Portfolio Pricing Research Articles

A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA

The computation of credit risk measures such as exposure and Credit Value Adjustments (CVA) requires the simulation of future portfolio prices. Recent metrics, such as dynamic Initial Margin (IM) and Margin Value Adjustments (MVA) additionally require the simulation of future conditional sensitivities. For portfolios with non-linear instruments that do not admit closed-form valuation formulas, this poses a significant computational challenge. This problem is addressed by proposing a static replication algorithm for interest rate options with early-exercise features under an affine term-structure model. Under the appropriate conditions, we can find an equivalent portfolio of vanilla options that replicate these products. Specifically, we decompose the product into a portfolio of European swaptions. The weights and strikes of the portfolio are obtained by regressing the target option value with interpretable, feed-forward neural networks. Once an equivalent portfolio of European swaptions is determined, we can leverage on closed-form expressions to obtain the conditional prices and sensitivities, which serve as an input to exposure and SIMM-driven MVA quantification. For a consistent forward sensitivity estimation, this involves the differentiation of the portfolio-weights. The accuracy and convergence of the method is demonstrated through several representative numerical examples, benchmarked against the established least-square Monte Carlo method.

Price Increases and Their Financial Consequences in International Business-to-Business Selling

Although price increases are common in business-to-consumer and business-to-business (B2B) markets, research examining the consequences of price increases on financial performance outcomes in B2B contexts is scarce. This study takes a relationship perspective on price increases and examines how a portfolio price increase affects the financial performance of the customer relationship and whether and how this effect varies between international business customers from different cultures. Based on objective data from 966 international B2B customers of a chemical goods company, this study examines a large-scale field intervention. Results show that higher portfolio price increases, although rooted in an increase of upstream costs, bring more severe harm to B2B customers’ sales revenue than lower portfolio price increases. These consequences vary with customers’ cultures as B2B customers with culture-specific communal norms are more susceptible to the magnitude of portfolio price increases than customers without such norms. International B2B companies, therefore, need to refrain from implementing uniform price increases and should consider their business customers’ cultural origin when designing and implementing price increases.

A Non-Performing Loans (NPLs) Portfolio Pricing Model Based on Recovery Performance: The Case of Greece

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the NPL portfolios considering the debtors’ behavioral response to various legal measures (phone calls, extrajudicial notices, court orders, and foreclosures). The model considered the recovery distribution over time and was applied successfully to the case of Greece. The model was also used to predict recovery, cost, and profit future cash flows, and to optimize the collection strategies related to the activation periods of different measures. A sensitivity analysis was also conducted to reveal the most significant factors affecting the collection process.

LSTM 신경망에 기반 한 주식시장 예측 연구

Purpose – This study aims to more accurately and effectively predict trends in portfolio prices by building a model using LSTM neural networks, and investigating the risk and profit prediction of investment portfolios. Design/Methodology/Approach – To obtain a return on stocks, this study used 60 monthly transaction data from major countries, including the United States and Korea, for five ETFs, BNDX, BND, VXUS, VTI, and 122630.KS, for five years from January 2016 to December of 2021. In addition, a related portfolio was constructed using modern portfolio theory. Through Min-Max normalization, five ETFs and closing data from April 20 to July 20, 2022 were normalized. The input data were classified into two characteristic dimensions, and an LSTM time series model was constructed with the number of nodes in six hidden layers. Findings – By establishing a portfolio and making regression predictions, it was possible to effectively reduce situations in which prediction accuracy was lowered due to large fluctuations in index-based stocks. Research Implications – The predicted results were tested using OLS regression analysis. The relationship between the risk of building a tangential portfolio with the same composition with different weights, the accuracy of stock price prediction by effectively reducing the low prediction accuracy of highly volatile stocks in the portfolio, and changing the set risk-free interest rate were examined.

Stock Market Stimulus

Abstract We study the stock market effects of the arrival of the three rounds of “stimulus checks” to U.S. taxpayers and the single round of direct payments to Hong Kong citizens. The first two rounds of U.S. checks appear to have increased retail buying and share prices of retail-dominated portfolios. The Hong Kong payments increased overall turnover and share prices on the Hong Kong Stock Exchange. We cannot rule out that these price effects were permanent. The findings raise novel questions about the role of fiscal stimulus in the stock market. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Assessing the Use of Gold as a Zero-Beta Asset in Empirical Asset Pricing: Application to the US Equity Market

This paper examines the use of the return on gold instead of treasury bills in empirical asset pricing models for the US equity market. It builds upon previous research on the safe-haven, hedging, and zero-beta characteristics of gold in developed markets and the close relationship between interest rates, stock, and gold returns. In particular, we extend this research by showing that using gold as a zero-beta asset helps to improve the time-series performance of asset pricing models when pricing US equities and industries between 1981 and 2015. The performance of gold zero-beta models is also compared with traditional empirical factor models using the 1-month Treasury bill rate as the risk-free rate. Our results indicate that using gold as a zero-beta asset leads to higher R-squared values, lower Sharpe ratios of alphas, and fewer significant pricing errors in the time-series analysis. Similarly, the pricing of small stock and industry portfolios is improved. In cross-section, we also find improved results, with fewer cross-sectional pricing errors and more economically meaningful pricing of risk factors. We also find that a zero-beta gold factor constructed to be orthogonal to the Carhart four factors is significant in cross-section and helps to improve factor model performance on momentum portfolios. Furthermore, the Fama–French three- and five-factor asset pricing models and the Carhart model are all improved by these means, particularly on test assets which have been poorly priced by the traditional versions. Our results have salient implications for policymakers, governments, central bank rate-setting decisions, and investors.

Can a dynamic correlation factor improve the pricing of industry portfolios?

We investigate whether a common trait shared by the major price patterns can be used to characterize the average returns of industry portfolios. We use a factor exploiting the premium induced by stocks with low and unstable correlations with the market and include it in the standard asset pricing models. The factor reduces the magnitude of alphas in these models and universally improves the description of the industry returns as measured by the GRS statistics. The finding is robust across different industry divisions and portfolio weightings.

Uniform asymptotic estimates in a time-dependent risk model with general investment returns and multivariate regularly varying claims

Consider an insurer with d lines of business and the freedom to make risk-free and risky investments. The investment portfolio price process is described as a general càdlàg process. It is assumed that the claim sizes from different lines of business and their common inter-arrival times form a sequence of independent and identically distributed (i.i.d.) random pairs, each pair obeying a particular dependence structure. With this dependence structure, claim sizes from different lines of business are distributed according to the multivariate regular variation. This paper proposes conditions that can be satisfied by several important stochastic processes, including the Lévy process, Vasicek interest rate model, Cox-Ingersoll-Ross interest rate model, Heston model, and Stochastic volatility model. Under these conditions, the uniform asymptotic expansions of ruin probabilities are derived, which hold uniformly for the entire time horizon. Numerical examples are provided as a means of illustrating the main results.

Stock Price Synchronicity and Technical Trading Effectiveness

This study investigates how stock price synchronicity, as a measure of how stock prices reflect market-wide information relative to firm-specific information, explains the profitability of moving-average (MA) technical trading. The stocks of firms with less synchronicity have more information uncertainty (IU) (i.e., they reflect less firm-specific information), which amplifies investors’ underreaction bias and the price momentum effect, and are therefore more profitable. Testing a sample of stocks listed on the Taiwan and the Taipei stock exchanges over July 1997-June 2021, we provide evidence consistent with the synchronicity-related IU hypothesis. For a low-synchronicity stock price quintile portfolio, the abnormal returns of an MA strategy relative to a buy-and-hold strategy as estimated by the Fama-French 5-factor model are high at 18.05% per annum and even higher for a high-synchronicity stock price quintile portfolio (9.22% per annum). The MA strategy for low-synchronicity stock price portfolio remains more effective even when considering equally and value-weighted portfolios, testing various sub-periods, considering alternative MA lag lengths and controlling for market variables such as liquidity, sentiment, economic policy uncertainty, and economic cycle.

Implied Ambiguity: Mean-Variance Inefficiency and Pricing Errors

We investigate the optimal portfolio choice problem for an investor who has a utility function of the smooth ambiguity model. We identify necessary and sufficient conditions for a given portfolio to be optimal for such an investor. We define the implied ambiguity of a portfolio as the smallest ambiguity aversion coefficient with which the portfolio is optimal, and the measure of ambiguity perception as the part of the variability in asset returns that can be attributed to the ambiguity. We show that there are one-to-one relations between the implied ambiguity, the Sharpe ratio, and the pricing errors when the portfolio is taken as the pricing portfolio, and that the measure of ambiguity perception is determined by the Sharpe ratio and the alpha. Based on the U.S. stock market data, we assess how ambiguity averse the representative investor is and what types of stocks the investor perceives as having more ambiguous returns than others. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

The Interdependence of Tehran Stock Exchange upon the Oil Price and USD Exchange rate return Using Quantile Regression and Time-Frequency Domain Analysis

Compared to GARCH, ARDL, VAR, and similar methods that are commonly used for stock market analysis and portfolio pricing, the quantile regression has proven to be more advantageous. In this study, we combine the quantile regression with wavelet decomposition to analyze different investment horizons in Tehran Stock Exchange. The discrete wavelet decomposition is used to divide the indices time series into short-term (2–16 days), mid-term (16–128 days), and long-term (128–512 days) horizons. The investment horizons are then accurately studied in a bear, normal, and bull market. Since Iran is an oil-exporting country and its economy is highly impacted by fluctuations in the USD exchange rate return, it is of crucial importance to analyze the effects of oil price and free-market USD exchange rate return on the stock market for investment policy-making and portfolio management. The results demonstrate how the exchange rate return volatility and the OPEC basket price fluctuation affect the stock market. The results illustrate strong evidence on the assumption of a long-term strong positive correlation between TSE and the USD exchange rate return increase.

Intraday arbitrage between ETFs and their underlying portfolios

Prior research suggests that nonfundamental exchange-traded fund (ETF) price shocks are transmitted to their portfolios through an arbitrage mechanism. We test this proposition by examining minute-by-minute returns and order imbalances but find little evidence that ETF trading impacts underlying returns. Specifically, panel vector autoregression shows that ETF returns do not lead portfolio prices. Instead, arbitrage opportunities arise from order imbalances and price movements in the underlying securities and are subsequently eliminated by ETF quote adjustments, rather than arbitrage trading. We extend our analysis to a daily frequency but still find little relation between ETF trading and constituent security prices.

Diversification in lottery-like features and portfolio pricing discount: Evidence from closed-end funds

Why do portfolios often trade at discounts relative to the sum of their components? I provide a new explanation based on the diversification in lottery-like features. I argue that portfolios trade at discounts when their components exhibit a strong lottery-like feature but a low tendency of producing extreme payoffs together. This discount can be partially mitigated if lottery-like components tend to produce extreme payoffs at the same time. I use the closed-end fund setting to provide empirical supports for this explanation. My findings support prospect theory from an alternative perspective and provide a novel explanation for the closed-end fund puzzle.

A value-at-risk approach to mis-estimation risk

Abstract Parametric mortality models permit detailed analysis of risk factors for actuarial work. However, finite data volumes lead to uncertainty over parameter estimates, which in turn gives rise to mis-estimation risk of financial liabilities. Mis-estimation risk can be assessed on a run-off basis by valuing the liabilities with alternative parameter vectors consistent with the covariance matrix. This run-off approach is especially suitable for tasks like pricing portfolio transactions, such as bulk annuities, longevity swaps or reinsurance treaties. However, a run-off approach does not fully meet the requirements of regulatory regimes that view capital requirements through the prism of a finite horizon, such as Solvency II’s one-year approach. This paper presents a methodology for viewing mis-estimation risk over a fixed time frame, and results are given for a specimen portfolio. As expected, we find that time-limited mis-estimation capital requirements increase as the horizon is lengthened or the discount rate is reduced. However, we find that much of the so-called mis-estimation risk in a one-year value-at-risk assessment can actually be driven by idiosyncratic variation, rather than parameter uncertainty. This counter-intuitive result stems from trying to view a long-term risk through a short-term window. As a result, value-at-risk mis-estimation reserves are strongly correlated with idiosyncratic risk. We also find that parsimonious models tend to produce lower mis-estimation risk than less-parsimonious ones.

Sustainable Investing in Equilibrium

We model investing that considers environmental, social, and governance (ESG) criteria. In equilibrium, green assets have low expected returns because investors enjoy holding them and because green assets hedge climate risk. Green assets nevertheless outperform when positive shocks hit the ESG factor, which captures shifts in customers' tastes for green products and investors' tastes for green holdings. The ESG factor and the market portfolio price assets in a two-factor model. The ESG investment industry is largest when investors' ESG preferences differ most. Sustainable investing produces positive social impact by making firms greener and by shifting real investment toward green firms.

Sustainable investing in equilibrium

We model investing that considers environmental, social, and governance (ESG) criteria. In equilibrium, green assets have low expected returns because investors enjoy holding them and because green assets hedge climate risk. Green assets nevertheless outperform when positive shocks hit the ESG factor, which captures shifts in customers’ tastes for green products and investors’ tastes for green holdings. The ESG factor and the market portfolio price assets in a two-factor model. The ESG investment industry is largest when investors’ ESG preferences differ most. Sustainable investing produces positive social impact by making firms greener and by shifting real investment toward green firms.

Strategic Trade when Securitized Portfolio Values Are Unknown

I examine the effect that the precision of securitization has on the market quality of the underlying asset, as well as focus on the market quality of the derivative asset. With securitization, the underlying portfolio has improved liquidity, the trading intensity of an informed trader is increased, and the informed trader’s expected profit is increased. When arbitrageurs are discretionary, jumps in illiquidity can occur. The combination of the underlying portfolio and derivative portfolio prices are revealing about the unknown liquidation value of the underlying portfolio and the derivative price is also revealing about the unknown tracking error.

The pricing of European non-performing real estate loan portfolios: evidence on stock market evaluation of complex asset sales

Recent empirical evidence raises doubt about the ability of financial market participants to generate information efficient valuations for capital market instruments whose cash flows are related to residual claims and dependent on real estate income. We contribute to this literature with the examination of value implications of non-performing loan (NPL) divestitures in the banking industry during the period 2012–2018. In a first step, we provide descriptive statistics of the European NPL market, which lacks transparency and publicly available basic information on portfolio size and components. We then analyze wealth effects of distressed loan sale announcements for a uniquely large transaction database with 317 NPL deals, which is largely driven by real estate collateral. Our results show positive stock market reactions for vendor banks following NPL divestitures that tend to be driven by real estate collateral and a size effect.

Strategic trade when securitized portfolio values are unknown

I examine the effect that the precision of securitization has on the market quality of the underlying asset, as well as focus on the market quality of the derivative asset. With securitization, the underlying portfolio has improved liquidity, the trading intensity of an informed trader is increased, and the informed trader’s expected profit is increased. When arbitrageurs are discretionary, jumps in illiquidity can occur. The combination of the underlying portfolio and derivative portfolio prices are revealing about the unknown liquidation value of the underlying portfolio and the derivative price is also revealing about the unknown tracking error.

The Pricing of European Non-Performing Real Estate Loan Portfolios: Evidence on Stock Market Evaluation of Complex Asset Sales

Recent empirical evidence raises doubt about the ability of financial market participants to generate information efficient valuations for capital market instruments whose cash flows are related to residual claims and dependent on real estate income. We contribute to this literature with the examination of value implications of non-performing loan (NPL) divestitures in the banking industry during the period 2012 to 2018. In a first step, we provide descriptive statistics of the European NPL market, which lacks transparency and publicly available basic information on portfolio size and components. We then analyze wealth effects of distressed loan sale announcements for a uniquely large transaction database with 317 NPL deals, which is largely driven by real estate collateral. Our results show positive stock market reactions for vendor banks following NPL divestitures that tend to be driven by real estate collateral and a size effect.