporary increase in returns, followed by a gradual fall. That is, when oil price increases are driven by stronger global economic activity or by speculative demand reflecting increased risk appetite, they can lead investors to search for higher yields in emerging markets, and thus raise corporate bond returns in the short term. Conversely, an unexpected rise in US uncertainty strengthens investors’ risk aversion and results in a substantial decline in emerging market corporate bond returns. These findings have crucial policy implications not only for portfolio strategies of global investors, but also for government authorities in emerging market economies. Full article

The exponential growth in popularity of ETFs over the last three decades has solidified ETFs as an essential component of many investors’ portfolios. Investor sentiment is one of the factors that influence market returns of ETFs during times of market volatility. This article highlights the gap in the literature by examining the role sentiment plays in ETF volatility and providing a more comprehensive understanding of how sentiment interacts with market conditions to affect ETF pricing in the South African context. This article aims to determine the effect of investor sentiment on JSE-listed ETF returns under changing market conditions. The study followed a quantitative methodology using monthly closing prices of seven JSE ETFs and an investor sentiment index. A sample period from October 2008 to December 2023 was used. For a more complex understanding of how sentiment evolved and influenced market regimes, the Markov regime-switching model was integrated with Principal Component Analysis. The results found that investor sentiment had a significant impact on most of the ETFs in both the bull and bear regimes. The bull market was more dominant than the bear market across the ETF returns. Therefore, investor sentiment affected the returns of JSE ETFs. Identifying the effect of investor sentiment on ETFs results in ETF portfolios being less affected by changing market conditions by using risk management techniques and diversifying across asset classes and investing methods. Full article

This study examines the effects of transitioning to hydrogen production in the National Capital Region (NCR) and Palawan Province, Philippines, focusing on technology, environment, and stakeholder impact. This research, conducted through a July 2022 survey, aimed to assess public awareness, knowledge, risk perception, and acceptance of hydrogen and its environmentally friendly variant, green hydrogen, infrastructure. Disparities were found between urban NCR and rural Palawan, with lower awareness in Palawan. Safety concerns were highlighted, with NCR respondents generally considering hydrogen production safe, while Palawan respondents had mixed feelings, particularly regarding nuclear-based hydrogen generation. This report emphasizes the potential ecological advantages of hydrogen technology but highlights potential issues concerning water usage and land impacts. It suggests targeted public awareness campaigns, robust safety assurance programs, regional pilot projects, and integrated environmental plans to facilitate the seamless integration of hydrogen technology into the Philippines’ energy portfolio. This collective effort aims to help the country meet climate action obligations, foster sustainable development, and enhance energy resilience. Full article

Understanding cross-correlation and information flow between stocks is crucial for stock market analysis. However, traditional methods often struggle to capture financial markets’ complex and multifaceted dynamics. This paper presents a robust combination of techniques, integrating three advanced methods: Multifractal Detrended Cross-Correlation Analysis (MFDCCA), transfer entropy (TE), and complex networks. To address inherent non-stationarity and noise in financial data, we employ Ensemble Empirical Mode Decomposition (EEMD) for preprocessing, which helps reduce noise and handle non-stationary effects. The application and effectiveness of this combination of techniques are demonstrated through examples, uncovering significant multifractal properties and long-range cross correlations among the stocks studied. This combination of techniques also captures the magnitude and direction of information flow between stocks. This holistic analysis provides valuable insights for investors and policymakers, enhancing their understanding of stock market behavior and supporting better-informed portfolio decisions and risk management strategies. Full article

In several countries, digital financial advisory services, particularly those supported by robo-advisors, are becoming increasingly popular in retail banking. These tools assist users with financial decisions such as risk assessment, portfolio selection, and rebalancing—all at a reduced cost. Recent studies suggest that, over time, robo-advisors could complement human financial advisors. Building on this research, which evaluates robo-advisors’ effectiveness in asset allocation, this study aims to assess the impact of this strategic shift on retail banks’ profitability. It compares the Canadian and French banking sectors, where robo-advisors were introduced in the 2010s. Results indicate that implementing robo-advisors enhances profitability in non-interest activities, with this effect being more pronounced in France than in Canada. Full article

This paper presents an integrated robotic trading strategy developed for the day-ahead energy market that includes different methods for time series analysis and forecasting, such as Detrended Fluctuation Analysis (DFA), Rescaled Range Analysis (R/S analysis), fractional derivatives, Long Short-Term Memory (LSTM) Networks, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. DFA and R/S analysis may capture the long-range dependencies and fractal features inherited by the nature of the electricity price time series and give information about persistence and variability in their behavior. Given this, fractional derivatives can be used to analyze price movements concerning the minor changes in price and time acceleration for that change, which makes the proposed framework more flexible for quickly changing market conditions. LSTM, from their perspective, may capture complex and non-linear dependencies, while SARIMA models may help handle seasonal trends. This integrated approach improves market signal interpretation and optimizes the market risk through adjustable stop-loss and take-profit levels which could lead to better portfolio performance. The proposed integrated strategy is based on actual data from the Bulgarian electricity market for the years 2017–2024. Findings from this research show how the combination of fractals with statistical and machine learning models can improve complex trading strategies implementation for the energy markets. Full article

The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or diversifiers in the portfolios of both OPEC and non-OPEC countries, focusing on their behavior during the COVID-19 pandemic. We employ a wavelet coherence approach to analyze the dynamic relationships between the variables. Portfolio optimization is conducted using CVaR to assess the effectiveness of these assets as safe havens, hedges, or diversification tools in mitigating financial risks during periods of heightened market volatility. The diversification benefits of commodities and Bitcoin in OPEC and non-OPEC stock portfolios decrease over time as their co-movement with stock markets increases. During the COVID-19 period, BTC did not act as a safe haven. However, gold served as a hedge for non-OPEC countries. Using CVaR, we found that BTC provides stronger diversification benefits than commodities, followed by gold. We examine the safe-haven role of Bitcoin and various commodities, specifically within the context of both OPEC and non-OPEC countries. Our study offers a more comprehensive analysis of how BTC and commodities function as portfolio assets during financial stress, providing valuable insights for investors and policymakers. Full article

The study examines the time-varying correlation and return spillover mechanism among developed (U.S.) and emerging (BRIC) stock markets during major crises from 2000 to 2023, namely the global financial crisis, COVID-19, and the Russia–Ukraine war. To do so, we used dynamic conditional correlation (DCC-GARCH) and time-varying parameter vector autoregression (TVP-VAR) models. This study finds that the nature of market crises plays a significant role in the interrelationship and return spillover mechanisms among the U.S. and BRIC stock markets. The interconnectedness of the stock markets was strengthened by crises such as the GFC and the COVID-19 pandemic. On the other hand, the Russia–Ukraine war temporarily disrupted the interrelationships between the markets. The study yields valuable insight to local and international investors in portfolio diversification and risk management strategies during market turbulence. Full article

Computational algorithms are systematically written instructions or steps used to solve logical and mathematical problems with computers. These algorithms are crucial to rapidly and efficiently analyzing complex data, especially in global optimization problems like portfolio investment optimization. Investment portfolios are created because investors seek high average returns from stocks and must also consider the risk of loss, which is measured using the value at risk (VaR). This study aims to develop a computational algorithm based on the metaheuristic particle swarm optimization (PSO) model, which can be used to solve global optimization problems in portfolio investment. The data used in the simulation of the developed computational algorithm consist of daily stock returns from the banking sector traded in the Indonesian capital market. The quantitative research methodology involves formulating an algorithm to solve the global optimization problem in portfolio investment with mathematical calculations and quantitative data analysis. The objective function is to maximize the mean-value-at-risk model for portfolio investment, with constraints on the capital allocation weights in each stock within the portfolio. The results of this study indicate that the adapted PSO algorithm successfully determines the optimal portfolio weight composition, calculates the expected return and VaR in the optimal portfolio, creates an efficient frontier surface graph, and establishes portfolio performance measures. Across 50 trials, the algorithm records an average expected return of 0.000737, a return standard deviation of 0.00934, a value at risk of 0.01463, and a Sharpe ratio of 0.0504. Further evaluation of the PSO algorithm’s performance shows high consistency in generating optimal portfolios with appropriate parameter selection. The novelty of this research lies in developing an accurate computational algorithm for determining the global optima of mean-value-at-risk portfolio investments, yielding precise, consistent results with relatively fast computation times. The contribution to users is an easy-to-use tool for computational analysis that can assist in decision-making for portfolio investment formation. Full article

This paper presents a systematic exploration of deep reinforcement learning (RL) for portfolio optimization and compares various agent architectures, such as the DQN, DDPG, PPO, and SAC. We evaluate these agents’ performance across multiple market signals, including OHLC price data and technical indicators, while incorporating different rebalancing frequencies and historical window lengths. This study uses six major financial indices and a risk-free asset as the core instruments. Our results show that CNN-based feature extractors, particularly with longer lookback periods, significantly outperform MLP models, providing superior risk-adjusted returns. DQN and DDPG agents consistently surpass market benchmarks, such as the S&P 500, in annualized returns. However, continuous rebalancing leads to higher transaction costs and slippage, making periodic rebalancing a more efficient approach to managing risk. This research offers valuable insights into the adaptability of RL agents to dynamic market conditions, proposing a robust framework for future advancements in financial machine learning. Full article

We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection. Based on data over the period of July 1992 to May 2020, we highlight the role of the global financial uncertainty factor in accurately forecasting gold price volatility relative to the benchmark GARCH-MIDAS-realized volatility model, with a dominant role of European financial uncertainties, and 36 out of the 42 regional financial market uncertainties. The forecasting performance of the global financial uncertainty factor is as good as an index of global economic conditions, with results based on a combination of these two models depicting evidence of complementary information. Moreover, the GARCH-MIDAS model with global financial uncertainty cannot be outperformed by the multivariate version of the GARCH-MIDAS framework, estimated using the adaptive LASSO, involving the top five developed and developing countries each, chosen based on their ability to explain the movements of overall global financial uncertainty. Our results imply that as financial uncertainties can improve the accuracy of the forecasts of gold returns volatility, it would help investors to design optimal portfolios to counteract financial risks. Also, as gold returns volatility reflects financial uncertainty, accurate forecasts of it would provide information about the future path of economic activity, and assist policy authorities in preventing possible economic slowdowns. Full article