Risks

24 pages, 2160 KiB

Open AccessArticle

Deciphering the Risk–Return Dynamics of Pharmaceutical Companies Using the GARCH-M Model

by Arvinder Kaur and Kavita Chavali

Risks 2025, 13(5), 87; https://doi.org/10.3390/risks13050087 (registering DOI) - 1 May 2025

This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. [...] Read more.

This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. The prediction of stock market volatility and associated risks is demonstrated by using the GARCH-M model. A sample is collected by clustering daily closing and opening prices from the official websites of the top ten pharmaceutical companies listed on the Bombay Stock Exchange for ten years, from 2012 to 2023. It is evident when using the GARCH-M model, which indicates pharma stock volatility clustering before the COVID-19 pandemic, that a significant relationship is present between risk and return and that these could cause future volatility and significant price movements. Before the COVID-19 pandemic, investors had time to adjust to market conditions, as the volatility was constant but less sensitive to transient shocks. Though it passed faster than ever, the COVID-19 pandemic produced significant market instability. The findings suggest that, especially before the COVID-19 pandemic, the high GARCH(-1) coefficients held Merton’s ICAPM, which maintains that past volatility shapes future returns. This sort of activity is compatible with the way financial markets usually operate. The findings suggest that volatility rose after the COVID-19 pandemic, but this was more because of changes in government policies and vaccines than because of regular market forces. Pricing patterns are dominated by stock interventions, liquidity constraints, and sentiments during a crisis period when volatility becomes irrelevant. Appropriate decision-making by individual investors, portfolio managers, and policymakers regarding the stock market is possible through effective prediction based on time-series analysis. The GARCH-M model is compatible with predicting future stock price changes efficiently. This study uniquely applies the GARCH-M model to the Indian pharmaceutical sector, offering valuable insights into stock volatility and risk–return dynamics, particularly during the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Risk Management for Capital Markets)

► Show Figures

Figure 1

20 pages, 502 KiB

Open AccessArticle

Cooperative Game Theory of Hierarchies: One Approach to Solving the Low-Risk Puzzle?

by Tobias Hiller

Risks 2025, 13(5), 86; https://doi.org/10.3390/risks13050086 (registering DOI) - 30 Apr 2025

Abstract

In this article, we extend the application of cooperative game theory to the so-called low-risk puzzle. Specifically, we apply concepts that consider hierarchies on the assets in the allocation of portfolio risk. These hierarchies have not previously been considered in portfolio risk allocation [...] Read more.

In this article, we extend the application of cooperative game theory to the so-called low-risk puzzle. Specifically, we apply concepts that consider hierarchies on the assets in the allocation of portfolio risk. These hierarchies have not previously been considered in portfolio risk allocation using cooperative game theory. We demonstrate our idea through a simulation study. Our results show that considering hierarchies can contribute to solving the low-risk puzzle. Our findings may advance further developments in portfolio theory. Full article

(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)

► Show Figures

Figure 1

23 pages, 4798 KiB

Open AccessArticle

Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

by Riyadh Mehdi, Ibrahim Elsiddig Ahmed and Elfadil A. Mohamed

Risks 2025, 13(5), 85; https://doi.org/10.3390/risks13050085 (registering DOI) - 30 Apr 2025

Abstract

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few [...] Read more.

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one focusing on banks in the Middle East and Africa, to employ the adaptive neural network-based fuzzy inference system (ANFIS) that combines neural networks and fuzzy logic systems. The significance of this study lies in its comprehensive coverage of major risks and performance variables and its application of highly technical, sophisticated, and precise AI techniques (ANFIS). The main findings indicate that credit risk, as measured by the non-performing loans (NPL) has significant impact on both ROE and EPS. Liquidity risk comes second in importance for ROE and EPS, with the loan-deposit ratio (LDR) being the dominant component. In contrast, liquidity risk is the most significant determinant of PER, followed by capital adequacy. Our results also show that CAR, LDR, and NPL are the most significant risk components of capital adequacy, liquidity, and credit risks, respectively. The study contributes to business knowledge by applying the ANFIS technique as an accurate predictor of risk rating. Future research will explore the relationship between risks and macroeconomic indicators and differences among countries. Full article

► Show Figures

Figure 1

14 pages, 362 KiB

Open AccessArticle

Optimizing Moral Hazard Management in Health Insurance Through Mathematical Modeling of Quasi-Arbitrage

by Lianlian Zhou, Anshui Li and Jue Lu

Risks 2025, 13(5), 84; https://doi.org/10.3390/risks13050084 (registering DOI) - 28 Apr 2025

Viewed by 105

Abstract

Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model [...] Read more.

Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model based on quasi-arbitrage conditions. The model optimizes health insurance design, focusing on the transition from Low-Deductible Health Plans (LDHPs) to High-Deductible Health Plans (HDHPs), and seeks to mitigate moral hazard by aligning the interests of both insurers and insured. Our analysis demonstrates how setting appropriate deductible levels and offering targeted premium reductions can encourage insured to adopt HDHPs while maintaining insurer profitability. The findings contribute to the theoretical framework of moral hazard mitigation in health insurance and offer actionable insights for policy design. Full article

(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)

16 pages, 498 KiB

Open AccessArticle

Can Unrealistic Optimism Among Consumers Precipitate Economic Recessions?

by Hyun-Soo Doh and Jiahao Pan

Risks 2025, 13(5), 83; https://doi.org/10.3390/risks13050083 (registering DOI) - 26 Apr 2025

Viewed by 186

Abstract

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism [...] Read more.

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism may rather accelerate recessions. Specifically, we develop a model in which consumers, under the influence of unrealistic optimism, believe that negative aggregate shocks will affect others but not themselves. This misjudgment leads to a premature fall in output prices, reducing production and triggering recessions. Additionally, we show that government intervention, when optimally timed, can mitigate the adverse effects of unrealistic optimism, offering important policy implications for stabilizing economies. By highlighting the possibility of optimism-induced downturns, this paper provides new insights into behavioral macroeconomics and offers a novel perspective on policy design. Full article

► Show Figures

Figure 1

28 pages, 2632 KiB

Open AccessFeature PaperArticle

A Neural Network Approach for Pricing Correlated Health Risks

by Alessandro G. Laporta, Susanna Levantesi and Lea Petrella

Risks 2025, 13(5), 82; https://doi.org/10.3390/risks13050082 - 24 Apr 2025

Viewed by 189

Abstract

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss [...] Read more.

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss the use of neural networks to set the price of health insurance coverage following the structure of a classical frequency-severity model. In particular, we propose negative multinomial neural networks to jointly model the frequency of possibly correlated medical claims and Gamma neural networks to estimate the expected claim severity. Using a case study based on real-world health insurance data, we highlight the overall better performance of the neural network models with respect to more established regression models, both in terms of accuracy (frequency models achieve an average out-of-sample deviance of 8.54 compared to 8.61 for classical regressions) and risk diversification, as indicated by the ABC lift metric, which is

5.62 \times 10^{- 3}

for neural networks versus

8.27 \times 10^{- 3}

for traditional models. Full article

(This article belongs to the Special Issue Market-Consistent Actuarial Valuation and Risk-Based Capital Assessment)

► Show Figures

Figure 1

50 pages, 6937 KiB

Open AccessArticle

The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today

by Nicolas Houlié

Risks 2025, 13(5), 81; https://doi.org/10.3390/risks13050081 (registering DOI) - 23 Apr 2025

Viewed by 98

Abstract

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, [...] Read more.

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

► Show Figures

Figure 1

12 pages, 2274 KiB

Open AccessArticle

A New Approach on Country Risk Monitoring

by Christos E. Kountzakis and Christos Floros

Risks 2025, 13(5), 80; https://doi.org/10.3390/risks13050080 - 22 Apr 2025

Viewed by 210

Abstract

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product [...] Read more.

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in terms of a spot value. Another index is the spot value of the percentage of the annual interest rate payments of the state to the owners of sovereign debt. These indexes provide an inefficient evaluation of the national debt and moreover they are sensitive in their calculative aspect. Hence, we propose another index of national debt evaluation, which is more realistic, since public debt is a part of the balance sheet of the state itself. Moreover, this index may be translated into growth variables of the national economy. Since Gross National Product relies on consumption of the Economy, more consumption implies an ’illusion’ about sovereign debt. On the other hand, this index has limits to its credibility because it depends on the size of the annual investments. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

Journal Menu

Journal Browser

Risks, Volume 13, Issue 5 (May 2025) – 8 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI