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19 pages, 433 KiB

Open AccessArticle

Analyzing Size of Loss Frequency Distribution Patterns: Uncovering the Impact of the COVID-19 Pandemic

by Shengkun Xie and Yuanshun Li

Risks 2024, 12(2), 40; https://doi.org/10.3390/risks12020040 - 18 Feb 2024

Viewed by 1654

Abstract

This study delves into a critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of [...] Read more.

This study delves into a critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviours and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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26 pages, 1180 KiB

Open AccessArticle

Stochastic Modeling of Wind Derivatives with Application to the Alberta Energy Market

by Sudeesha Warunasinghe and Anatoliy Swishchuk

Risks 2024, 12(2), 18; https://doi.org/10.3390/risks12020018 - 23 Jan 2024

Viewed by 1877

Abstract

Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural [...] Read more.

Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of European put style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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20 pages, 745 KiB

Open AccessArticle

Multiscale Volatility Analysis for Noisy High-Frequency Prices

by Tim Leung and Theodore Zhao

Risks 2023, 11(7), 117; https://doi.org/10.3390/risks11070117 - 26 Jun 2023

Cited by 1 | Viewed by 1827

Abstract

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of [...] Read more.

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of volatility behaviors suitable for intraday price processes. Algorithms for numerical estimation from time series observations are then introduced, with a new Hurst exponent estimator proposed for the noisy fractional Brownian motion model. Using real-world high-frequency price data for a collection of U.S. stocks and ETFs, we estimate the parameters in the noisy fractional Brownian motion and illustrate how the volatility varies over different timescales. The Hurst exponent and noise level also exhibit an intraday pattern whereby the the noise ratio tends to be higher near market close. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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23 pages, 2132 KiB

Open AccessArticle

Regulation and De-Risking: Theoretical and Empirical Insights

by Lawrence Haar and Andros Gregoriou

Risks 2023, 11(6), 104; https://doi.org/10.3390/risks11060104 - 2 Jun 2023

Viewed by 2267

Abstract

The purpose of the Bank for International Settlements regulatory agenda, as implemented by financial regulators globally, has been to make banks safer and reduce the likelihood of systemic events. Using an original model of bank profit maximisation under a regulatory constraint, we statistically [...] Read more.

The purpose of the Bank for International Settlements regulatory agenda, as implemented by financial regulators globally, has been to make banks safer and reduce the likelihood of systemic events. Using an original model of bank profit maximisation under a regulatory constraint, we statistically examine how market risk exposure has interacted with financial performance and capital structure, to see if the Basel regulatory agenda concerning the quantity, quality and liquidity of capital, has prompted changes in banking behaviour as measured by exposure to market risk. Breaking new ground, we empirically explore how the regulatory agenda has affected the largest banks. We analyse if the regulatory agenda has succeeded in aligning the cost of capital with their exposure to market risk, measured by Value at Risk; or if regulations have induced changes to banking activities. We find rather than regulation inducing changes to the rate at which unchanged risk exposure is capitalised; it leads to changes in the nature of exposures. Risk has declined along with financial performance while the cost of capital is largely unchanged. A consequence of regulation may be to encourage the migration of riskier activities to organisations where it may be borne more cheaply. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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22 pages, 4519 KiB

Open AccessEditor’s ChoiceArticle

Context-Based and Adaptive Cybersecurity Risk Management Framework

by Henock Mulugeta Melaku

Risks 2023, 11(6), 101; https://doi.org/10.3390/risks11060101 - 31 May 2023

Cited by 9 | Viewed by 8774

Abstract

Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing [...] Read more.

Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing their common problems regarding cybersecurity culture, developing a cyber-risk management plan, and devising a more proactive and collaborative approach that is suitable according to their organization context. To this end, firstly various enterprise, Information Technology (IT), and cybersecurity risk management frameworks are thoroughly reviewed along with their advantages and limitations. Then, we propose a proactive cybersecurity risk management framework that is simple and dynamic, and that adapts according to the current threat and technology landscapes and organizational context. Finally, performance metrics to evaluate the framework are proposed. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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13 pages, 788 KiB

Open AccessArticle

A Compound Up-and-In Call like Option for Wind Projects Pricing

by Michele Bufalo, Antonio Di Bari and Giovanni Villani

Risks 2023, 11(5), 90; https://doi.org/10.3390/risks11050090 - 11 May 2023

Cited by 2 | Viewed by 1682

Abstract

Wind energy projects represent, currently, a valid opportunity to support United Nations Sustainable Development Goal 7. However, these projects can appear financially unattractive considering the unfavorable meteorological conditions, uncertain electricity market price, uncertain market demand, unpredictable project performance, riskiness of investment stages, etc. [...] Read more.

Wind energy projects represent, currently, a valid opportunity to support United Nations Sustainable Development Goal 7. However, these projects can appear financially unattractive considering the unfavorable meteorological conditions, uncertain electricity market price, uncertain market demand, unpredictable project performance, riskiness of investment stages, etc. This paper provides a real options pricing model applied for the evaluation of a wind farm project to include the uncertainty that can affect future performance. The methodology proposed uses a compound call option model with two barriers applied, respectively, to the twofold phase framework that would act as a sort of up-and-in barrier. The compound call option model allows us to valuate the managerial flexibility to proceed with the following investment stages depending on the success of the previous ones and, through the barriers, the methodology gives the investor the opportunity to consider some profitability thresholds below, past which the investment should be abandoned. We develop a discrete case methodology by using the binomial approach. A hypothetical case study is shown to implement the theoretical framework by using likely data. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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25 pages, 539 KiB

Open AccessArticle

Underwriting Cycles in Property-Casualty Insurance: The Impact of Catastrophic Events

by Annette Hofmann and Cristina Sattarhoff

Risks 2023, 11(4), 75; https://doi.org/10.3390/risks11040075 - 11 Apr 2023

Cited by 2 | Viewed by 3849

Abstract

This paper challenges the question of existence and predictability of underwriting cycles in the U.S. property and casualty insurance industry. Using an approach in the frequency domain, we demonstrate the existence of a hidden periodic component in annual aggregated loss ratios. The data [...] Read more.

This paper challenges the question of existence and predictability of underwriting cycles in the U.S. property and casualty insurance industry. Using an approach in the frequency domain, we demonstrate the existence of a hidden periodic component in annual aggregated loss ratios. The data support an underwriting cycle length of 8–9 years. Going beyond previous research and studying almost 30 years of quarterly underwriting data, we can improve forecasting performance by (dis)connecting cycles and catastrophic events. Superior out-of-sample forecast results from models with intervention variables flagging the time point of catastrophic outbreaks is achieved in terms of mean squared/absolute forecast errors. We evaluate model confidence sets containing the most accurate model with a certain confidence level. The analysis suggests that reliable forecasts can be achieved net of the irregular major peaks in loss distributions that arise from natural catastrophes as well as unusual “black swan” events. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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18 pages, 1156 KiB

Open AccessEditor’s ChoiceArticle

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

by Jan Reig Torra, Montserrat Guillen, Ana M. Pérez-Marín, Lorena Rey Gámez and Giselle Aguer

Risks 2023, 11(3), 57; https://doi.org/10.3390/risks11030057 - 9 Mar 2023

Cited by 5 | Viewed by 2510

Abstract

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also [...] Read more.

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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Review

Jump to: Research

20 pages, 1176 KiB

Open AccessFeature PaperReview

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

by Michel Dacorogna

Risks 2023, 11(5), 98; https://doi.org/10.3390/risks11050098 - 18 May 2023

Cited by 2 | Viewed by 2086

Abstract

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, [...] Read more.

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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