Single Asset Research Articles

The behaviour of property prices when affected by infrequent floods

The reduction in property values following a major flood event is significant to individuals, communities and governments alike. Property owners who purchase properties just before a major flood risk significant personal loss on their largest single asset. The costs of a major event are shared across communities through government funded recovery efforts in the short term, and changes to insurance premiums and house values in the long term. Designing protections, adaptations and insurance mechanisms that can equitably ameliorate future impacts requires a detailed understanding of how flood risk affects property prices. This paper explores patterns of discounting of property prices following infrequent flooding events. It relates property value discounting in response to floods to various theories of market behaviour. There are alternative behavioural theories which explain why agents may react in this manner. We use a behavioural framework proposed in earlier literature to construct econometric measurements that provide the opportunity to make statistical inferences on whether the expected behaviours are supported by the data. The patterns found are consistent with a market where buyers are either unaware of the risk of flood or unable to evaluate it probabilistically.

Do precious metals hedge crude oil volatility jumps?

In this study, we investigate the impacts of contemporaneous and lagged implied oil volatility (OVX) jumps on precious metals (gold, palladium, platinum, and silver) with the focus on hedging property of precious metals. Additionally, the impacts of OVX jumps on precious metals is investigated in returns and volatility. The results show that gold returns are relatively less responsive against contemporaneous and lagged OVX jumps, thus, gold acts as a weak hedge against OVX jumps. However, other metals (copper, palladium, platinum, and silver) do not serve as a hedge against contemporaneous OVX jumps. Nevertheless, these metals takeover the traditional hedging favourite ‘gold’ in the case of lagged OVX jumps and offers a strong hedge. It suggests that ignoring past information could severely undermine the investigation of OVX and the precious metal relationship. OVX jumps result in an increased volatility in precious metals, thereby indicating that all precious metals behave as a single asset class in terms of volatility transmissions. Moreover, gold is the contemporaneous metal of choice when risk perception is high or investors are averse to risk. Nevertheless, as information is diffused, other metals yield better performance as a hedge.

Analysis of Life Extension Performance Metrics for Optimal Management of Offshore Wind Assets

Abstract The main objective of this study is to develop an optimal life extension management strategy for ageing offshore wind farms. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. To this end, the key metrics to evaluate the life extension performance of an offshore wind farm are investigated. The mean value and the standard deviation of each performance metric are calculated using a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by the multi-asset portfolio optimization. In this regard, the Markowitz modern portfolio theory is applied to estimate a risk-adjusted return parameter, the Sharpe ratio of the overall portfolio of offshore wind assets. Later on, the key performance metrics are compared to identify the most suitable metrics at different stages of life extension, and a further discussion is given for different offshore wind farm sizes. Moreover, the optimal management strategy, which maximizes the Sharpe ratio of the overall offshore wind farm, is analyzed using one of the key performance metrics under optimistic, moderate, and pessimistic scenarios. Finally, the optimal allocation (portfolio) of offshore wind assets attained based on the mean-variance optimization is presented for the different stages of the life extension of the offshore wind farms accounting for the uncertainty propagation during the life extension.

Embedded draw-down constraint reward function for deep reinforcement learning

Money management, also known as asset allocation, is constantly at the forefront of research in the trading and investing fields. Since Markowitz established the current portfolio theory in 1952, it has drawn many experts to this intriguing topic. The Kelly criteria are one of the brightest stars among these new techniques. It provides an elegant solution for players and investors to get the best bidding fraction and maximize their logarithm worth over time. However, it ignores the fact that each investor has a different risk tolerance, and the proportion was calculated using the Kelly criterion without taking into account the downside risk. This paper attempts to develop a risk prediction model using a probability-based method and adjust the reward function of deep reinforcement learning to account for the downside risk. To summarize, rather than a naive reward function that solely optimizes the return, the improved deep reinforcement learning may consider an investor’s risk tolerance. Finally, we solely analyze the scenario of a single asset and employ DXY, GBP/USD, and EUR/USD as the underlying training and validation data sets. The outcome demonstrates that the adjustment to the reward mechanism produces an interesting performance. When the required MDD (Maximum draw-down) is greater than 3%, the likelihood is on average greater than 70%.

Return Predictability in Laboratory Asset Markets

Empirical studies find that the order imbalance of retail trades can predict future stock returns. The authors investigated the cause of the puzzle using data from the laboratory asset markets in which inexperienced subjects trade in a single asset market (SSW design). The authors found that the retail order imbalance in period t positively predicted returns in period t + 1 in laboratory markets. The existence of return predictability in laboratory markets in which insider information or institutional investors are absent suggests that the predictability is not contingent upon private information or the activities of institutional investors, thus diminishing support of theories relying on these 2 conditions. In addition, the authors found that the return predictability in lab results was stronger and more statistically significant when the subjects were more excited. They tested this novel lab finding in empirical data and confirmed that return predictability is more robust when the market sentiment is higher. The findings suggest that the cause of the return predictability is likely linked to speculative activities.

Diminishing Dreams

Diminishing Dreams

Time-varying dependence dynamics between international commodity prices and Australian industry stock returns: a Perspective for portfolio diversification

This paper investigates the time-varying dependence dynamics between the international commodity prices of Brent crude oil, natural gas, cocoa, and Australia's sectoral stock returns, using daily prices ranging from 1st December 2011 to 23rd April 2020. The considered sector stocks include Technology, Telecom, Real Estate, Energy, Basic Materials, Utilities, Industrials, Financials and Health Care. We employ the time-varying Markov-switching copulas to investigate the dynamic dependence between those international commodity prices and the Australian industry stocks returns, as well as using various portfolio techniques including the minimum variance portfolio, the minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. We establish that the dependence between the commodities and sectoral stock returns is Markov switching and time varying. Our results further show that Brent crude oil is a good hedge for the financial sector, natural gas is a good hedge for all sectors except the real estate, and cocoa co-crashes with the technology, industrials and real estate sectoral returns. Based on the hedging effectiveness of the competing portfolios, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset. In particular, the results indicate the significant role for commodities, with weights ranging from approximately 25% to 50%, depending on the portfolio construction of choice. Australian stocks typically have the lowest portfolio weights under all competing portfolios, thus confirming their hedging role to a commodity investment portfolio. Our analysis contributes unique evidence to the literature on the role of commodity assets as a complement to mainstream investment.

Differential taxation and security market lines–a clarification

The literature on differential taxation of asset specific payoffs presents different representations of security market lines (SMLs), with puzzling features such as parallel lines and lines with the same intercept but different slopes - a security market fan. We show that these results can be traced back to using different combinations of pre-tax and after-tax figures for betas and expected returns. Above all, for betas based on returns and a stochastic discount factor (SDF), there is a single SML in after-tax expected return and after-tax beta space. Using pre-tax values for betas and/or expected returns leads to different representations in the respective beta and expected return space. With betas that use the market return instead of the SDF, it is important to distinguish whether tax payments are redistributed back to the agents. If they are, a single SML can only be obtained through a beta using the pre-tax market return and the after-tax return of single assets. In addition to SML representations, we also discuss differential taxation with respect to mean-variance frontiers in expected return and standard deviation spaces.

Investor attention and cryptocurrency price crash risk: a quantile regression approach

PurposeMotivated by the lure of cryptocurrencies for retail investors, whose concentrated holdings are particularly exposed to price crash risk, this paper aims to study the relationship between investor attention and crash risk for a range of cryptocurrencies.Design/methodology/approachThis study adopts a quantile regression approach to determine the effect of investor attention on crash risk. Crash risk is measured using the negative coefficient of skewness and down up volatility.FindingsThis study finds that the connection is concentrated in the tails of the crash risk distribution. Investor attention has a positive relationship with crash risk when crash risk is low (below-median quantiles) and negative when crash risk is high (above-median). The findings are consistent for different measures of crash risk, for alternate internet searches and for a panel of large cryptocurrencies in addition to Bitcoin. This study also notes seasonality in crash risk, with higher crash risk during the June–August period and lower crash risk in the Halloween period that runs from November to April.Originality/valueThe results provide insights that are not apparent in previous analyses of cryptocurrency price crash risk. The results are particularly important for retail investors, who constitute a large portion of the cryptocurrency market, as they tend to hold concentrated investments and so a price crash of a single asset may have a large bearing on their wealth.

The Log-Asset Dynamic with Euler–Maruyama Scheme under Wishart Processes

This article deals with Wishart process which is defined as matrix generalization of a squared Bessel process. We consider a single risky asset pricing model whose volatility is described by Wishart affine diffusion processes. The multifactor volatility specification enables this model to be flexible enough to describe the market prices for short or long maturities. The aim of the study is to derive the log-asset returns dynamic under the double Wishart stochastic volatility model. The corrected Euler–Maruyama discretization technique is applied in order to obtain the numerical solution of the log-asset return dynamic under Bi-Wishart processes. The numerical examples show the effect of the model parameters on the asset returns under the double Wishart volatility model.

Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies

This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity as it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks, using daily data spanning from 4th January 2015 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP-VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest that clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind, emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period.

Multi-Asset Barrier Options Pricing by Collocation BEM (with Matlab® Code)

In this paper, we extend the SABO technique (Semi-Analytical method for Barrier Options), based on collocation Boundary Element Method (BEM), to the pricing of Barrier Options with payoff dependent on more than one asset. The efficiency and accuracy already revealed in the case of a single asset is confirmed by the presented numerical results.

Scalar Multivariate Risk Measures with a Single Eligible Asset

In this paper, we present results on scalar risk measures in markets with transaction costs. Such risk measures are defined as the minimal capital requirements in the cash asset. First, some results are provided on the dual representation of such risk measures, with particular emphasis given on the space of dual variables as (equivalent) martingale measures and prices consistent with the market model. Then, these dual representations are used to obtain the main results of this paper on time consistency for scalar risk measures in markets with frictions. It is well known from the superhedging risk measure in markets with transaction costs that the usual scalar concept of time consistency is too strong and not satisfied. We will show that a weaker notion of time consistency can be defined, which corresponds to the usual scalar time consistency but under any fixed consistent pricing process. We will prove the equivalence of this weaker notion of time consistency and a certain type of backward recursion with respect to the underlying risk measure with a fixed consistent pricing process. Several examples are given, with special emphasis on the superhedging risk measure.

Cross-section instability in financial markets: impatience, extrapolation, and switching

This paper presents a stylized model of interaction among boundedly rational heterogeneous agents in a multi-asset financial market to examine how agents’ impatience, extrapolation, and switching behaviors can affect cross-section market stability. Besides extrapolation and performance based switching between fundamental and extrapolative trading documented in single asset market, we show that a high degree of ‘impatience’ of agents who are ready to switch to more profitable trading strategy in the short run provides a further cross-section destabilizing mechanism. Though the ‘fundamental’ steady-state values, which reflect the standard present-value of the dividends, represent an unbiased equilibrium market outcome in the long run (to a certain extent), the price deviation from the fundamental price in one asset can spill-over to other assets, resulting in cross-section instability. Based on a (Neimark–Sacker) bifurcation analysis, we provide explicit conditions on how agents’ impatience, extrapolation, and switching can destabilize the market and result in a variety of short and long-run patterns for the cross-section asset price dynamics.

Bitcoin’s value proposition: shorting expansionary monetary policies

PurposeProfessionals and academics alike hold polarized opinions about Bitcoin’s purpose and its fundamental value. This paper aims to describe Bitcoin’s unique features that make it such an intriguing asset and proposes a new way to consider Bitcoin and its underlying value.Design/methodology/approachIn this paper the author discusses Bitcoin’s defining features that make it a unique asset. The author argues that Bitcoin should not be considered as a single purpose asset only, but rather as a new digital financial asset serving several functions, at least partially. The author discusses the role of Bitcoin in the traditional financial system, contrasts Bitcoin to gold, considers the implications of the continuance of expansionary policies on Bitcoin and discusses the impact of the emergence of cryptocurrencies as a new asset class on public policies.FindingsIn addition to functioning as a means of payment (at least partially) and a diversification tool, part of Bitcoin’s value proposition stems from its worth as a short position on modern expansionary monetary policies. Indeed, Bitcoin’s value should rise if expansionary monetary policies are maintained, amounting to a tool to short these policies, which should be considered in future attempts to value Bitcoin.Originality/valueThe author adds a new layer to the ongoing thought process by arguing of a function played by Bitcoin unaccounted for thus far by the literature. Additionally, the author describes the features and mechanisms, allowing Bitcoin to play that role.

European Option Pricing under Wishart Processes

This study deals with a single risky asset pricing model whose volatility is described by Wishart affine processes. This multifactor model with two dependency matrices describing the correlation between the asset dynamic and Wishart processes makes it more flexible enough to fit the market data for short or long maturities. The aim of the study is to derive and solve the call option pricing problem under the double Wishart stochastic volatility model. The Fourier transform techniques combined with perturbation methods are employed in order to price the European call options. The numerical illustrations on pricing predictions show similar behavior of price movements under the double Wishart model with respect to the market price.

The Myth of Diversification Reconsidered

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods in which diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: It is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article, the authors highlight a fundamental flaw in the way some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples in which both assets perform poorly, they ignore instances of successful diversification (i.e., periods in which one asset’s gains offset the other’s losses). The authors propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full-sample correlation. The authors review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes. <b>TOPICS:</b>Portfolio theory, portfolio construction, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ There is strong empirical evidence that asset class correlations are asymmetric, which poses complications in portfolio construction. ▪ Investors prefer diversification when a portfolio’s main growth engine performs poorly and unification when it performs well. ▪ To measure correlation asymmetry caused by nonnormality, investors must adjust for changes in correlation that arise mathematically when part of a sample is excluded. ▪ Unlike prior research, investors should condition correlations on the performance of a single asset, not two assets.

Time consistency for scalar multivariate risk measures

Abstract In this paper we present results on dynamic multivariate scalar risk measures, which arise in markets with transaction costs and systemic risk. Dual representations of such risk measures are presented. These are then used to obtain the main results of this paper on time consistency; namely, an equivalent recursive formulation of multivariate scalar risk measures to multiportfolio time consistency. We are motivated to study time consistency of multivariate scalar risk measures as the superhedging risk measure in markets with transaction costs (with a single eligible asset) (Jouini and Kallal (1995), Löhne and Rudloff (2014), Roux and Zastawniak (2016)) does not satisfy the usual scalar concept of time consistency. In fact, as demonstrated in (Feinstein and Rudloff (2021)), scalar risk measures with the same scalarization weight at all times would not be time consistent in general. The deduced recursive relation for the scalarizations of multiportfolio time consistent set-valued risk measures provided in this paper requires consideration of the entire family of scalarizations. In this way we develop a direct notion of a “moving scalarization” for scalar time consistency that corroborates recent research on scalarizations of dynamic multi-objective problems (Karnam, Ma and Zhang (2017), Kováčová and Rudloff (2021)).

Semi-automated Estimation of Reliability Measures from MaintenanceWork Order Records

Determining mean-time-between-failure (MTBF) estimation for in-service assets is an essential process. Statistical distributions of end-of-life values are used to assess asset reliability performance and the effectiveness of maintenance strategy. However, identifying the end-of-life event for each instance of functional failure is an arduous, manual process dependent on structured and unstructured fields in the maintenance management system and rules used by individual reliability engineers. We emulate the process of end-of-life event detection using a natural language processing pipeline followed by statistical parameter estimation to produce MTBF values for in-service assets from maintenance work order data. Using this pipeline we test how alternate mappings of words in unstructured text and the use, or not, of structured data can impact the identification of end-of-life events. We demonstrate the pipeline on data sets from two industrial users with 14,508 and 89,259 maintenance work orders, respectively. We find that resulting MTBF estimates can vary from, for example, 97 to 226 days for a single asset depending on the mappings and rules used. The main contributions of this paper are a) a demonstration of the impact of undocumented decisions made by reliability engineers in identifying end-of-life events on MTBF estimates, and b) an end to end pipeline for MTBF estimation from raw maintenance work order texts. In addition, we provide open-source code for the pipeline that can be used by asset owning organisations to semi-automate the MTBF estimation process in a manner that is fast and scalable, and ensures the rules used for end of life determination are documented and hence the process is transparent and repeatable.

ExSol

No longer just prophesied about, cyber-attacks to Energy Delivery Systems (EDS) (e.g., the power grid, gas and oil industries) are now very real dangers that result in non-trivial economical losses and inconveniences to modern societies. In such a context, risk analysis has been proposed as a valuable way to identify, analyze, and mitigate potential vulnerabilities, threats, and attack vectors. However, performing risk analysis for EDS is difficult due to their innate structural diversity and interdependencies, along with an always-increasing threatscape. Therefore, there is a need for a methodology to evaluate the current system state, identify vulnerabilities, and qualify risk at multiple granularities in a collaborative manner among different actors in the context of EDS. With this in mind, this article presents ExSol , a collaborative, real-time, risk assessment ecosystem that features an approach for modeling real-life EDS infrastructures, an ontology traversal technique that retrieves well-defined security requirements from well-reputed documents on cyber-protection for EDS infrastructures, as well as a methodology for calculating risk for a single asset and for an entire system. Moreover, we also provide experimental evidence involving a series of attack scenarios in both simulated and real-world EDS environments, which ultimately encourage the adoption of ExSol in practice.