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16 pages, 2146 KiB

Open AccessArticle

Distributed Least-Squares Monte Carlo for American Option Pricing

by Lu Xiong, Jiyao Luo, Hanna Vise and Madison White

Risks 2023, 11(8), 145; https://doi.org/10.3390/risks11080145 - 08 Aug 2023

Viewed by 1826

Option pricing is an important research field in financial markets, and the American option is a common financial derivative. Fast and accurate pricing solutions are critical to the stability and development of the market. Computational techniques, especially the least squares Monte Carlo (LSMC) [...] Read more.

Option pricing is an important research field in financial markets, and the American option is a common financial derivative. Fast and accurate pricing solutions are critical to the stability and development of the market. Computational techniques, especially the least squares Monte Carlo (LSMC) method, have been broadly used in optimizing the pricing algorithm. This paper discusses the application of distributed computing technology to enhance the LSMC in American option pricing. Although parallel computing has been used to improve the LSMC method, this paper is the first to explore distributed computing technology for LSMC enhancement. Compared with parallel computing, distributed computing has several advantages, including reducing the computational complexity by the “divide and conquer” method, avoiding the complicated matrix transformation, and improving data privacy as well as security. Moreover, LSMC is suitable for distributed computing because the price paths can be simulated and regressed separately. This research aims to show how distributed computing, particularly the divide and conquer approach implemented by Apache Spark, can be used to improve the efficiency and accuracy of LSMC in American option pricing. This paper provides an innovative solution to the financial market and could contribute to the advancement of American option pricing research. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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32 pages, 2302 KiB

Open AccessEditor’s ChoiceArticle

Do Behavioral Biases Affect Investors’ Investment Decision Making? Evidence from the Pakistani Equity Market

by Zain UI Abideen, Zeeshan Ahmed, Huan Qiu and Yiwei Zhao

Risks 2023, 11(6), 109; https://doi.org/10.3390/risks11060109 - 06 Jun 2023

Cited by 5 | Viewed by 11688

Abstract

Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play [...] Read more.

Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play in the decision making process. We first document the empirical evidence to support that the behavioral biases and market anomalies are closely associated and that these two factors significantly influence the investors’ investment decision making. The additional analyses confirm the mediating roles of certain market anomalies in the association between the investors’ behavioral biases and their investment decision making. Furthermore, empirical evidence reveals that financial literacy moderates the association between behavioral biases and market anomalies, and eventually influences the investors’ investment decision making. Overall, although the results are inconclusive for the relationships between certain variables, our results highlight the importance of financial literacy in terms of optimal investment decision making of individuals and the stability of the overall stock market. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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16 pages, 899 KiB

Open AccessArticle

A Guaranteed-Return Structured Product as an Investment Risk-Hedging Instrument in Pension Savings Plans

by Zvika Afik, Elroi Hadad and Rami Yosef

Risks 2023, 11(6), 107; https://doi.org/10.3390/risks11060107 - 05 Jun 2023

Viewed by 1291

Abstract

This study proposes a structured product (SP) for hedging defined contribution pension fund members against capital market risk. Using Monte Carlo simulations on three different guaranteed returns to test the investment strategy of the SP against a balanced investment portfolio, we measure their [...] Read more.

This study proposes a structured product (SP) for hedging defined contribution pension fund members against capital market risk. Using Monte Carlo simulations on three different guaranteed returns to test the investment strategy of the SP against a balanced investment portfolio, we measure their performance across a wide variety of capital market returns and risk scenarios. The results show that the SP guarantees a minimal return on the pension savings portfolio and offers a higher portfolio return at a lower investment risk, compared with the balanced investment portfolio. We conclude that the SP may become popular among pension fund members, potentially leading to improved risk management, greater competition, and investment strategy innovations for defined contribution pension schemes. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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24 pages, 563 KiB

Open AccessArticle

On Valuation and Investments of Pension Plans in Discrete Incomplete Markets

by Michail Anthropelos and Evmorfia Blontzou

Risks 2023, 11(6), 103; https://doi.org/10.3390/risks11060103 - 01 Jun 2023

Viewed by 1113

Abstract

We study the valuation of a pension fund’s obligations in a discrete time and space incomplete market model. The market’s incompleteness stems from the non-replicability of the wage process that finances the pension plan through time. The contingent defined-benefit liability of the pension [...] Read more.

We study the valuation of a pension fund’s obligations in a discrete time and space incomplete market model. The market’s incompleteness stems from the non-replicability of the wage process that finances the pension plan through time. The contingent defined-benefit liability of the pension fund is a function of the wages, which can be seen as the payoff of a path-dependent derivative security. We apply the notion of the super-hedging value and propose its difference from the current pension’s fund capital as a measure of distance to liability hedging. The induced closed-form expressions of the values and the related investment strategies provide insightful comparative statistics. Furthermore, we use a utility-based optimization portfolio to point out that in cases of sufficient capital, the application of a subjective investment criterion may result in heavily different strategies than the super-hedging one. This means that the pension fund will be left with some liability risk, although it could have been fully hedged. Finally, we provide conditions under which the effect of a possible early exit leaves the super-hedging valuation unchanged. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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17 pages, 483 KiB

Open AccessArticle

Whoops! It Happened Again: Demand for Insurance That Covers Multiple Risks

by Liang Hong, Harris Schlesinger and Boyi Zhuang

Risks 2023, 11(4), 73; https://doi.org/10.3390/risks11040073 - 10 Apr 2023

Viewed by 1122

Abstract

This article studies insurance demand in a two-period framework in which an individual faces risks in both current and future periods. Models for insurance with and without the presence of endogenous saving are both discussed. In contrast to what most literature suggests, when [...] Read more.

This article studies insurance demand in a two-period framework in which an individual faces risks in both current and future periods. Models for insurance with and without the presence of endogenous saving are both discussed. In contrast to what most literature suggests, when decisions on insurance and saving are made separately, insurance alone does not always unambiguously reduce risk, and decision makers might demand more insurance when there is a positive loading on the premium than when the insurance price is actuarially fair. We compare the demand for insurance in our framework with that in a two-period model where risk is concentrated in the second period and derive the conditions under which these demands differ. We examine the effects of risk aversion and derive the conditions under which a more risk-averse individual demands more or less insurance. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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14 pages, 686 KiB

Open AccessArticle

Financial Literacy Confidence and Retirement Planning: Evidence from China

by Bingzheng Chen and Ze Chen

Risks 2023, 11(2), 46; https://doi.org/10.3390/risks11020046 - 15 Feb 2023

Cited by 3 | Viewed by 3908

Abstract

Though ample empirical evidence demonstrates the relationship between objective financial literacy and retirement planning, we have a limited understanding of the role of individuals’ subjective financial literacy in their retirement planning. In this study, we examine how individuals’ financial literacy confidence bias affects [...] Read more.

Though ample empirical evidence demonstrates the relationship between objective financial literacy and retirement planning, we have a limited understanding of the role of individuals’ subjective financial literacy in their retirement planning. In this study, we examine how individuals’ financial literacy confidence bias affects their retirement planning behaviors using survey data in China. Based on the difference between respondents’ subjective and objective financial literacy from survey data, we construct measures of individuals’ financial literacy overconfidence and underconfidence for empirical analysis. Our results document the critical role of individuals’ assessment of financial literacy in their retirement planning. We find that individuals’ financial literacy overconfidence (underconfidence) significantly promotes (demotes) their retirement planning behaviors. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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20 pages, 1941 KiB

Open AccessArticle

Commodity Prices after COVID-19: Persistence and Time Trends

by Manuel Monge and Ana Lazcano

Risks 2022, 10(6), 128; https://doi.org/10.3390/risks10060128 - 16 Jun 2022

Cited by 8 | Viewed by 5567

Abstract

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to [...] Read more.

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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22 pages, 2473 KiB

Open AccessArticle

Risk Assessment of Polish Joint Stock Companies: Prediction of Penalties or Compensation Payments

by Aleksandra Szymura

Risks 2022, 10(5), 102; https://doi.org/10.3390/risks10050102 - 12 May 2022

Cited by 3 | Viewed by 4581

Abstract

Corporate misconduct is a huge and widespread problem in the economy. Many companies make mistakes that result in them having to pay penalties or compensation to other businesses. Some of these cases are so serious that they take a toll on a company’s [...] Read more.

Corporate misconduct is a huge and widespread problem in the economy. Many companies make mistakes that result in them having to pay penalties or compensation to other businesses. Some of these cases are so serious that they take a toll on a company’s financial condition. The purpose of this paper was to create and evaluate an algorithm which can predict whether a company will have to pay a penalty and to discover what financial indicators may signal it. The author addresses these questions by applying several supervised machine learning methods. This algorithm may help financial institutions such as banks decide whether to lend money to companies which are not in good financial standing. The research is based on information contained in the financial statements of companies listed on the Warsaw Stock Exchange and NewConnect. Finally, different methods are compared, and methods which are based on gradient boosting are shown to have a higher accuracy than others. The conclusion is that the values of financial ratios can signal which companies are likely to pay a penalty next year. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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5 pages, 342 KiB

Open AccessOpinion

A Generalized Model for Pricing Financial Derivatives Consistent with Efficient Markets Hypothesis—A Refinement of the Black-Scholes Model

by Jussi Lindgren

Risks 2023, 11(2), 24; https://doi.org/10.3390/risks11020024 - 17 Jan 2023

Viewed by 2288

Abstract

This research article provides criticism and arguments why the canonical framework for derivatives pricing is incomplete and why the delta-hedging approach is not appropriate. An argument is put forward, based on the efficient market hypothesis, why a proper risk-adjusted discount rate should enter [...] Read more.

This research article provides criticism and arguments why the canonical framework for derivatives pricing is incomplete and why the delta-hedging approach is not appropriate. An argument is put forward, based on the efficient market hypothesis, why a proper risk-adjusted discount rate should enter into the Black-Scholes model instead of the risk-free rate. The resulting pricing equation for derivatives and, in particular, the formula for European call options is then shown to depend explicitly on the drift of the underlying asset, which is following a geometric Brownian motion. It is conjectured that with the generalized model, the predicted results by the model could be closer to real data. The adjusted pricing model could partly also explain the mystery of volatility smile. The present model also provides answers to many finance professionals and academics who have been intrigued by the risk-neutral features of the original Black-Scholes pricing framework. The model provides generally different fair values for financial derivatives compared to the Black-Scholes model. In particular, the present model predicts that the original Black-Scholes model tends to undervalue for example European call options. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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