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In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices and provides a connection to hedging prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility. [ABSTRACT FROM AUTHOR]

We analyze the problem of constructing multiple buy-and-hold mean-variance portfolios over increasing investment horizons in continuous-time arbitrage-free stochastic interest rate markets. The orthogonal approach to the one-period mean-variance optimization of Hansen and Richard (Econometrica 55(3):587–613, 1987) requires the replication of a risky payoff for each investment horizon. When many maturities are considered, a large number of payoffs must be replicated, with an impact on transaction costs. In this paper, we orthogonally decompose the whole processes defined by asset returns to obtain a mean-variance frontier generated by the same two securities across a multiplicity of horizons. Our risk-adjusted mean-variance frontier rests on the martingale property of the returns discounted by the log-optimal portfolio and features a horizon consistency property. The outcome is that the replication of a single risky payoff is required to implement such frontier at any investment horizon. As a result, when transaction costs are taken into account, our risk-adjusted mean-variance frontier may outperform the traditional mean-variance optimal strategies in terms of Sharpe ratio. Realistic numerical examples show the improvements of our approach in medium- or long-term cashflow management, when a sequence of target returns at increasing investment horizons is considered. [ABSTRACT FROM AUTHOR]

In this paper, in order to serve credit risk management, we introduce a pricing model for a vulnerable Bull Spread options in a Mixed Modified Fractional Hull-White-Vasicek stochastic volatility and stochastic interest rate model. We use Milstein scheme to find the sample paths of asset price and its volatility, and the sample paths of interest rates of asset price movement. We use the double Mellin transform to obtain an analytical vulnerable bull spread call option formula and an analytical vulnerable bull spread put option formula under fractional stochastic volatility and fractional stochastic interest rates. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks. [ABSTRACT FROM AUTHOR]

The objective of this paper is to emphasize the differences between a call and a warrant as well as the different valuation methods of warrants which have been introduced in the financial literature. For the sake of simplicity and applicability, we only consider a debt-free equity-financed firm. More recently a formal distinction between structural and reduced form pricing models has been introduced. This distinction is important whether one wishes to price a new warrant issue or outstanding warrants. If we are interested in pricing a new issue of warrants, e.g. in the context of a management incentive package, one has to rely on a structural model. However most of practitioners use the simple Black-Scholes formula. In this context, we analyze the accuracy of the approximation of the "true" price of a warrant by the Black-Scholes formula. We show that in the current low interest rate environment, the quality of the approximation deteriorates and the sensitivity of this approximation to the volatility estimate increases. [ABSTRACT FROM AUTHOR]

This paper studies the green new product development (GNPD) problem of a risk-averse capital constrained supply chain (SC). The SC is managed by an SME entrepreneur, seeking financial support from a multi-sided FinTech platform (MSP) to develop a portfolio of green and non-green products. The MSP offers the SC a combination of equity financing (EF) and debt financing (DF) facilities and must decide on the interest rate of its DF facility. Using a benchmark model, we first characterize the SC's production and the MSP's financing decisions under a deregulated scenario. Focusing on an alternative case with government intervention (i.e., hybrid environmental-green entrepreneurship policy), we next develop a three-level game theoretical model and sequentially characterize the decision-making behavior of government, MSP, and SC. The model outcomes are analyzed by considering the policy approach (i.e., economic influence vs. social welfare) and the platform's risk attitude. The results reveal that, when coupled with an appropriate government intervention policy, a regulated scenario leads to a better outcome, particularly when the MSP is risk-neutral and strikes a right balance between the EF and DF. The win–win situation may not be realized when the MSP is risk-averse and the host government is merely focused on its economic influence. To successfully promote sustainable supply chain finance (SSCF) through digital platforms, policy makers are urged to leverage their legislative power and prioritize green entrepreneurship and social welfare over their financial maximization agenda. [ABSTRACT FROM AUTHOR]

This paper considers a capital-constrained online retailer (OR) selling products through an e-commerce platform (EP) who also offers financial services to retailers. During the selling season, the OR exerts an effort to promote market demand through activities like sales promotions, advertising and live-streaming selling events. To investigate the EP-based financing scheme, a game-theoretic model is developed where the EP functions as the leader determining the interest rate and platform usage fee rate, and the OR functions as the follower determining the order quantity and effort level. We explore the impacts of the OR's risk-aversion and find that when the OR is risk-averse (1) she sets a high effort level, and the EP sets a high usage fee rate; (2) a high risk-averse OR orders less products than low risk-averse OR. We design specific revenue-cost sharing contracts to coordinate the supply chain and demonstrate that the designed contracts are feasible. Moreover, we find that the OR consistently prefers EP financing compared to bank financing. [ABSTRACT FROM AUTHOR]

Green product development depends on the green innovation behavior of upstream and downstream companies in the supply chain. This paper focuses on a green supply chain consisting of a supplier and a manufacturer and considers two financing schemes wherein the manufacturer's operating capital and green innovation capital are both constrained: bank credit financing (BCF) and supplier green investment (SGI). We investigate the financing strategy of a manufacturer with different financing decision preferences and supply chain coordination contract designs. The results show that when the manufacturer's financing decision preference is low bankruptcy risk, it should choose BCF when the bank loan interest rate is lower than a threshold and SGI when the bank loan interest rate is higher than this threshold; when the manufacturer's financing decision preference is high R&D benefit, it should choose BCF when the initial capital is lower than another threshold and choose SGI when the initial capital is higher than this threshold. Then, we compare whether cost-sharing, quantity discount and revenue-sharing contracts can coordinate the supply chain, and discuss the selection strategies of supply chain members for coordination contracts. Research shows that revenue-sharing contracts fail to coordinate; however, when the cost-sharing ratio and quantity discount rate are appropriate, both cost-sharing and quantity discount contracts can achieve supply chain coordination. The lower the supplier's unit production cost, the stronger its motivation to accept a higher cost-sharing ratio and quantity discount rate. For the supplier, the key to choosing any coordination contract lies in the relative height of the cost-sharing ratio and quantity discount rate while the manufacturer should always choose a quantity discount contract when the quantity discount rate is high enough. [ABSTRACT FROM AUTHOR]

Bond immunization is an important topic in portfolio management. This paper presents a scenario based optimization framework for solving a cash flow matching problem. In this problem, the time horizon of the cash flow generated by the liability is longer than the maturities of the available bonds, and the interest rates are uncertain. Bond purchase decisions are made each period to generate cash flows to cover the obligations due in the future. We use buffered probability of exceedance (bPOE) and conditional value-at-risk (CVaR) to control for the risk of shortfalls. The initial cost of the hedging portfolio of bonds is minimized and optimal positions in bonds are calculated at all time periods. We also study the methodology when solving the optimization problem to minimize bPOE instead of CVaR, which has important practical relevance. The methodology we present in this paper is quite general and can be extended to other financial optimization problems. We use portfolio safeguard optimization package to solve the optimization problems. [ABSTRACT FROM AUTHOR]

This paper examines the problem of hedging banks interest rate margins. We assume that the demand's deposits follow an exponential Lévy process with potential jumps. The forward market rates are assumed to follow the standard market model introduced by Brace et al. (Math Finance 7(2):127–155, 1997). As Adam et al. (Hedging interest rate margins on demand deposits, Université Paris 1 Panthéon-Sorbonne working paper, 2012), we consider that deposit rates depend linearly on market rates. Face to incompleteness, the liability manager must hedge both interest rate and demand deposit risks. For this purpose, we introduce various quadratic hedging criteria, allowing us to provide explicit hedging strategies that we further analyze. We illustrate in particular the impact of both the trends and the volatilities of interest rates and demand deposits. [ABSTRACT FROM AUTHOR]

The paper studies the term structure of interest rates imposed by equilibrium in a production economy consisting of participants with heterogeneous preferences with information costs and short selling constraints. Our model accounts explicitly and simultaneously for information costs and short sales in the pricing of assets in the economy. Consumption is continuous. The paper contains an exact solution to market equilibrium. It provides an explicit constructive solution for determining the state-price density process. Interest rates and their behaviors are a function of economic variables with information costs and short selling constraints. [ABSTRACT FROM AUTHOR]

An open question in interest rates derivative pricing is whether the price of the contracts should be computed by means of a multi-curve approach (different yield curves for discounting and forwarding) or by using a single curve (just one yield curve both for discounting and forwarding). The answer is of primary importance for financial markets as it allows to define a class of fair contracts. This paper calculates and compares the price of a simple swap within both multi-curve and single curve approaches and proposes a generalization of the lattice approach, which is usually used to approximate short interest rate models in the multi-curve framework. As an example, I show how to use the Black et al. (Financ Anal J 46(1):33–39, 1990) interest rate model on binomial lattice in multi-curve framework and calculate the price of the 2–8 period swaption with a single (LIBOR) curve and two-curve (OIS+LIBOR) approaches. Such technique can be used for pricing any interest rate based contract. [ABSTRACT FROM AUTHOR]

Bitcoin is emerging as a distinct asset class among investors given its seemingly detached price behavior relative to market and economic fundamentals. Its incomparably high returns in recent years has further fuelled intense interest and investment into Bitcoin and cryptocurrencies at large. This paper cautions that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets. Using a Markov regime-switching model to distinguish between regimes of high and low Bitcoin price volatility, this paper shows that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns. Distinguishing between periods of high and low Bitcoin price volatility reveals heterogeneity in the explanatory power of market risk factors; in particular, Bitcoin returns are more difficult to explain during periods of high volatility relative to periods with low volatility. This finding can partially explain why extant studies, which neglect to distinguish between exchange rate regimes in Bitcoin, have difficulty linking Bitcoin prices to economic fundamentals. [ABSTRACT FROM AUTHOR]

In this paper, we examine the effect of relative/absolute firm efficiency on weighted average cost of capital (WACC). Using a sample of Korean listed firms, we find that WACC is negatively associated with relative firm efficiency (operational performance) suggesting that firms with higher (lower) relatively efficiency are expected to pay lower (higher) capital costs. When we repeat our analysis using absolute firm efficiency (ROA), we do not find a statistically significant relationship. Our results suggest relative efficiency which is estimated as output (sales) divided by the resources that are directly under the control of management is assessed by capital providers and impounded into a firm's capital costs. Absolute efficiency (ROA) which is estimated as sales divided by total assets is not. Our results suggest that simple accounting ratios used in the accounting literature are not considered as informative to explain borrowing costs compared to relative efficiency that captures managerial operational performance. [ABSTRACT FROM AUTHOR]

The development of the Internet and the emergence of social networks led people usually to give opinions and opinions about any product they have purchased. These comments and opinions may be viewed as positive or negative. So, it becomes a real challenge for any organization to manage these comments especially when they are regarded as negative news. Opinion mining is the discipline that is able to process these opinions. It is used as a decision-making tool to develop a strategy to reduce the number of negative comments or simply to compute general tendencies. In this paper, we study the case of opinions left by non-resident customers of a luxury hotel situated in a touristic country. Different tools and quantitative analysis, including codification, linear correlation as well as a multiple correspondence analysis, are implemented enabling to search for the reason of negative opinions. The main results indicated that negative ratings depend on negative perception of the service and/or the welcome and do not depend on journey type. Financial impact has been also studied. [ABSTRACT FROM AUTHOR]

This study addresses interest rate sensitivity of emerging market corporate debt. Previous research suggests that interest rate sensitivity of corporate bonds depends on residual maturity of issues, creditworthiness of issuers, embedded options and other idiosyncratic factors. However, the dependence of interest rate sensitivity on phases of the business cycle has not received an appropriate academic attention. This paper provides empirical evidence and theoretical interpretation of a dichotomy of interest rate sensitivity across the phases of the cycle, and sheds light on how credit spreads respond to interest rates. The historical span of the research covers the period of 2004–2016. The findings imply that hedging interest rate risk ought to be a dynamic process and take into consideration where the economy is positioned in the current business cycle. This research provides important insights on the nature of interest rate sensitivity, capable of enhancing financial stability and improving efficiency of financial system. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new methodology to price American put options under stochastic interest rates. We derive an analytic approximation that can be evaluated very fast and is fairly accurate. The method uses the so-called forward risk adjusted measure to derive analytic prices. We show that for American puts the correlation between the stock price and the interest rate has different influences on European option values and early exercise premiums. [ABSTRACT FROM AUTHOR]

This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing. [ABSTRACT FROM AUTHOR]

Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practical problem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to be understand whether conclusions drawn from developed countries carry over to emerging markets. We estimate model parameters of fully flexible Nelson-Siegel-Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. We investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). By including both the very dense U.S. data and the comparatively sparse emerging market data, we ensure that are results are not specific to a particular data-set. We find that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization introduced in this paper, do well. Our results are consistent across four countries, both in- and out-of-sample, and for perturbations in prices and starting values. For academics and practitioners interested in optimization methods, this study provides clear evidence of the practical importance of choice of optimization method and validates a method that works well for the NSS model. [ABSTRACT FROM AUTHOR]

This paper analyses the interaction of monetary and fiscal policies in a dynamic stochastic general equilibrium model of a monetary union where conventional monetary policy is restricted by the binding zero lower bound constraint. We use the OccBin toolkit to analyze the performance of optimally set monetary and fiscal policies and compare outcomes with simple rules for government spending or the interest rate. Government spending are set independently from each other. We make several findings. First, fiscal policy plays an active role in stabilizing the economy under the binding zero lower bound constraint. Second, properly designed fiscal policy can even shorten the duration of the binding constraint. Third, coordination of policies is not always preferable. [ABSTRACT FROM AUTHOR]

This paper proposes generalized parametric models of the short-term interest rate that nest one-factor CEV and discrete time GARCH models. The paper estimates the generalized and nested models with skewed fat-tailed distributions to determine the correct specification of the conditional distribution of interest rates. The results indicate that the discrete time models that incorporate the level and GARCH effects into the diffusion function and that accommodate the tail-thickness of the interest rate distribution perform much better than the CEV model in forecasting the future volatility of interest rates. The results also show that the significance of nonlinearity in the drift function relies crucially on the specification of the volatility function. [ABSTRACT FROM AUTHOR]

This paper aims at examining the term structure of interest rates and European-type interest rate option prices in a partially observable economy. It extends the existing literature on incomplete information by developing a one-factor model which is consistent with the initial yield curve and by providing closed-form solutions for discount bonds and different kinds of options. The model of this paper encompasses Hull and White’s (1990). Moreover, through a numerical example, these two models are compared and the impact of incomplete information on option prices is analysed. [ABSTRACT FROM AUTHOR]

With data-driven optimization, this study investigates the sellers' inventory replenishment and financial decisions, and lenders' interest rate decisions in online retailing platforms. Moreover, we focus on the annual large-scale promotion, which requires massive capital in a short period. While scholars studying the data-driven inventory replenishment problem hardly consider capital-constrained sellers, these problems are important because the seller's capital level can significantly influence the order quantity and generate different effects on inventory management. Hence, we propose two novel data-driven game-theoretic approaches (including separated and integrated methods) using machine learning and deep learning methods to optimize inventory replenishment and financial decisions for the sellers who obtain financial support from the online platform. Moreover, we propose a data-driven game-theoretic model for the online platform to optimize their interest rate considering the market potential. We explore the real retailing transaction data containing 199,390 weekly sales records. We find that the seller and lender can benefit when the seller chooses integrated machine learning and quantile regression method. However, we find that only a low capital level can motivate the seller to choose to borrow from the lender. Interestingly, our results also suggest that the lender has the motivation to build a data-driven system that helps sellers optimize inventory decisions. Our work identifies the optimal interest rate and inventory decision under the data-driven method. We propose data-driven decision support tools by evaluating the values of both the lender's and the seller's profit and provide new management insights on joint inventory and financing decisions. [ABSTRACT FROM AUTHOR]

In this paper we investigate denumerable state semi-Markov decision chains with small interest rates. We consider average and Blackwell optimality and allow for multiple closed sets and unbounded immediate rewards. Our analysis uses the existence of a Laurent series expansion for the total discounted rewards and the continuity of its terms. The assumptions are expressed in terms of a weighted supremum norm. Our method is based on an algebraic treatment of Laurent series; it constructs an appropriate linear space with a lexicographic ordering. Using two operators and a positiveness property we establish the existence of bounded solutions to optimality equations. The theory is illustrated with an example of a K-dimensional queueing system. This paper is strongly based on the work of Denardo [11] and Dekker and Hordijk [7]. [ABSTRACT FROM AUTHOR]

Typically, implied volatilities for defaultable instruments are not available in the financial market since quotations related to options on defaultable bonds or on credit default swaps are usually not quoted by brokers. However, an estimate of their volatilities is needed for pricing purposes. In this paper, we provide a methodology to infer market implied volatilities for defaultable bonds using equity implied volatilities and CDS spreads quoted by the market in relation to a specific issuer. The theoretical framework we propose is based on the Merton's model under stochastic interest rates where the short rate is assumed to follow the Hull-White model. A numerical analysis is provided to illustrate the calibration process to be performed starting from financial market data. The market implied volatility calibrated according to the proposed methodology could be used to evaluate options where the underlying is a risky bond, i.e. callable bond or other types of credit-risk sensitive financial instruments. [ABSTRACT FROM AUTHOR]

Macaulay duration is a well-known and widely used interest rate risk measure. It is commonly believed that it only works for parallel shifts of interest rates. We show in this paper that this limitation is largely due to the traditional parametric modelling and the derivative approach, the Macaulay duration works for non-parallel shifts as well when the non-parametric modelling and the equivalent zero coupon bond approach are used. We show that the Macaulay duration provides the best one-number sensitivity information for non-parallel interest rate changes and that a Macaulay duration matched portfolio is least vulnerable to the downside risk caused by non-parallel rate changes under some verifiable conditions. [ABSTRACT FROM AUTHOR]

Proper scoring rules are commonly applied to quantify the accuracy of distribution forecasts. Given an observation they assign a scalar score to each distribution forecast, with the lowest expected score attributed to the true distribution. The energy and variogram scores are two rules that have recently gained some popularity in multivariate settings because their computation does not require a forecast to have parametric density function and so they are broadly applicable. Here we conduct a simulation study to compare the discrimination ability between the energy score and three variogram scores. Compared with other studies, our simulation design is more realistic because it is supported by a historical data set containing commodity prices, currencies and interest rates, and our data generating processes include a diverse selection of models with different marginal distributions, dependence structure, and calibration windows. This facilitates a comprehensive comparison of the performance of proper scoring rules in different settings. To compare the scores we use three metrics: the mean relative score, error rate and a generalized discrimination heuristic. Overall, we find that the variogram score with parameter p = 0.5 outperforms the energy score and the other two variogram scores. [ABSTRACT FROM AUTHOR]

We consider a compound Poisson surplus process perturbed by diffusion with debit interest. When the surplus is below zero or the company is on deficit, the company is allowed to borrow money at a debit interest rate to continue its business as long as its debt is at a reasonable level. When the surplus of a company is below a certain critical level, the company is no longer profitable, we say that absolute ruin occurs at this situation. In this risk model, absolute ruin may be caused by a claim or by oscillation. Thus, the absolute ruin probability in the model is decomposed as the sum of two absolute ruin probabilities, where one is the probability that absolute ruin is caused by a claim and the other is the probability that absolute ruin is caused by oscillation. In this paper, we first give the integro-differential equations satisfied by the absolute ruin probabilities and then derive the defective renewal equations for the absolute ruin probabilities. Using these defective renewal equations, we derive the asymptotical forms of the absolute ruin probabilities when the distributions of claim sizes are heavy-tailed and light-tailed. Finally, we derive explicit expressions for the absolute ruin probabilities when claim sizes are exponentially distributed. [ABSTRACT FROM AUTHOR]

Many financial optimization problems involve future values of security prices, interest rates and exchange rates which are not known in advance, but can only be forecast or estimated. Several methodologies have therefore been proposed to handle the uncertainty in financial optimization problems. One such methodology is Robust Statistics, which addresses the problem of making estimates of the uncertain parameters that are insensitive to small variations. A different way to achieve robustness is provided by Robust Optimization, which looks for solutions that will achieve good objective function values for the realization of the uncertain parameters in given uncertainty sets. Robust Optimization thus offers a vehicle to incorporate an estimation of uncertain parameters into the decision making process. This is true, for example, in portfolio asset allocation. Starting with the robust counterparts of the classical mean-variance and minimum-variance portfolio optimization problems, in this paper we review several mathematical models, and related algorithmic approaches, that have recently been proposed to address uncertainty in portfolio asset allocation, focusing on Robust Optimization methodology. We also give an overview of some of the computational results that have been obtained with the described approaches. In addition we analyze the relationship between the concepts of robustness and convex risk measures. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of dynamic asset allocation under a bounded shortfall risk in a market composed of three assets: cash, stocks and a zero coupon bond. The dynamics of the instantaneous short rates is driven by a Hull and White model. In this setting, we determine and compare optimal investment strategies maximizing the CRRA utility of terminal wealth with and without value at risk constraint. [ABSTRACT FROM AUTHOR]

This paper investigates some common interest rate models for scenario generation in financial applications of stochastic optimization. We discuss conditions for the underlying distributions of state variables which preserve convexity of value functions in a multistage stochastic program. One- and multifactor term structure models are estimated based on historical data for the Swiss Franc. An analysis of the dynamic behavior of interest rates generated with these models reveals several deficiencies which have an impact on the performance of investment policies derived from the stochastic program. While barycentric approximation is used here for the generation of scenario trees, these insights may be generalized to other discretization techniques as well. [ABSTRACT FROM AUTHOR]

This study explores the operation decisions of a dual-channel supply chain consisting of a capital-constrained manufacturer, an e-commerce platform (ECP), and a third-party logistics company (3PL). This study first proposes two game models to obtain the equilibrium solutions of the supply chain members. Then, it compares and analyzes the equilibrium strategies under the two financing modes. This study has obtained the following interesting findings. The increase of the ECP interest rate will reduce the profit of the unit product in the distribution channel. However, the total income of the ECP will increase with the increase in the financing income. Under the 3PL financing mode, if the transportation fee of the direct sales channel is lower than that of the distribution channel, the ECP will reduce the sales price to compete with the direct sales channel of the manufacturer. Under the same conditions, the ECP financing mode is more competitive than the 3PL financing mode, and ECP financing services are the dominant strategy for the manufacturer, ECP, and consumers. If a 3PL company wants to win in the financing service, lowering interest rate or implementing differentiated transportation charges are important measures. [ABSTRACT FROM AUTHOR]

The blockchain, a decentralized ledger system, has been considered a revolutionary technology to transform businesses, including the financial markets. As peer-to-peer (P2P) lending is also known for decentralization in the sense that individuals borrow from and lend to each other directly, bypassing traditional banks, we explore the possibility and benefits of blockchains in P2P lending. We first provide an economic analysis of existing P2P lending problems, including herding and platform-borrower collusion, with and without the blockchain mechanism. We note that, compared with the existing markets, blockchain-backed P2P lending is highly effective in privacy preservation and borrower quality distinction. Since the role of intermediaries is eliminated in the blockchain world, the chance of platform-borrower collusion is also minimized. Moreover, we show that borrowers and lenders can expect additional surplus when the origination fee in traditional P2P lending is replaced with a smaller tokenization cost and when borrowers present a more accurate financial incentive in a smart contract. With the smart contract, borrowers are allowed to propose optimal interest rates based on their creditworthiness and capacity of repayment while detailed personal information is not required. Additionally, we demonstrate that small investors' surplus can be further enhanced due to the elimination of herding. [ABSTRACT FROM AUTHOR]

Traditionally, in the fashion industry, purchasing decisions for retailers are made based on various factors such as budget, profit target, and interest rate. Since the market demand is highly volatile, risk is inherently present and it is critically important to incorporate risk consideration into the decision making framework. Motivated by the observed industrial practice, we explore via a mean-variance approach the multi-period risk minimization inventory models for fashion product purchasing. We first construct a basic multi-period risk optimization model for the fashion retailer and illustrate how its optimal solution can be determined by solving a simpler problem. Then, we analytically find that the optimal ordering quantity is increasing in the expected profit target, decreasing in the number of periods of the season, and increasing in the market interest rate. After that, we propose and solve several extended models which consider realistic and timely industrial measures such as minimum ordering quantity, carbon emission tax, and carbon quota. We analytically derive the necessary and sufficient condition(s) for the existence of the optimal solution for each model and show how the purchasing budget, the profit target, and the market interest rate affect the optimal solution. Finally, we investigate the supply chain coordination challenge and analytically illustrate how an upstream manufacturer can offer implementable supply contracts to optimize the supply chain. [ABSTRACT FROM AUTHOR]

We study the multi-loan-repayment problem by formulating it as a single-machine scheduling problem with preemptive and time-dependent processing times to minimize the makespan. We transform the scheduling problem into a continuous non-linear optimization problem and obtain an approximate solution by solving a series of the corresponding linear programming problems. We also identify agreeable conditions for the problem and discuss the computational complexity of the problem. [ABSTRACT FROM AUTHOR]

In this study, we present a stochastic programming asset–liability management model which deals with decision-dependent randomness. The model focuses on a pricing problem and the subsequent asset–liability management problem describing the typical life of a consumer loan. Such problems are frequently tackled by many companies, including multinationals. When doing so, they must consider numerous factors. These factors include the possibility of their customer rejecting the loan, the possibility of the customer defaulting on the loan and the possibility of prepayment. The randomness associated with these factors have a clear relationship with the offered interest rate of the loan which is the company's decision and thus, induces decision-dependent randomness. Another important factor, which plays a major role for liabilities, is the price of money in the market. This is determined by the market interest rates. We captured their evolution in the form of a scenario tree. In summary, we formulated a non-linear, multi-stage stochastic program with decision-dependent randomness, which spanned the lifetime of a typical consumer loan. Its solution showed us the optimal decisions that the company should make. In addition, we performed a sensitivity analysis demonstrating the results of the model for various parameter settings that described different types of customers. Finally, we discuss the losses caused if companies do not act in the optimal way. [ABSTRACT FROM AUTHOR]

We consider the optimal investment problem of a fund manager in the presence of a minimum guarantee constraint on the fund performance. The manager receives a fee which is proportional to the liquidation value of the portfolio or of the surplus over the guarantee in case it is positive and zero otherwise, eventually augmented by a constant fee. Her remuneration is reduced through the application of a penalty if the value of the fund at maturity is below a specified-in-advance threshold (minimum guarantee). We deal with two different settings: a continuous time economy with constant instantaneous interest rate and the case where the interest rate evolves as the Vasicek model. Explicit formulas for the optimal investment strategy are presented. We compare our portfolio strategies to the Merton portfolio and to the Option Based Portfolio Insurance strategy. [ABSTRACT FROM AUTHOR]

We consider a two-stage stochastic bond portfolio optimization problem, where an investor aims to optimize the cost of bond portfolio under different scenarios while ensuring predefined liabilities during a given planning horizon. The investor needs to optimally decide whether to buy, hold, or sell bonds based upon present market conditions under different scenarios and varying assumptions, where the scenarios are determined based on interest rates and buying prices of the bonds. Three stochastic integer programming models are proposed and tested using real data from Saudi Sukuk (Bond) Market. The obtained results demonstrate the varying optimal decisions made to manage bond portfolio over the two stages. In addition, the three stochastic programming models for bond portfolio optimization are tested on a large set of randomly generated instances similar to the Saudi Sukuk (Bond) Market. The results of computational experiments attest the efficiency of the proposed models. [ABSTRACT FROM AUTHOR]

This research revisits the economic capital management regarding banking books of financial institutions exposed to the emerging market sovereign debt. We develop a derivative-based integrated approach to quantify economic capital requirements for considered jointly interest rate and credit risk. Our framework represents a major contribution to the empirical aspects of capital management. The proposed innovative modeling allows applying standard historic value-at-risk techniques developed for stand-alone risk factors to evaluate aggregate impacts of several risks. We use the time-series of credit default swap spreads and interest rate swap rates as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, creditworthiness, and other macroeconomic parameters, is gauged by means of numerical modeling. Our contribution to the new economic thinking regarding the interest rate risk and credit rate risk management consists in their integrated treatment as the dynamics of interest rate and credit spreads is found to demonstrate the features of automatic stabilizers of each other. This research sheds light on how financial institutions may address hedge strategies against downside risks. It is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. From the regulatory perspective, by taking into account inter-risk diversification effects it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital. [ABSTRACT FROM AUTHOR]

The manufacturer who is a supplier of trade credit may face non-payment risk from customers and a capital shortage problem simultaneously. Trade credit insurance, as one of the most important risk management tools, has been widely used in companies' daily operation. In this study, the manufacturer who allows customers to delay payment for goods already delivered purchases trade credit insurance to transfer and reduce non-payment risk and borrows money from a bank to accommodate the capital constraint problem. The Stackelberg game and loss-averse theory are used to establish a newsboy model including trade credit insurance, and the optimal insurance coverage and total sales of the manufacturer are thereby investigated. Subsequently, the interest rate decision of the bank under different risk-averse situations is also characterized. We find that the interest rate set by a loss-averse bank is equal to or greater than that given by a risk-neutral bank. The use of trade credit insurance can help the manufacturer expand sales and dramatically reduce its default risk. Both the bank and the manufacturer are better off due to the use of trade credit insurance, but contrary to what one might expect, the bank prefers giving a higher interest rate to the manufacturer when the premium rate is in a reasonable region, which indicates that the manufacturer cannot use the insurance to negotiate better financing terms. [ABSTRACT FROM AUTHOR]

A model to price default free bonds, similar to ones developed by Cox, Ingersoll and Ross, Langetieg, and Richard, is empirically examined. Calculation of model prices involves three disjoint tasks: (1) estimation of the values of the real interest rate and the inflation rate (which we will refer to as state variables or sources of uncertainty) as well as the parameters of the state stochastic differential equations, (2) estimation of the market prices of risk associated with the two state variables, and (3) the solution of the valuation partial differential equation. Task 1 is accomplished by using a Kalman Filter algorithm, task 2 uses a Fama/MacBeth approach, and task 3 utilizes an Alternating Direction Implicit finite difference technique. Model prices are compared to actual prices. The model performs better during a period of relatively stable economic conditions compared to a period associated with more volatile conditions. Pricing errors are smaller at short maturities, and increase as time to maturity increases. [ABSTRACT FROM AUTHOR]