Journal of Risk and Financial Management

Research

4 pages, 241 KiB

Open AccessCommunication

by Lev Klebanov and Svetlozar T. Rachev

J. Risk Financial Manag. 2023, 16(4), 251; https://doi.org/10.3390/jrfm16040251 - 20 Apr 2023

Cited by 3 | Viewed by 796

A new class of probability distributions closely connected to generalized hyperbolic distributions is introduced. It is better adapted for studying the distributions of sums of a random number of random variables. The properties of these distributions are studied. It seems that this class [...] Read more.

A new class of probability distributions closely connected to generalized hyperbolic distributions is introduced. It is better adapted for studying the distributions of sums of a random number of random variables. The properties of these distributions are studied. It seems that this class may be useful for modeling asset returns. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

28 pages, 1923 KiB

Open AccessArticle

Pricing European Options under Stochastic Volatility Models: Case of Five-Parameter Variance-Gamma Process

by Aubain Hilaire Nzokem

J. Risk Financial Manag. 2023, 16(1), 55; https://doi.org/10.3390/jrfm16010055 - 16 Jan 2023

Cited by 2 | Viewed by 2539

Abstract

The paper builds a Variance-Gamma (VG) model with five parameters: location (

μ

), symmetry (

δ

), volatility (

σ

), shape (

α

), and scale (

θ

); and studies its application to the pricing of European options. The results [...] Read more.

The paper builds a Variance-Gamma (VG) model with five parameters: location (

μ

), symmetry (

δ

), volatility (

σ

), shape (

α

), and scale (

θ

); and studies its application to the pricing of European options. The results of our analysis show that the five-parameter VG model is a stochastic volatility model with a

Γ (α, θ)

Ornstein–Uhlenbeck type process; the associated Lévy density of the VG model is a KoBoL family of order

ν = 0

, intensity

α

, and steepness parameters

\frac{δ}{σ^{2}} - \sqrt{\frac{δ^{2}}{σ^{4}} + \frac{2}{θ σ^{2}}}

and

\frac{δ}{σ^{2}} + \sqrt{\frac{δ^{2}}{σ^{4}} + \frac{2}{θ σ^{2}}}

; and the VG process converges asymptotically in distribution to a Lévy process driven by a normal distribution with mean

(μ + α θ δ)

and variance

α (θ^{2} δ^{2} + σ^{2} θ)

. The data used for empirical analysis were obtained by fitting the five-parameter Variance-Gamma (VG) model to the underlying distribution of the daily SPY ETF data. Regarding the application of the five-parameter VG model, the twelve-point rule Composite Newton–Cotes Quadrature and Fractional Fast Fourier (FRFT) algorithms were implemented to compute the European option price. Compared to the Black–Scholes (BS) model, empirical evidence shows that the VG option price is underpriced for out-of-the-money (OTM) options and overpriced for in-the-money (ITM) options. Both models produce almost the same option pricing results for deep out-of-the-money (OTM) and deep-in-the-money (ITM) options. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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18 pages, 2105 KiB

Open AccessArticle

Time Dependence of CAPM Betas on the Choice of Interval Frequency and Return Timeframes: Is There an Optimum?

by Pankaj Agrrawal, Faye W. Gilbert and Jason Harkins

J. Risk Financial Manag. 2022, 15(11), 520; https://doi.org/10.3390/jrfm15110520 - 07 Nov 2022

Cited by 6 | Viewed by 3780

Abstract

The traditional CAPM beta is almost exclusively calculated over a return period that spans a window length of 60-months, at one-month return frequencies. It is one of the most utilized models in the asset management industry to assess systematic risk. Yet there is [...] Read more.

The traditional CAPM beta is almost exclusively calculated over a return period that spans a window length of 60-months, at one-month return frequencies. It is one of the most utilized models in the asset management industry to assess systematic risk. Yet there is limited evidence to suggest that these estimation parameters are optimal. Utilizing data between January 2000 and December 2021 for the Russell 1000 index, we test daily, weekly, and monthly beta estimations to calculate tracking errors (TE) for the use of these betas in predicting subsequent performance over daily, weekly, and monthly timeframes. We identify that daily CAPM betas are best for predicting subsequent period daily returns and that weekly CAPM betas are strongly correlated with forward weekly and monthly period returns. Leveraging the significant advances in computing resources and the increasing utilization of high frequency trading strategies, we argue that additional window length and return interval-based CAPM betas should be calculated for estimating the systematic risk embedded in diversified portfolios. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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15 pages, 617 KiB

Open AccessArticle

Does Limited Liability Reduce Leveraged Risk?: The Case of Loan Portfolio Management

by Deb Narayan Barik and Siddhartha P. Chakrabarty

J. Risk Financial Manag. 2022, 15(11), 519; https://doi.org/10.3390/jrfm15110519 - 07 Nov 2022

Cited by 1 | Viewed by 1476

Abstract

Return–risk models are the two pillars of modern portfolio theory, which are widely used to make decisions in choosing the loan portfolio of a bank. Banks and other financial institutions are subjected to limited-liability protection. However, in most of the model formulation, limited [...] Read more.

Return–risk models are the two pillars of modern portfolio theory, which are widely used to make decisions in choosing the loan portfolio of a bank. Banks and other financial institutions are subjected to limited-liability protection. However, in most of the model formulation, limited liability is not taken into consideration. Accordingly, to address this, we have, in this article, analyzed the effect of including it in the model formulation. We formulate four models, two of which are maximizing the expected return with risk constraint, including and excluding limited liability, and other two of which are minimizing of risk with threshold level of return with and without limited liability. Our theoretical results show that the solutions of the models with limited liability produce better results than the others, in both minimizing risk and maximizing expected return. More specifically, the portfolios that included limited liability are less risky as compared to the portfolios that did not include limited liability. Finally, an illustrative example is presented to support the theoretical results obtained. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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19 pages, 438 KiB

Open AccessArticle

Revisiting the Long-Run Dynamic Linkage between Dividends and Share Price with Advanced Panel Econometrics Techniques

by Sudatta Bharati Mohapatra and Nirmal Chandra Kar

J. Risk Financial Manag. 2022, 15(10), 486; https://doi.org/10.3390/jrfm15100486 - 21 Oct 2022

Cited by 1 | Viewed by 1724

Abstract

The log-linearized present value model (PVM) has been widely used in corporate finance to understand the long-run relationship between share price and dividends using panel data. However, the application of recently established panel econometric approaches that account for slope heterogeneity and cross-section dependency [...] Read more.

The log-linearized present value model (PVM) has been widely used in corporate finance to understand the long-run relationship between share price and dividends using panel data. However, the application of recently established panel econometric approaches that account for slope heterogeneity and cross-section dependency in the recent literature regarding the long-run link between share price and dividends in an Indian setting is limited. This paper re-examines the log-linearized PVM in an Indian setting using newly developed panel unit root, cointegration, and long-run dynamic estimation approaches. This study employed a panel dataset of 60 Bombay Stock Exchange (BSE)-listed Indian firms paying regular dividends for 28 years (1990–2017). The study found unit root, cointegration, and a long-run relationship between dividend and share price series for Indian firms during a 28-year sample period. By demonstrating the presence of a long-run link between share price and dividends, this paper contributes to the literature on the PVM, which is crucial in comprehending market rationality and share price behavior in India. This paper also discusses issues related to panel data, such as cross-section dependency and slope heterogeneity, as well as panel econometric approaches that can be applied in the appropriate settings. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

18 pages, 470 KiB

Open AccessArticle

Pricing Cat Bonds for Cloud Service Failures

by Loretta Mastroeni, Alessandro Mazzoccoli and Maurizio Naldi

J. Risk Financial Manag. 2022, 15(10), 463; https://doi.org/10.3390/jrfm15100463 - 15 Oct 2022

Cited by 1 | Viewed by 2270

Abstract

The use of the cloud to store personal/company data and to run programs is gaining wide acceptance as it is more efficient and cost-effective. However, cloud services may not always be available, which could lead to losses for customers and the cloud provider [...] Read more.

The use of the cloud to store personal/company data and to run programs is gaining wide acceptance as it is more efficient and cost-effective. However, cloud services may not always be available, which could lead to losses for customers and the cloud provider (the provider is typically obligated to compensate its customers). It can protect itself from such losses through insurance, which transfers the risk to the insurer. In the case of poor cloud availability, the amount that the insurer has to pay back to the cloud provider may become so high that it endangers the insurer’s financial solvency. We propose the use of cat bonds as reinsurance tools as well as the Nowak–Romaniuk pricing scheme. The outage frequency was described by the Poisson process and the loss severity was described by a Pareto random variable; we derived a closed formula for the price of a cat bond in a stochastic interest rate environment, using both one-factor and two-factor short-rate models. We demonstrated the applicability of our pricing formula in a real context. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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21 pages, 563 KiB

Open AccessArticle

Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence

by Weijia Peng and Chun Yao

J. Risk Financial Manag. 2022, 15(8), 334; https://doi.org/10.3390/jrfm15080334 - 28 Jul 2022

Cited by 1 | Viewed by 1982

Abstract

This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset, which indicates [...] Read more.

This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset, which indicates that volatilities from one sector are largely derived from the contagious effect of other sectors. We further investigated the importance of idiosyncratic jumps and co-jumps to predict the sector-level S&P500 exchange-traded fund (ETF) volatility. It was found that the predictive content of co-jumps is higher than that of idiosyncratic jumps, suggesting that systematic risk is more informative than idiosyncratic risk in volatility forecasting. Additionally, we carried out Monte Carlo experiments designed to examine the relative performances of the four co-jump tests. The findings indicate that the BLT test and the co-exceedance rule of the LM test outperform other tests, while the co-exceedance rule of the LM test has larger power and a smaller empirical size than that of the BLT test. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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18 pages, 7071 KiB

Open AccessArticle

Cap Rates as a Function of Real Economic Growth

by Matt Larriva

J. Risk Financial Manag. 2022, 15(8), 324; https://doi.org/10.3390/jrfm15080324 - 22 Jul 2022

Viewed by 2500

Abstract

This study investigates the interactive effect of changes in the Gross Domestic Product (GDP) and the Consumer Price Index (CPI) on US multifamily cap rates. The data from the US and 20 of its metropolitan statistical areas (MSAs) was used from 2000 to [...] Read more.

This study investigates the interactive effect of changes in the Gross Domestic Product (GDP) and the Consumer Price Index (CPI) on US multifamily cap rates. The data from the US and 20 of its metropolitan statistical areas (MSAs) was used from 2000 to 2021. The accompanying cap rate data is sourced to Green Street. A binary logistic regression model was specified by reducing the interaction between first-differenced GDP and CPI to a single binary variable and reducing the first-differenced cap rate series to a binary variable. Cap rate changes are forecasted, and the model is evaluated using standard goodness of fit methods, a confusion matrix, and a comparison to a buy-and-hold strategy. Overall, this study provides new evidence to explain and simplify the impact of inflation and economic growth on cap rates. The results show that the method of forecasting cap rates is highly robust in locations where growth is consistent with the national average (established cities), while it is less robust in fast-growing markets. It can be inferred that, in established cities and the US as a whole, cap rate growth can be modeled as a function of only the underlying GDP growth relative to CPI growth. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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10 pages, 304 KiB

Open AccessArticle

Performance Evaluation of Utility Exchange-Traded Funds: A Super-Efficiency Approach

by Ioannis E. Tsolas

J. Risk Financial Manag. 2022, 15(7), 318; https://doi.org/10.3390/jrfm15070318 - 21 Jul 2022

Cited by 3 | Viewed by 1710

Abstract

Choosing funds is a general issue for investors, with the aim of balancing potential risks and returns. The aim of this article is to use a super-efficiency approach to analyze and rank exchange-traded funds (ETFs) in order to find the best utility ETFs. [...] Read more.

Choosing funds is a general issue for investors, with the aim of balancing potential risks and returns. The aim of this article is to use a super-efficiency approach to analyze and rank exchange-traded funds (ETFs) in order to find the best utility ETFs. The range-adjusted measure (RAM)-based data envelopment analysis (DEA) model is used in this work to evaluate a set of utility ETFs and rank inefficient funds, while the super-efficiency RAM model is used to fully rank RAM-based efficient funds. Other slack-based selected DEA models are also used to analyze the ETFs. The results show that the suggested approach delivers the same efficient funds as other slack-based selected DEA models; hence, it appears to be useful as a fund selection tool. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

19 pages, 336 KiB

Open AccessArticle

National Governance Index, Corruption Index and Growth Rate—International Evidence from Sub-Saharan and MENA Countries

by Omar Al Farooque, Ali Hamid and Lan Sun

J. Risk Financial Manag. 2022, 15(6), 261; https://doi.org/10.3390/jrfm15060261 - 09 Jun 2022

Cited by 1 | Viewed by 3113

Abstract

In an international setting of developing countries, applying advanced statistical estimation approaches such as the system generalized method of moments (GMM), two-stage least square (2SLS) regressions, and cluster analysis, this paper revisits the impact of macro-level governance quality and the corruption index on [...] Read more.

In an international setting of developing countries, applying advanced statistical estimation approaches such as the system generalized method of moments (GMM), two-stage least square (2SLS) regressions, and cluster analysis, this paper revisits the impact of macro-level governance quality and the corruption index on the economic growth rate. We use cross-country panel data for 40 sub-Saharan and the Middle Eastern and North African (MENA) countries over the period of 2009–2020. The empirical results document the positive and negative effects of the national governance index and the corruption index on the economic growth rate. Additionally, foreign direct investment and population have a positive impact on the economic growth rate and trade openness has a negative impact. The study evaluates the robustness of these associations through a series of tests. These findings have important policy implications for policymakers and regulators in developing countries. In particular, the study recommends the implementation of an anti-corruption campaign and improving country-level governance quality that could encourage increased foreign direct investment for an accelerated economic growth rate. These will further enhance accountability, transparency, the rule of law, social responsibility, the public voice, and government effectiveness. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

23 pages, 326 KiB

Open AccessArticle

The Pricing Model of Pension Benefit Guaranty Corporation Insurance with Regime-Switching Processes

by Ting-Fu Chen, Shih-Kuei Lin, An-Sing Chang and Wei-Hao Wang

J. Risk Financial Manag. 2022, 15(6), 258; https://doi.org/10.3390/jrfm15060258 - 09 Jun 2022

Viewed by 1553

Abstract

This paper aims to evaluate Pension Benefit Guaranty Corporation (PBGC) insurance values through regime-switching models. We separate periods of the economy with faster growth from those with slower growth to observe long-term trends in the economy. We derive a fair PBGC insurance pricing [...] Read more.

This paper aims to evaluate Pension Benefit Guaranty Corporation (PBGC) insurance values through regime-switching models. We separate periods of the economy with faster growth from those with slower growth to observe long-term trends in the economy. We derive a fair PBGC insurance pricing formula under distress termination and intervention termination using regime-switching processes. We set parameters by estimating the S&P 500 index and one-year treasury bills via expectation maximization particle swarm optimization (EM-PSO)-Gradient, which is an extension of the EM-Gradient method. Then, we conduct sensitivity analysis to investigate the impact of model parameters on insurance values. According to the maximum likelihood estimation results, the Akaike information criterion (AIC) and Bayesian information criterion (BIC) estimators show that the regime-switching process has better goodness of fit than the geometric Brownian motion. Scenario analysis also supports the adequacy of our pricing formula. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

18 pages, 618 KiB

Open AccessArticle

The Generalized Gamma Distribution as a Useful RND under Heston’s Stochastic Volatility Model

by Benzion Boukai

J. Risk Financial Manag. 2022, 15(6), 238; https://doi.org/10.3390/jrfm15060238 - 26 May 2022

Cited by 1 | Viewed by 1803

Abstract

We present the Generalized Gamma (GG) distribution as a possible risk neutral distribution (RND) for modeling European options prices under Heston’s stochastic volatility (SV) model. We demonstrate that under a particular reparametrization, this distribution, which is a member of the scale-parameter family of [...] Read more.

We present the Generalized Gamma (GG) distribution as a possible risk neutral distribution (RND) for modeling European options prices under Heston’s stochastic volatility (SV) model. We demonstrate that under a particular reparametrization, this distribution, which is a member of the scale-parameter family of distributions with the mean being the forward spot price, satisfies Heston’s solution and hence could be used for the direct risk-neutral valuation of the option price under Heston’s SV model. Indeed, this distribution is especially useful in situations in which the spot’s price follows a negatively skewed distribution for which Black–Scholes-based (i.e., the log-normal distribution) modeling is largely inapt. We illustrate the applicability of the GG distribution as an RND by modeling market option data on three large market-index exchange-traded funds (ETF), namely the SPY, IWM and QQQ as well as on the TLT (an ETF that tracks an index of long-term US Treasury bonds). As of the writing of this paper (August 2021), the option chain of each of the three market-index ETFs shows a pronounced skew of their volatility ‘smile’, which indicates a likely distortion in the Black–Scholes modeling of such option data. Reflective of entirely different market expectations, this distortion in the volatility ‘smile’ appears not to exist in the TLT option data. We provide a thorough modeling of the option data we have on each ETF (with the 15 October 2021 expiration) based on the GG distribution and compare it to the option pricing and RND modeling obtained directly from a well-calibrated Heston’s SV model (both theoretically and also empirically, using Monte Carlo simulations of the spot’s price). All three market-index ETFs exhibited negatively skewed distributions, which are well-matched with those derived under the GG distribution as RND. The inadequacy of the Black–Scholes modeling in such instances, which involves negatively skewed distribution, is further illustrated by its impact on the hedging factor, delta, and the immediate implications to the retail trader. Similarly, the closely related Inverse Generalized Gamma distribution (IGG) is also proposed as a possible RND for Heston’s SV model in situations involving positively skewed distribution. In all, utilizing the Generalized Gamma distributions as possible RNDs for direct option valuations under the Heston’s SV is seen as particularly useful to the retail traders who do not have the numerical tools or the know-how to fine-calibrate this SV model. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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23 pages, 611 KiB

Open AccessArticle

Portfolio Optimization on Multivariate Regime-Switching GARCH Model with Normal Tempered Stable Innovation

by Cheng Peng, Young Shin Kim and Stefan Mittnik

J. Risk Financial Manag. 2022, 15(5), 230; https://doi.org/10.3390/jrfm15050230 - 23 May 2022

Viewed by 2773

Abstract

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. [...] Read more.

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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19 pages, 869 KiB

Open AccessArticle

Dynamic and Static Volatility Interruptions: Evidence from the Korean Stock Markets

by Kyong Shik Eom, Kyung Yoon Kwon, Sung Chae La and Jong-Ho Park

J. Risk Financial Manag. 2022, 15(3), 105; https://doi.org/10.3390/jrfm15030105 - 25 Feb 2022

Viewed by 2596

Abstract

We conducted a comprehensive analysis on the sequential introductions of dynamic and static volatility interruptions (VIs) in the Korean stock markets. The Korea Exchange introduced VIs to improve price formation, and to limit risk to investors from brief periods of abnormal volatility for [...] Read more.

We conducted a comprehensive analysis on the sequential introductions of dynamic and static volatility interruptions (VIs) in the Korean stock markets. The Korea Exchange introduced VIs to improve price formation, and to limit risk to investors from brief periods of abnormal volatility for individual stocks. We found that dynamic VI is effective in price stabilization and discovery, while the effect of static VI is limited. The static VI functions similarly to the pre-existing price-limit system; this accounts for its limited incremental benefit. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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25 pages, 649 KiB

Open AccessArticle

The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence

by Imene Ben El Hadj Said and Skander Slim

J. Risk Financial Manag. 2022, 15(2), 66; https://doi.org/10.3390/jrfm15020066 - 01 Feb 2022

Cited by 3 | Viewed by 2877

Abstract

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three volatility [...] Read more.

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three volatility components, defined over different time horizons, using the similarity measure between lagged Google search queries and volatility. Results show that investor attention positively affects future volatility in the short-run. The effect of investor attention is likely to reverse in the long-run, consistently with the price pressure hypothesis. The proposed model demonstrates important gains in terms of volatility forecast accuracy and outperforms highly competitive models. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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25 pages, 395 KiB

Open AccessArticle

Maximum Drawdown, Recovery, and Momentum

by Jaehyung Choi

J. Risk Financial Manag. 2021, 14(11), 542; https://doi.org/10.3390/jrfm14110542 - 11 Nov 2021

Cited by 3 | Viewed by 3057

Abstract

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of [...] Read more.

We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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17 pages, 2017 KiB

Open AccessArticle

Multi-Asset Value Payoff: Is Recent Underperformance Cyclical?

by Yesim Tokat-Acikel, Marco Aiolfi and Yiwen Jin

J. Risk Financial Manag. 2021, 14(10), 477; https://doi.org/10.3390/jrfm14100477 - 11 Oct 2021

Viewed by 2340

Abstract

Recent value factor underperformance has called into question whether the value factor payoff is cyclically low, or if there are more structural challenges. We use a new approach to explore a link between the well-known macroeconomic exposures of traditional asset classes and those [...] Read more.

Recent value factor underperformance has called into question whether the value factor payoff is cyclically low, or if there are more structural challenges. We use a new approach to explore a link between the well-known macroeconomic exposures of traditional asset classes and those of value premia in a multi-asset context, focusing on country equities, bonds, and currencies in developed markets. Taking advantage of the cross-country inflation and growth expectations implicit in every value portfolio, we derive the net inflation and real growth characteristics embedded in each asset class carry portfolio at each point in time. Our analysis provides several insights: (1) Multi-asset value payoff is only weakly related to the global business cycle. (2) However, we find that the payoff to value portfolios is strongly linked to relative growth and inflation expectations across countries. (3) Over the last decade, we find that cheaper assets have had much lower net relative macro exposures compared to earlier time periods. This characteristic coincides with the period of unconventional central bank policies designed to lift global growth after the Global Financial Crisis (GFC). Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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16 pages, 805 KiB

Open AccessArticle

Global Index on Financial Losses Due to Crime in the United States

by Thilini Mahanama, Abootaleb Shirvani and Svetlozar T. Rachev

J. Risk Financial Manag. 2021, 14(7), 315; https://doi.org/10.3390/jrfm14070315 - 09 Jul 2021

Viewed by 2338

Abstract

Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the [...] Read more.

Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the financial losses reported by the Federal Bureau of Investigation. The objective of our paper is to introduce new risk hedging financial contracts for crime, consistent with dynamic asset pricing. Underlying the index, we hedge the investments by issuing marketable European call and put options and providing risk budgets. These budgets show that real estate, ransomware, and government impersonation are the main risk contributors in our index. Next, we evaluate the performance of our index via stress testing to determine its resilience to economic crisis. Of all the factors considered in this study, unemployment rate has the potential to demonstrate the highest systemic risk to the portfolio. Our portfolio will help investors envision risk exposure in the market, gauge investment risk based on their desired risk level, and hedge strategies for potential losses due to economic crashes. In conclusion, we provide a basis for the securitization of insurance risk from certain crimes that could forewarn investors to transfer their risk to capital market investors. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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20 pages, 9131 KiB

Open AccessArticle

Systemic Risk Modeling with Lévy Copulas

by Yuhao Liu, Petar M. Djurić, Young Shin Kim, Svetlozar T. Rachev and James Glimm

J. Risk Financial Manag. 2021, 14(6), 251; https://doi.org/10.3390/jrfm14060251 - 05 Jun 2021

Cited by 2 | Viewed by 3168

Abstract

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula [...] Read more.

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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33 pages, 2478 KiB

Open AccessArticle

Insuring Hollywood: A Movie Returns Index and the American Stock Market

by Davide Lauria and Wyatt D. Phillips

J. Risk Financial Manag. 2021, 14(5), 189; https://doi.org/10.3390/jrfm14050189 - 21 Apr 2021

Cited by 1 | Viewed by 8698

Abstract

The aim of this paper is the definition of a daily index representing the risk-return on investments in the American film industry. The index should be used to predict the riskiness and the expected return of movie projects at the level of the [...] Read more.

The aim of this paper is the definition of a daily index representing the risk-return on investments in the American film industry. The index should be used to predict the riskiness and the expected return of movie projects at the level of the overall industry and then to determine a premium for insurance for such an investment. Such an index can inform the decision making in relation to risk but also timing. Though not currently legal in the United States, such an index may be relevant at some point in the future or in other countries for film production companies as well as venture capitalists interested in investing in one or a slate of motion picture productions or more broadly in the holdings of a media conglomerate, an exhibition chain, or some other aspect of the media landscape. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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18 pages, 565 KiB

Open AccessArticle

Sample Path Generation of the Stochastic Volatility CGMY Process and Its Application to Path-Dependent Option Pricing

by Young Shin Kim

J. Risk Financial Manag. 2021, 14(2), 77; https://doi.org/10.3390/jrfm14020077 - 15 Feb 2021

Cited by 2 | Viewed by 2461

Abstract

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S&P 100 [...] Read more.

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S&P 100 index options market, using the least square regression method. Moreover, we discuss path-dependent options, such as Asian and Barrier options. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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22 pages, 2203 KiB

Open AccessArticle

An Artificial Intelligence Approach to the Valuation of American-Style Derivatives: A Use of Particle Swarm Optimization

by Ren-Raw Chen, Jeffrey Huang, William Huang and Robert Yu

J. Risk Financial Manag. 2021, 14(2), 57; https://doi.org/10.3390/jrfm14020057 - 02 Feb 2021

Cited by 2 | Viewed by 2437

Abstract

In this paper, we evaluate American-style, path-dependent derivatives with an artificial intelligence technique. Specifically, we use swarm intelligence to find the optimal exercise boundary for an American-style derivative. Swarm intelligence is particularly efficient (regarding computation and accuracy) in solving high-dimensional optimization problems and [...] Read more.

In this paper, we evaluate American-style, path-dependent derivatives with an artificial intelligence technique. Specifically, we use swarm intelligence to find the optimal exercise boundary for an American-style derivative. Swarm intelligence is particularly efficient (regarding computation and accuracy) in solving high-dimensional optimization problems and hence, is perfectly suitable for valuing complex American-style derivatives (e.g., multiple-asset, path-dependent) which require a high-dimensional optimal exercise boundary. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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33 pages, 1884 KiB

Open AccessArticle

Option Pricing Incorporating Factor Dynamics in Complete Markets

by Yuan Hu, Abootaleb Shirvani, W. Brent Lindquist, Frank J. Fabozzi and Svetlozar T. Rachev

J. Risk Financial Manag. 2020, 13(12), 321; https://doi.org/10.3390/jrfm13120321 - 15 Dec 2020

Cited by 3 | Viewed by 3026

Abstract

Using the Donsker–Prokhorov invariance principle, we extend the Kim–Stoyanov–Rachev–Fabozzi option pricing model to allow for variably-spaced trading instances, an important consideration for short-sellers of options. Applying the Cherny–Shiryaev–Yor invariance principles, we formulate a new binomial path-dependent pricing model for discrete- and continuous-time complete [...] Read more.

Using the Donsker–Prokhorov invariance principle, we extend the Kim–Stoyanov–Rachev–Fabozzi option pricing model to allow for variably-spaced trading instances, an important consideration for short-sellers of options. Applying the Cherny–Shiryaev–Yor invariance principles, we formulate a new binomial path-dependent pricing model for discrete- and continuous-time complete markets where the stock price dynamics depends on the log-return dynamics of a market influencing factor. In the discrete case, we extend the results of this new approach to a financial market with informed traders employing a statistical arbitrage strategy involving trading of forward contracts. Our findings are illustrated with numerical examples employing US financial market data. Our work provides further support for the conclusion that any option pricing model must preserve valuable information on the instantaneous mean log-return, the probability of the stock’s upturn movement (per trading interval), and other market microstructure features. Full article

(This article belongs to the Special Issue Mathematical and Empirical Finance)

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